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Volume 7
                                                                                                                                                                             June 2012
                                                                                                                                                                             The CVA Desk:
                                                                                                                                                                             Pricing the True Cost of Risk _ P.18

                                                                                                                                                                             The Optimization of Everything:
                                                                                                                                                                             Derivatives, CCR and Funding _ P.26

                                                                                                                                                                             Through the Looking Glass:
                                                                                                                                                                             Curve Fitting _ P.32

                                                                                                                                                                             The Social Media World:
                                                                                                                                                                             What Risk Can Learn From It _ P.38

                                                                                                                                                                             Stochastic and Scholastic:
                                                                                                                                                                             The Interconnectivity of Risk _ P.44




Not all risks are worth taking.
                                                                                                                               Back to the

                                                                                                                               Future
Measuring risk along individual business lines can lead to a distorted picture of exposures. At Algorithmics,
we help clients to see risk in its entirety. This unique perspective enables financial services companies to
mitigate exposures, and identify new opportunities that maximize returns. Supported by a global team of
risk professionals, our award-winning enterprise risk solutions allow clients to master the art of risk-informed
decision making through the science of knowing better.

                                                     algorithmics.com
                                                                                                                   JUNE 2012




                                                                                                                               Revisiting capital and the bank of tomorrow
Not all risks are worth taking.                                                                                    Not all risks are worth taking.
Measuring risk along individual business lines can lead to a distorted picture of exposures. At Algorithmics,      Measuring risk along individual business lines can lead to a distorted picture of exposures. At Algorithmics,
we help clients to see risk in its entirety. This unique perspective enables financial services companies to       we help clients to see risk in its entirety. This unique perspective enables financial services companies to
mitigate exposures, and identify new opportunities that maximize returns. Supported by a global team of            mitigate exposures, and identify new opportunities that maximize returns. Supported by a global team of
risk professionals, our award-winning enterprise risk solutions allow clients to master the art of risk-informed   risk professionals, our award-winning enterprise risk solutions allow clients to master the art of risk-informed
decision making through the science of knowing better.                                                             decision making through the science of knowing better.

                                                     algorithmics.com                                                                                                   algorithmics.com
Table of Contents




                                                                                                                                                                                                                                                                                                         Volume 7
                                                                                                                                                                                                                                                                                                         June 2012
                                                                                                                                                                                                                                                                 BACK TO THE FUTURE	               P12   BEST OF	                       P02
                                                                                                                                                                                                                                                                                                         Recent Awards and Recognitions
                                                                                                                                                                                                                                                                 Revisiting Capital and
                                                                                                                                                                                                                                                                 the Bank of Tomorrow                    Opening bell	p03
                                                                                                                                                                                                                                                                                                         Responses to Uncertainty
                                                                                                                                                                                                                                                                 THE CVA DESK	                     P18
                                                                                                                                                                                                                                                                                                         IN CONVERSATION	                    P04
                                                                                                                                                                                                                                                                 Pricing the Cost of Risk                IBM’s Brenda Dietrich
                                                                                                                                                                                                                                                                 at Societe Generale                     IN REVIEW	p08
                                                                                                                                                                                                                                                                                                         Earth Audit
                                                                                                                                                                                                                                                                 THE OPTIMIZATION OF EVERYTHING	   P26
                                                                                                                                                                                                                                                                 OTC Derivatives,
express written permission of Algorithmics Software LLC or any other member of the Algorithmics group of companies. The materials presented herein are for informational purposes only and do not constitute financial, investment or risk management advice. 
© 2012 Algorithmics Software LLC, an IBM Company. All rights reserved. You may not reproduce or transmit any part of this document in any form or by any means, electronic or mechanical, including photocopying and recording, for any purpose without the




                                                                                                                                                                                                                                                                                                         reading room	p10
                                                                                                                                                                                                                                                                                                         A Roundup of New and
                                                                                                                                                                                                                                                                 Counterparty Credit Risk                Noteworthy Titles
                                                                                                                                                                                                                                                                 and Funding                             the last word	p50
                                                                                                                                                                                                                                                                                                         Risk Man’s Desk
                                                                                                                                                                                                                                                                 THROUGH THE LOOKING GLASS	        P32
                                                                                                                                                                                                                                                                 An Empirical Look at
                                                                                                                                                                                                                                                                 Curve Fitting Counterparty              PUBLISHER
                                                                                                                                                                                                                                                                                                         Michael Zerbs
                                                                                                                                                                                                                                                                                                                           PRODUCTION &
                                                                                                                                                                                                                                                                                                                           DISTRIBUTION

                                                                                                                                                                                                                                                                 Credit Risk Exposures                   EDITORIAL AND
                                                                                                                                                                                                                                                                                                                           MANAGER
                                                                                                                                                                                                                                                                                                                           Elizabeth Kyriacou
                                                                                                                                                                                                                                                                                                         ART DIRECTION
                                                                                                                                                                                                                                                                                                         Touchback         Contact
                                                                                                                                                                                                                                                                 THE SOCIAL MEDIA WORLD	           P38                     Information

                                                                                                                                                                                                                                                                 (and what risk can
                                                                                                                                                                                                                                                                                                         CONTRIBUTORS      Algorithmics,
                                                                                                                                                                                                                                                                                                         Leo Armer         an IBM Company
                                                                                                                                                                                                                                                                                                         Andy Aziz         185 Spadina Avenue
                                                                                                                                                                                                                                                                 learn from it)                          David Bester
                                                                                                                                                                                                                                                                                                         Bob Boettcher
                                                                                                                                                                                                                                                                                                                           Toronto, Ontario,
                                                                                                                                                                                                                                                                                                                           Canada
                                                                                                                                                                                                                                                                                                         Tom Chernaik      M5T 2C6
                                                                                                                                                                                                                                                                 STOCHASTIC AND SCHOLASTIC	        P44   Mike Earley       416-217-1500
                                                                                                                                                                                                                                                                                                         Jon Gregory
                                                                                                                                                                                                                                                                 Assets, Liabilities and the             Francis Lacan
                                                                                                                                                                                                                                                                                                         Alan King
                                                                                                                                                                                                                                                                                                                           think@algorithmics.com
                                                                                                                                                                                                                                                                                                                           algorithmics.com/think

                                                                                                                                                                                                                                                                 Interconnectivity of Risk               Gary King
                                                                                                                                                                                                                                                                                                         John Macdonald
                                                                                                                                                                                                                                                                                                         Cesar Mora
                                                                                                                                                                                                                                                                                                         David Murphy
                                                                                                                                                                                                                                                                                                         Yaacov Mutnikas
                                                                                                                                                                                                                                                                                                         Martin Thomas
TH!NK JUNE 2012




BEST OF
Our commitment to innovation has helped
Algorithmics earn a number of public
recognitions from industry publications, reader
surveys, and judged panels year after year.
Below is a list of awards we recently received.




Best Risk Management Technology Provider, HFMWeek’s European Hedge Fund
Services 2012. Best Global Deployment for Algorithmics’ collateral
management client BNY Mellon, American Financial Technology Awards
(AFTAs) 2011. First place for Risk Management – Regulatory/economic capital
calculation, Structured Products Technology Rankings 2012. First place
overall for Enterprise-wide risk management and first place in enterprise-
wide market risk management, risk dashboards, risk aggregation, risk
capital calculation (economic) and collateral management in Risk magazine’s
Risk Technology Rankings 2011. Readers’ Choice Winner (Highly Commended)
for Best Risk Management Product or Service, Banking Technology Awards 2011.
First place in Market risk management and ALM, Asia Risk Technology
Rankings 2011. Best Risk Analytics Provider, Waters Rankings 2011.
Best Solvency II software package, Life & Pension Risk Awards 2011.
First place overall first place for Scenario Analysis, Key risk indicators,
and Operational risk loss data collection, Operational Risk & Regulation
Software Rankings 2011. Shortlisted, best post-trade risk management
product for Algo Collateral, Financial News Awards for Excellence in
Trading & Technology, Europe 2011.
2
Opening Bell




opening bell
Recent elections in France and Greece have added
a new chapter to the ongoing sovereign debt crisis
in Europe. At the time of this issue going to print,
Greek voters turned on the Conservative New
Democracy and Socialist PASOK, two parties that
have defined Greek politics for decades. New Greek
parties from the left and right are divided in outlook
but united in opposition to EU-IMF bailouts and
their widely unpopular austerity measures.
In France, François Hollande has replaced           capitalization and risk profiles was owned
former President Nicolas Sarkozy. “Europe           by decision makers. The impact of this
is watching us,” said Hollande during his           framework on their business holds inter-
victory speech. “At the moment when                 esting implications.
the result was proclaimed, I am sure that              Elsewhere in our pages are other features
in many countries of Europe there was               that explore new approaches to existing
relief and hope: finally austerity is no            challenges. These include a look at intercon-
longer destiny.” Yet following both elections,      nectivity and stochastic modeling, risk and
Chancellor Angela Merkel of Germany                 social media, and the CVA desk’s function
clearly stated that neither she nor her             of pricing the true cost of risk. In “Through
government were interested in reopening             the Looking Glass” we return to the topic
the eurozone fiscal pact, or the strategy of        of curve fitting, with an empirical look at
deficit-cutting austerity measures.                 how chief risk officers and supervisors can
   What is the appropriate response in times        gain critical insights into major exposures
of uncertainty and conflicting views on             they would otherwise be unable to obtain.
future direction? This has been an issue for          In finance and politics, there will always
financial service firms since the financial         be an element of uncertainty. As an industry
crisis. Regulators, governments and analysts        and as global citizens, we will continue to
have called for financial firms to change           identify and respond to the challenges of
the way they do business.                           our times by searching the past, and also for
   One way that firms may be able to respond        solutions that have yet to be constructed.
is by looking to how they have managed
uncertainty in the past. In “Back to the Future”,
this issue’s cover story revisits capital and
its role in the bank of tomorrow. When early
banks operated as partnerships with personal
liability attached, every decision regarding




                                                                           Michael Zerbs
                                                                           Vice President,
                                                                           IBM Risk Analytics




                                                                                                              3
4
Brenda Dietrich has spent her professional       TH!NK: You have been connected with IBM Research since the mid-1980s.
                                                 Has the company’s approach to research changed over this span?
career with IBM Research, and recently           Brenda: It has changed quite a lot. In my early days with the group, IBM
became the company’s first CTO of Analytics      Research most closely resembled a think tank. Our job was to figure out
                                                 cool things one could do with computing and computers first, and
Software. In this issue’s conversation, Brenda   then to try and establish a shared vision within the company’s product
                                                 lines. In that period we invented some wonderful things and published
discusses the nature of research, new data       papers and patents. After we were done, it fell to others to find applications
                                                 for our work. Over time, it has become more of a shared responsibility to
streams, and how the way we think about          connect our work with IBM product and service lines.
information is changing.                            In the last decade or so, we in the Research division have been much
                                                 more tightly engaged with actual end users. Part of our role is now to
                                                 understand how people approach computing, how they would like to
                                                 use computing, and doing experiments in the art of the possible with real
                                                 people. And that is a huge amount of fun.



                                                                                                                             5
TH!NK JUNE 2012



“The name of the                                                                TH!NK: What would be an example of that type of compliance?




game right now
                                                                                Brenda: Think about the GPS in your car. I don’t always follow the
                                                                                instructions mine gives me. And I really wish that she would keep
                                                                                track of what I do and learn that “Brenda prefers this street to that
                                                                                route,” for whatever reason and be responsive to that, rather than




is to find insight
                                                                                just yell at me and recalculate every time.
                                                                                TH!NK: I would too. The information GPS devices pick up represents
                                                                                new data streams, which are a big focus of the 2012 GTO. What
                                                                                streams are out there?




faster than
                                                                                Brenda: We’re most familiar with structured data, which is generally
                                                                                numeric and tends to be nicely organized. You can find each of the pieces
                                                                                of it that you want, and nothing else. You can do queries against structured
                                                                                data. You can find averages, and ranges, and apply standard deviations.




anybody else.”
                                                                                    A lot of people say structured data is data you do arithmetic on,
                                                                                but a lot of properly formatted text data is also structured. For example,
                                                                                the name field in a client record. You can’t average two names or talk
                                                                                about a range of names; that doesn’t make any sense. But you can
                                                                                match names against one another in a way to say, “these two instances
                                                                                are actually the same person versus they are different people.”
                                                                                    With geo-spatial data, we tend to be computing along varied types
                                                                                of metrics, so what you tend to do with location data is compute
        TH!NK: Why the emphasis on working with people?
                                                                                distances. People we try to count. And then we try to categorize them.
        Brenda: Ten years ago, the research lab was focused on the algorithm.       Most catalog data is now also fairly well structured.You couldn’t do
        The operating model for the math team was, “someone gives me            things like Amazon searches if their catalog data weren’t reasonably
        the mathematical representation of the business problem and I’ll        well structured. Now, it still may be imperfect, but it’s far less imperfect
        work on the algorithm to solve it.” It would return a mathematical      than five years ago.
        representation of the solution, or perhaps a code, and it was someone
                                                                                TH!NK: Catalog data takes us online, which is where most unstruc-
        else’s role to fit that into the business process.
                                                                                tured data exists. How do you define it, and what is its significance?
            For the relatively static problems we were looking at then, like
        flight schedules for airlines or production schedules for the manu-     Brenda: I don’t have a concise definition for unstructured data, but
        facturing floor, this worked reasonably well. But we now live in a      I would say that unstructured data is the data that we don’t yet know
        world where things are much more dynamic. The easy stuff has been       how we are going to be computing (as opposed to just storing, copying,
        done. As we try to push the use of mathematics to support business      and accessing) with. A lot of free form text data is unstructured.
        decision making, we are working in problem domains that are             Once you have annotated it and tagged it with the associated metadata,
        much more subtle. Fine differences between the way two different        you can say, “this sentence is about this person. It’s about where she
        enterprises in the same industry operate come into play.                lives. It’s about where she works. It’s about her relationship with
                                                                                other people.” When you have all of that in the metadata it starts to
        TH!NK: Has the direction of research remained consistent, or has it
                                                                                move towards the structured space.
        evolved in surprising directions?
                                                                                   But when it is just free form text, without the meta data, that’s
        Brenda: The GTO (Global Technology Outlook) is an annual                unstructured. Most instances of data are very unstructured, like
        process in the Research division. I have been directly involved in      voice data or tweets. Then it gets more complicated when we include
        every GTO since 1995. In the 1990s, our focus was much more about       data coming off of sensor systems. We don’t know what we are going to
        speeds and feeds: how fast would storage be – how dense would storage   do with it yet, and right now it is just drilling random measurements.
        be? How fast would access be? How many compact transistors and          We know what each field means, we don’t yet know how we are going
        circuits would fit onto a chip? I’d estimate the split at that time     to be computing on them. So that is kind of in a strange land.
        was around 50% hardware, 50% how the hardware will be used.
                                                                                TH!NK: So the next challenge is to incorporate these new data streams?
        This past year it was maybe 10% hardware, and 90% how the hardware
        will be used.                                                           Brenda: Yes. Let’s talk about a really simple example – retail forecasting.
                                                                                The easily acquired structured data comes off the point of sale device.
        TH!NK: What caused the change?
                                                                                You obtain huge amounts because every item is scanned. You can sort
        Brenda: For many applications hardware is good enough now,              through that data and count the number of a given item bought each
        whereas that wasn’t the case 15 years ago. It’s not that we are         day at each location. You can keep track of what combinations are
        struggling with the challenge of how to do the things we want to do     bought together. And you can do simple time series to forecast how
        with computers. Now we are saying, “ok, we have this enormous           much it will evolve in the future.
        wealth of data and computational power. What can we do with it?”           You can also pull in other sources of data like weather, advertising
        In other words, it is much more focused on how we can use technology,   events, or a single time event, and begin to understand how they impact
        less on how we can progress technology along its natural course.        the consumption, demand, and purchasing of these items. If you extract
           We are also now more concerned with other perspectives. I would      the effect from the underlying signal, you can propagate that signal
        call this modeling compliance: do the people do with computers          forward using your usual time series methods. You can then look at
        things they should do, and can they (both the computers and the         when these events are going to happen in the future, and put the
        people) adapt?                                                          multipliers back in. It can lead to an even better job of forecasting.




6
In Conversation




TH!NK: And data streams can be used to look at individual decision         TH!NK: What would that role be?
making as well.
                                                                           Brenda: Within a company like IBM, analytics touch almost every
Brenda: To stay with retail for a moment, the area that everyone           part of our internal operations. We use analytics in human resources.
is looking at, especially for big-ticket items, is intent to buy.          We use them in supply chain. We use them in our own financial
This is what people tweet about, what they post on social media            planning. We use them in our own risk analysis. We use them
sites, on various blogs, and mention in comment fields on sites            in facility planning. We use them in compensation planning.
like Amazon.                                                               They’re everywhere.
   These are activities that tend to be done before the buy happens.          There is a danger however, of each individual group using different
A lot of them are probably noise. And so the two things you will           tools and different data to make the same sets of decisions. And a
want to do is to detect a mention of a product, and you want to            Chief Data Officer, who may or may not be your CIO, may be charged
keep track of the mentions of that product. You can review the             with the one source of the truth. They are the keepers who know the
data by source, by what type of person, by time, and then compare          data of record for everything, as there should be.
that to the actual buys of the product that occurred at some later            But there’s more than one way to analyze data. There are different
point. You want to understand – is there a decent correlation              techniques. This is a field where it does require some understanding
here? Is expression of intent a signal to buy? How powerful of a           of the theory behind the methods. In an enterprise that is using
signal is it?                                                              analytics and multiple business functions, having someone who will
   If this process gives differentiating insight to one of the actors in   say, “this is our strategy; these are the tools we will use; this is how
an economic ecosystem that the other actors don’t have, it helps           we will share; this is how we will be more efficient.”
create an advantage. And the name of the game right now is to find            It’s about more than having the same data go into two analysis
insight faster than anybody else.                                          processes. It is about the assumptions and the methodologies that
                                                                           are used in the analysis processes. That’s why I think a Chief Analytics
TH!NK: Moving away from retail, what does the future hold for high
                                                                           Officer is going to be very important in the future in companies.
performance users?
                                                                           TH!NK: It sounds like the data we gather is changing, and the ways we
Brenda: Over time I think it will become important to gain a better
                                                                           gather that data are changing too. Does this mean we need to change
understanding of how data is used in combination. One of the first
                                                                           the way we think about data itself?
uses of mathematical algorithms to control how a computer actually
worked dates back to the big flat platter disc drives. For those drives,   Brenda: As someone who grew up believing in the scientific method,
you had to decide which track you were putting what data in.               it’s my comfort zone. Researchers and scientists generally say,
And you did the analysis up front of which data were you most likely       “I want to look at the data, I want the data to inspire hypothesis, and
to be accessing most often, so that data could go in the center track.     then I want a way to test that hypothesis.”
The least frequently accessed data was at the very center and at              We can’t necessarily create new experiments with everything
the very edge.                                                             that we come across today. What we can observe, which is vast, isn’t
   You did this because the center is the point from which, no matter      the law of physics. We almost have to think of ourselves differently.
where the head happens to be, on average it’s the shortest distance        Perhaps as researchers we should no longer think of ourselves as
to get to. So, this was a really important algorithm. It sped things up    laboratory scientists, but more like astronomers. We have great
tremendously in terms of data access.                                      tools to observe the stars, but we can’t move them around.
TH!NK: How does that relate to using data in combination?

Brenda: Most of the advanced analytics we use pull in multiple
sets of data. We might be pulling in bigger historical data sets when
we are looking at one economic measurement versus another.
Moving forward, we may want to pull in event information as well.
                                                                                            “We should no
                                                                                      longer think of
We may want to see if an event, or a publication, or a blog, or some
other signals affect our targets, and with what duration.
   The goal would be to pull multiple sources of data and try to
determine if one piece informs another piece in any way. Can we



                                                                                        ourselves as
compute “a priori” the frequency with which two pieces or classes of
data are going to be used together, and figure out some way to store
them so that we can get them ready and together, at the same time?
Because you can’t start the computations until you have both of



                                                                                          laboratory
those pieces. As long as the memory is in one place and the information
is on a magnetic drive somewhere, you bring one in first and then
wait for the other.
   If we could fetch them together because they were the same




                                                                                           scientists,
distance apart, if you will, it would be very interesting. That’s my
notion of using data together.
TH!NK: Let’s talk for a moment about titles. A Chief Risk Officer
is common. I have heard people start to speak about a Chief



                                                                                        but more like
Data Officer.
Brenda: I would want to be Chief Analytics Officer.




                                                                                       astronomers.”                                                      7
TH!NK JUNE 2012




        in review




        Earth
           Audit
        The finite supply of natural
        resources drives economies and
        influences pricing. Even though
        their costs may not be directly
        factored into all products and
        services, availability of key
        minerals can impact operational
        risk, markets, and capital. Using
        rough calculations, this energy
        audit illustrates how increases
        in living standards affect the
        rate of consumption, and brings
        an eye-opening perspective to
        the state of our planet.


                                            IF DEMAND GROWS…
                                            Some key resources will be exhausted more quickly if
                                            predicted new technologies appear and the population grows
                                            ANTIMONY	 15-20 years	 SILVER	  15-20                   years
                                            HAFNIUM	 10-years	TANTALUM	20-30                        years
                                                      ˜
                                            INDIUM	   5-10 years	  URANIUM	 30-40                   years
                                            PLATINUM	 15 years	ZINC	        20-30                   years
                                            SOURCE: ARMIN RELLER, UNIVERSITY OF AUGSBURG; TOM GRAEDEL, YALE UNIVERSITY




8
In Review




                  © 2007 Reed Business Information - UK.
All rights reserved. Distributed by Tribune Media Services




                                                        9
TH!NK JUNE 2012




READING ROOM
There’s a connection between our thought process and the courses of
action we choose. These new and noteworthy titles explore the science
of decision making, and the impact ideas can have on society as a whole.



SCIENCE
                        +




James Gleick digs deep into The Information, a journey from the language of Africa’s talking drums to the
origins of information theory. Gleick explores how our relationship to information has transformed the very
nature of human consciousness. Charles Seife examines the peculiar power of numbers in Proofiness, an
eye-opening look at the art of using pure mathematics for impure ends. In Being Wrong, Kathryn Schulz
wonders why it’s so gratifying to be right and so maddening to be mistaken – and how attitudes towards error
affect decision making and relationships. John Coates reveals the biology of financial boom and bust in
The Hour Between Dog and Wolf. Coates, a trader turned neuroscientist, shows how risk-taking transforms
our body chemistry and drives us to extremes of euphoria or depression.




Poor Economics                      The Price of Civilization   Paper Promises          Finance and the
by Abhijit Banerjee, Esther Duflo   by Jeffrey Sachs            by Philip Coggan        Good Society
PublicAffairs                       Random House                Allen Lane              by Robert Shiller
                                                                                        Princeton University Press




SCREENING ROOM
                                              +




Based on the bestselling book by Andrew Ross Sorkin, Too Big
To Fail reshapes the 2008 financial meltdown as a riveting thriller.
Centering on U.S. Treasury Secretary Henry Paulson, the film goes
behind closed doors for a captivating look at the men and women
who decided the fate of the world’s economy in a few short weeks.
                                                                                        Too Big to Fail
                                                                                        Directed by Curtis Hanson
                                                                                        HBO Films
10
Reading Room




The Information               Proofiness                    Being Wrong                     The Hour Between
by James Gleick               by Charles Seife              by Kathryn Schulz               Dog and Wolf
Vintage                       Viking Adult                  Ecco                            by John Coates
                                                                                            Random House




SOCIETY
                   +




Jeffrey Sachs has travelled the world to help diagnose and cure seemingly intractable economic problems.
In The Price of Civilization, Sachs offers a bold plan to address the inadequacies of American-style capitalism.
Abhijit Banerjee and Esther Duflo offer up a ringside view of Poor Economics, arguing that creating a world
without poverty begins with understanding the daily decisions facing the poor. Philip Coggan’s Paper Promises
examines debt, the global finance system, and how the current financial crisis has deep roots – going back to the
nature of money itself. Robert Shiller believes that finance is more than the manipulation of money or management
of risk. In Finance and the Good Society, Shiller calls for more innovation and creativity so that society can
harness the power of finance for the greater good.




                                                                                                                11
12
13
We are unlikely to return to the capi-
                                                                                                    talization levels or strict regional
                                                                                                    focus employed by the gentlemen of
                                                                                                    Pawtuxet. There are however crucial
                                                                                                    lessons to be learned when examining
                                                                                                    the structure and scope of early
                                                                                                    financial institutions. When we
                                                                                                    talk about addressing concerns over
                                                                                                    capital, funding, and liquidity, it just
                                                                                                    might be that what we need for the
                                                                                                    bank of tomorrow is not a new model,
                                                                                                    but rather one that takes inspiration
                                                                                                    from the bank of yesterday.

                                                                                                    FIRST DEPOSITS
                                                                                                    In the early history of banking, each
                                                                                                    partner made decisions knowing they
                                                                                                    shared liability if the bank failed. As a
                                                                                                    result, choices on whom credit should
                                          ver the decades there       be extended to were not taken likely. Unambitious business owners
                                         have been many views on with a slight but steady production of widgets were considered
                                        what the bank of the future ideal customers. Less attractive was the dubious repayment
                                      would be. Some ideas have potential of innovative or entrepreneurial types. The latter group
                                  been radical and some have been represented the potential of a phenomenal return on investment,
                              transitional, while others never really but only if an unproven product or process succeeded.
took hold. This particular view begins in Pawtuxet, Rhode Island,       This philosophy was in keeping with the dominant banking
on the eastern seaboard of the United States. By all accounts a       theory of the 18th & early 19th centuries. The real-bills doctrine
lovely place to visit, Pawtuxet is well known for scenic harbour proposed that banks should restrict the extension of credit to
views and boating along its historic river corridor. But in the early customers involved in the transfer of existing products only.
19th century, textile mills and coastal trade dominated its land- Real-bills supporters argued that by basing loans on the security
scape. As the community thrived and local businesses grew, the of actual goods, any individual bank’s liquidity was ensured.
Pawtuxet Bank emerged. Common for its time, the bank was a              While there is an admirable simplicity in tying loans to tangible
partnership and its directors mostly merchant-manufacturers.          goods, the skyline of the 19th century was starting to change. There
  The Pawtuxet Bank’s directors shared personal liability in the were towers, factories and infrastructure projects that needed to
event of loan default, or if the bank itself failed. In “The Struc- be built, without existing product to offer in exchange for funding.
ture of Early Banks in Southeastern New England”, Naomi R. And these projects were poised to generate great returns.
Lamoreaux recounts the events of June 1840 when the bank’s              The unlimited liability model was ill-suited to finance these
stockholders presented themselves before the Rhode Island             projects. When shareholders’ money was directly on the line, banks
General Assembly. The group appeared seeking permission to had good reason to avoid speculative projects. Because the incen-
reduce the bank’s capitalization from $87,750 to $78,000 in tives for self-discipline were so high, banks often lent only to those
order to cover losses sustained due to the death of John Pettis, they knew best. These could be local businessmen, often engaged
one of the bank’s directors:                                          in the same type of industry as the bank shareholders. Often bank
   Pettis died in 1838 with notes worth $8,800 outstanding at         funds became personal resources for the shareholders themselves,
   the bank and endorsements amounting to at least another            and this type of insider lending, or trading, would frequently make
   $1,500...(t)his loss was not sufficiently                                               up the majority of a bank’s exposures.
   large to cause the bank to fail. Nor did          The adjustment (towards                  As the world began to change, so did banks.
   depositors or the bank’s own noteholders                                                By the late 1850s, Great Britain had moved
   suffer. Most (91 percent) of the bank’s
                                                       limited liability) would            towards limited liability, with France following
   loans were backed by capital rather than           correct what had turned              suit in 1867. As with unlimited liability, the
   notes or deposits, and the stockholders           out to be the too successful logic was easy enough to follow: if a bank
   simply absorbed the loss.                         risk measure of personal              could diversify its investor base, there would
                                                    obligation: banks weren’t
                                                      interested in funding
                                                          anything risky.

14
Back to the Future




                                                            DYNAMIC CAPITAL MANAGEMENT
                                                            BY FRANCIS LACAN
                                                            To visualize the concept of dynamic capital management, think of flying
                                                            an aircraft as close as possible to the ground. If you go too high, the
be a greater availability of credit and capital. The        cost of fuel becomes unreasonable. You cannot go negative because
adjustment would correct what had turned out to be          the option doesn’t really exist. The goal is to seek out the most efficient
the too successful risk measure of personal obligation:     middle ground that best mimics the changing landscape below.
banks weren’t interested in funding anything risky.            Managing capital dynamically would enable a bank to determine,
   In “Early American Banking: The Significance of          on a day by day and month by month basis, the most efficient flight
the Corporate Form,” Richard Sylla suggests that the        path for capital and allocate it accordingly. Optimization and
tipping point away from unlimited liability originated      anticipation are the two extremes guiding such decisions, and in the
with the New York Free Banking law of 1838 which            middle reside a big set of constraints. Basel III and its liquidity coverage
stated, “no shareholder of any such association shall       ratio have restricted certain freedoms, particularly in terms of asset
be liable in his individual capacity for any contract,      qualification. The other set of constraints is risk management.
debt or engagement of such association.” New York’s         Liquidity is increasingly subject to risk management because there
free banking law didn’t just make limited liability         are lots of dependences in funding liquidity and the rest of the risk.
possible. It opened the door for the incorporation of          It seems liquidity is following a similar path to what happened
banks and the freedom from personal obligation.             with capital and solvency. Banks didn’t invest much in economic
                                                            capital, but the strong requirement to look at regulatory capital
ONE STEP FORWARD, TWO STEPS BACK                            acted as an incentive to build more analytics, more rigorous reporting,
As banks moved beyond their villages in search of capital   and to become more serious about addressing uncertainty with
and opportunities, the strategies and measurements          the right tools. What is more complex for capital management is
used in their operation changed as well. Instead of         to connect all the sources of information. Pulling cash flow across
prudence being the only driver, customer profitability      entities and supporting good cash management today still has a lot
and shareholder value became ongoing concerns.              of room to evolve. There are for example too many overlapping
   Expansions, mergers, and deregulation replaced local     systems of information that are not very good at talking to one
partnerships with a mandate to maximize customer            another. Overcoming this hurdle would be a huge step towards
bases and profitability. Operating at an extreme opposite   active capital management, rebalancing, and optimization.
of the early 19th century model were institutions              The current baseline for automation is extremely rudimentary.
like Alfinanz, an offshore administration factory that      The only true mechanical element is the planning of short term inflows
functioned as a global back office for a global network     and outflows within the Treasury, because their contractual commit-
of financial advisors, intermediaries, or brokers.          ments are relatively easy to model. The rest is seen as shocks, and
   As banks moved from private partnerships to              the focus seems to be on what the regulators are asking banks
public corporations, shareholder demands added              to address as the possibility of these shocks. As a result, banks are
another voice to how bank capital and risk would be         being pushed into modeling with greater consistency what may
managed. Enhanced returns were a factor in banks’           happen with respect to different uncertainties tied to cash flows.
decisions to pursue diversification, more complex              In the short term, banks will have to continue on the foundations
transactions such as structured products, and other         of operational management of cash and collateral, addressing
strategies that gained support from managers operating      regulatory requirements for cash flow modeling and forecasting,
with limited liability.                                     asset qualification, and scenario modeling. Together, these elements
   In the modern era of banking, even the idea of ‘who      will provide a rugged foundation to move towards automated
is a customer’ was up for grabs. In the 1990s, First        decision making, and eventually, a more automated approach to
Manhattan Consulting Group took a leading role in           at least some aspects of capital management.
introducing profit-based segments to banking. First            This prediction comes with a number of ‘ifs’: if you are able to
Manhattan came to prominence with the now famous            have very good and trustable aggregated pooling of all internal
conclusion that only 20% of a bank’s customers were         and external balances of cash in all currencies, and if you have
profitable. Their idea to focus on profitable customers     access to a very good repository for your treasury operations so
only was an attractive one to banks who were seeking to     you can see your money market for all these currencies, you could
improve low revenue growth, particularly in core retail     to an extent begin to automate capital allocations for particular
products. The concept also encouraged mergers and the       areas of the business. Decisions on how to refinance each of these
creation of larger banks, who were better positioned to     currencies, and perhaps rebalance positions into a smaller number
take advantage of segmentation opportunities.               of currencies to save costs or optimize even the risk profile of certain
   Today we see banks retreating from these drivers and     transactions, could in theory be automated.
measures, often forced to adjust strategy by regulation,       This won’t happen tomorrow. But with the proper foundation, we
and perhaps in retreat from acting “in loco parentis”.      have the technology to make dynamic capital management part
                                                            of the bank of the future.




                                                                                                                                  15
TH!NK JUNE 2012




                                     It just might be that what we need for the bank of
                                     tomorrow is not a new model, but rather one that
                                         takes inspiration from the bank of yesterday.


Shareholder demands, which focused exclusively on the creation            as easy as flipping a switch. A lack of cheap availability and
of shareholder value, must now be balanced against closer regulatory      reduced funding sources have changed the capital landscape.
scrutiny and the need to protect customer interests.                      Banks of the future must focus on the preservation and leverage
   Diversification led to its own set of challenges, as it did not help   of available capital, and make that capital work harder.
spread risk well. The credit crisis demonstrated that market risk
and credit risk can appear in unexpected ways, and that the need to
maintain strong liquidity positions was more crucial than realized.
   All the short term profitability in the world cannot help if the
system isn’t stable. And today, if you want stability, every discus-
sion must begin with the importance of access to capital.

CAPITAL: THE ONCE AND FUTURE KING
In his memoir On the Brink: Inside the Race to Stop the Collapse
of the Global Financial System, former U.S. Secretary of the
Treasury Henry Paulson reflects back on the credit crisis. One of
his conclusions is that the financial system contained too much
leverage, much of which was buried in complex structured products:
    Today it is generally understood that banks and investment
    banks in the U.S., Europe, and the rest of the world did not
    have enough capital. Less well understood is the important
    role that liquidity needs to play in bolstering the safety and
    stability of banks...(f )inancial institutions that rely heavily
    on short-term borrowings need to have plenty of cash on
    hand for bad times. And many didn’t.
Politicians and regulators have joined hands on capital, proposing
measures that would lead to banks holding more of it. Many
banks have argued against this approach, claiming that additional
capital requirements would affect performance and competition.
Yet recent investigations into the correlation between bank capital
and profitability suggest that holding additional capital may not         Part of this focus must be organizational. Allocation of capital can
be a bad thing. Which is encouraging, since banks will likely have        no longer be controlled at a business unit, subsidiary, country, or
to do it anyway.                                                          branch level. It needs to be allocated at the time of doing business
  Allen Berger and Christa Bouwman’s interests are reflected in           with specific customers, business lines, and even at a transaction
the title of their recent paper, “How Does Capital Affect Bank Per-       level. Dynamic capital leads to a radically different structure,
formance During Financial Crises?” The authors examine the                where the treasury becomes the ‘owner’ of capital, lending it to
effects of capital on bank performance, as well as how these              deal makers on demand.
effects might change during normal times as well as banking and             A dynamic treasury requires great understanding of the uses and
market crises. The empirical evidence led Berger and Bouwman              cost of capital, connected to the technological ability to ‘solve’ the
to the following conclusions:                                             problem of Big Data. In the sidebars to this article, my colleagues
    First, capital enhances the performance of all sizes of banks         have expanded on the linked topics of dynamic capital and
    during banking crises. Second, during normal times and                managing complex data.
    market crises, capital helps only small banks unambiguously
    in all performance dimensions; it helps medium and large              BANKING ON THE PAST
    banks improve only profitability during market crises and             In the early 19th century, the UK limited banking partnerships to
    only market share during normal times.                                six members. No one is suggesting banks return to this restriction.
Empirical evidence, regulatory measures, and perhaps common               But if we think about various aspects of the unlimited liability
sense dictate that holding additional capital is a worthy goal for        banking model, it appears many of their tendencies are being
banks. Yet even if banks wanted to raise capital thresholds, it isn’t     echoed in calls from regulators and stakeholders.




16
Back to the Future




                                                             Data Complexity BY Leo Armer
                                                             In the Pawtuxet model, a small number of operating partners owned
                                                             the bank’s data. It was their responsibility to collect information
                                                             about their clients, and use this knowledge to guide business decisions.
Long-dated compensation reform and shareholder ‘say             Banks today have challenges managing data, in large part
on pay’ programs can be seen as measures intended            because the acts of collecting and analyzing information have
to update the shared liability and sense of ownership        become so separated. The greater this disconnect, the more
partners used to bring to banks. The credit crisis has       important transparency becomes.
driven home the importance of liquidity, and that gaining       For both banks and clients, it’s crucial to be able to ask: “If this
capital can be expensive – if it can even be acquired in     is my risk number, where did it originate? How do I track it? How
times of a crisis. In a way this reflects the notion early   can I see which systems it passed through, and what happened
bankers held that capital was expensive, and bringing        to it along the way?” Being able to take a number from a balance
in additional funds or partners would dilute earnings.       sheet or a general ledger and drill back to its origin provides a huge
   Insider lending and specialization that gave way to       amount of confidence.
diversification and fewer restrictions on portfolios is         In order to make good decisions, you need to see the big picture.
being balanced by technologically enabled means to           If data complexity is viewed purely as a technological issue, its
better know customers. Enhanced collateral manage-           strategic importance can be overlooked. When institutions attack
ment and approaches like CVA can be used to gain             data issues purely from an IT perspective, rules are created, trans-
a deeper understanding of capital exposures before           formations take place, and the data is considered ‘clean’ after
entering into an agreement.                                  going through a reconciliation process. Various systems and
                                                             approaches are employed to ensure that the numbers coming out
                                                             of the front system match numbers coming out of the general ledger
                                                             system, and these match the numbers coming from treasury.
                                                                The problem is, as much as you can clean the data on a Monday,
                                                             unless you change the people or method of entering that data, it’s
                                                             going to need cleaning up again on Tuesday.
                                                                Today, a few firms are approaching data complexity from a business
                                                             perspective. They have put their main focus on creating a single
                                                             data warehouse where all the information is stored. This approach is
                                                             based on the insight that every piece of data has a golden source:
                                                             a reliable point of origin before it gets passed through different
                                                             hands and different teams. It becomes as much about changing
                                                             mental attitudes as it is about technical architectures.
                                                                Creating a golden source for data becomes even more crucial
                                                             when we see what has happened in the last couple of years. CVA
                                                             charges for example occur when a bank puts a variable fee on top
                                                             of a deal, depending on whom they are trading with.
                                                                If your bank was to trade with another bank, and you had a long
                                                             history with the other bank and deep insights into their credit status,
                                                             the bank would likely get a better price for that trade than a small
                                                             finance company from Greece who might be looking less solvent.
                                                                In these transactions, where is the golden source? Is it in the
                                                             middle office or front office data? Who is taking ownership over
                                                             the trades? They can’t be processed the way they used to be,
If banks are to thrive in the future, preservation and       otherwise you’re swimming against the current demand for real
leverage of available capital are crucial steps. Dynamic     time responses. If it takes six or seven days to work out what
allocation, enabled by a treasury that quickly and           happened when a counterparty defaults, you’re too far behind
effectively uses available capital in prudent ways,          the curve.
could perhaps be the defining characteristic of the bank        It is becoming more common to run into or hear discussions
of tomorrow. From the outside, these institutions            about appointing a CDO, or Chief Data Officer. A CDO, or at least
would look nothing like the stakeholders of the              the mindset within an institution that data quality is crucial and
Pawtuxet Bank, but they would be related in spirit.          strategically relevant, can help banks evolve beyond workarounds
                                                             and create a repository of golden source data. Through a framework
                                                             where standards, direction, and architecture are provided to different
                                                             departments throughout an organization, the bank of the future can
                                                             overcome data complexity.




                                                                                                                                17
18
CVA
THE

DESK
Pricing the cost of risk at Societe Generale

                             By Bob Boettcher




                                            19
TH!NK JUNE 2012




                              ,
                  “I wouldn t want
                   to overstate it –
                   it’s not bringing
                   the industry to
                   a halt. But there
                   is increasing
                   focus on limiting
                  exposures, even
                   among global
                   banks. And that is
                   starting to affect
                   the way we
                   do business.”




20
The CVA Desk




                                               THE SETUP
                                               Life used to be different – at least in terms
                                               of how counterparty credit risk was calcu-
                                               lated. In the past, an interest rate swap
                                               would have been priced the same for every
                                               client. But Lehman’s default, and more re-
                                               cently the Greek sovereign stress, has
                                               changed all that. Now, no client is assumed
                                               to be truly risk free. Different prices are
                                               now expected for different clients on that
                                               same interest rate swap, depending on
                                               variables including the client’s rating and
                                               the overall direction of existing trades be-




A
                                               tween both parties.
                    ny time one bank takes        Noting their emergence, and particularly
                    a risk against another their activity in the sovereign CDS market,
                    the probability of default the Bank of England defined CVA desks in
                    exists. To offset this their 2010 Q2 report as follows:
                    concern, and to support        A commercial bank’s CVA desk
                                                   c




                    ongoing stability within       centralises the institution’s control
                    the interbank market,          of counterparty risks by managing
 banks have long emphasized the impor-             counterparty exposures incurred
 tance of measuring and managing coun-             by other parts of the bank...CVA
 terparty risk. Yet over the past few              desks will charge a fee for managing
 months banks have becomes noticeably              these risks to the trading desk,
 less comfortable trading with each other.         which then typically tries to pass
    The recent deterioration in credit ratings     this on to the counterparty through
 that has hit many U.S. and European banks         the terms and conditions of the
 has led to a heightened sensitivity over          trading contract. But CVA desks are
 counterparty risk. These apprehensions            not typically mandated to maximise
 may not be voiced directly, but they              profits, focusing instead on risk
 become evident when front office trades           management.
 that would have cleared in the past The Bank of England’s summary captures
 no longer do because credit lines have the classic model for running a CVA desk,
 been reduced.                                 which Murphy has implemented at SG
   As head of the CVA desk at Societe CIB. The classic approach incorporates
 Generale Corporate & Investment Banking three elements:
 (SG CIB), David Murphy has a unique               1.	pricing of new trades
 vantage point on interbank relationships.         2.	transferring risk to a centralized
“I wouldn’t want to overstate it – it’s not           desk from individual desks
 bringing the industry to a halt. But there is     3.	hedging or otherwise mitigating the
 increasing focus on limiting exposures, even         aggregated risk on a global basis
 among global banks. And that is starting to On all new interest rate, FX, equity, or
 affect the way we do business.”               credit derivatives, CVA desks price the
    CVA desks have grown in popularity as marginal counterparty risk for inclusion
 banks seek more effective ways to manage into the overall price charged to the client.
 and aggregate counterparty credit risk.          CVA is a highly complex calculation – and
 From his seat at SG CIB, David has a bird’s manually calculating that for the thou-
 eye view on the challenges associated with sands of trades and potential trades that
 establishing CVA desks, and the benefits pass through a bank every day isn’t realistic.
 banks can realize by gaining an active view An effective automated system therefore
 on their portfolio of credit risk.            becomes crucial to a CVA desk’s viability.




                                                                                                    21
TH!NK JUNE 2012




     UPSIDES OF AUTOMATION                                                                  “Really, what our sales team are interested
     “For a plain vanilla trade with another bank done on an electronic trading              in, is earning as much as possible net of the
      platform, our target delivery time for the price is approximately 10 milliseconds,”    CVA. Through the automated system tools,
      says David. “On the other end of the complexity spectrum, highly-structured,           we’ve empowered sales and traders to do
      long-dated trades may require two or three days to calculate the CVA price.            trades with the lowest CVA possible. So it’s
      Within this range we deliver CVA pricing within timescales that don’t delay            worth their while spending time looking
      the overall trade completion.”                                                         for that price, and they can now do that
         While automated pricing copes well with vanilla products and the speeds             themselves quickly and efficiently – without
      required for those trades, there will always be exotic trades, trades where            the delays or extra resources required
      clients have a non-standard credit story, or a trade with special risk mitigation.     when using the manual pricing process.”
      In these cases, David has a team of four who provide this manual pricing to              These ‘pre-deal’ checks are purely
      Sales and Traders on request.                                                          indicative – and optional for Sales and
        “We try to reduce the need for manual pricing as much as possible, but the           Traders. But if they don’t do this check, they
      business will always have trades where they need someone to take a closer              face a big risk, because every time a new
      look. There may also be situations where we think the automated pricing                trade is booked, an ‘official’ CVA fee is then
      isn’t good enough, so we want to take a look anyway and we don’t allow the             auto-calculated – which is then recorded
      sales or trades to use the automated pricing provided,” he explains.                   alongside the trade – and will be deducted
         In the manual process, the CVA desk team often passes along suggestions             from the Sales/Trader performance.
      to the salesperson for improving the credit risk in a trade and enabling the
      sales person to offer the trade at a lower credit price. Examples of that would       PERILS OF PRICING
      include improving the collateral agreement with a client, or inserting a              “A key challenge of building a CVA pricing
      break clause.                                                                          system is ensuring real-time access to data
        “Via the manual process, we have educated our sales team and traders how             in three categories: trade details; static
      they can change the credit risk (and reduce the price). With this knowledge,           data (client data such as rating, details
      they now use the automated pre-deal CVA calculations to provide several                of all pre-existing trades, netting status,
      CVA prices for different versions of the same trade. This allows them to               collateral details etc), and market data.”
      achieve the best price for the client – while minimizing the counterparty risk.”         “Designing the system which has reliable
                                                                                             and timely data in all 3 categories is crucial,
                                                                                             given the impact that will have on pricing
                                                                                             and/or hedging decisions. It’s a tough
                                                                                             market with sophisticated competitors: if
                                                                                             we under-price a risk, you can be sure we
                                                                                             will start attracting a large market share.
                                                                                             And if we over-price, then we lose business
                                                                                             unnecessarily.”

     “We try to reduce the need for
      manual pricing as much as possible,
      but the business will always have
     trades where they need someone to
     take a closer look.”




22
The CVA Desk




                                                 “Through the
                                                 automated system
                                                 tools, we’ve
                                                  empowered sales
                                                 and traders to do
                                                 trades with the
                                                 lowest CVA possible.”



HALFWAY TO HEDGING                               “If you take SG CIB’s total portfolio of clients, just over
Murphy’s first priority for SG CIB has been       10% have a liquid CDS curve. In other words, for 90% of
to ensure the CVA desk correctly prices           our clients, if we wanted to go and buy CDS protection,
the risk in all new trades. Now that this         we couldn’t do it because there’s no market. To hedge
process is well-advanced, the desk will           these illiquid risks, banks would need to use some
start to focus more on hedging – or otherwise     kind of credit index.”
mitigating – its legacy portfolio of credit          But this is an imperfect hedge. In ‘normal’ times, the
risks. “For hundreds of years banks have          credit spread of the index and the ‘generic’ spread
managed reasonably well hedging 0% of             applied to calculate the client’s CVA will move in tandem.
their counterparty risk. So instead of an         The hedge is therefore effective in reducing earnings
instant seismic shift to 100% hedging of all      volatility from day-to-day changes in the CVA Reserve.
risks, we will be hedging selected segments       But, if the client deteriorates – or even defaults – due to
of the credit and market risk – which             an idiosyncratic reason, then the index hedge may not
avoids paying away all CVA income to the          be affected at all (i.e. the hedge doesn’t work). So the
market,” says Murphy.                             decision whether to hedge illiquid names depends on
   For banks evolving the CVA function,           what you want to protect against: actual losses following
there are two main reasons hedging is not         default… or earnings volatility caused by changes in
further along. The first is technology: they      market credit spreads.
may not yet be fully confident in their risk
measurement system, which requires a
complex and time-intensive development
period. The second reason is strategic: the
bank might not think that all of its potential
hedges are very useful.




                                                                                                                         23
TH!NK JUNE 2012




In the traditional CVA       ACCOUNTING FOR BASEL
                             In the traditional CVA approach, a bank accepts a new trade, takes
                             a fee and uses that fee to buy good hedges for all the risks in that
approach, a bank accepts     trade. These hedges should eliminate all of the bank’s risk, but
                             this is not necessarily the case once Basel III is taken into account.
a new trade, takes a fee        Basel III does not recognize all types of hedges that the bank
                             might want to use. Therefore the regulatory capital for certain
and uses that fee to buy     trades will not be zero, even if the bank has used the full CVA fee
                             to hedge all its risks.
                                The first impact Basel III has on CVA desks is on pricing.
good hedges for all the      Pre-deal pricing needs to be reviewed to ensure the costs of
                             imposed regulatory capital are covered. If not, additional pricing
risks in that trade. These   may need to be added. And the decision on which risks are
                             efficient to hedge also becomes affected not just by strategic or
hedges should eliminate      business reasons, but also by the regulatory capital impact.
                                As part of Basel III’s updated regulatory capital guidelines,
                             a new element has been added: VaR on CVA. Regulators have
all of the bank’s risk…      specified very precisely how the underlying CVA must be calcu-
                             lated for this charge. Banks will therefore need to decide whether
                             to adjust their pricing and balance sheet CVA to match the
                             BIII rules, or to use different CVA calculations for pricing and
                             Regulatory purposes.




24
The CVA Desk




A DEFINING ROLE
When individual trading desks own risk, one desk may have had a
positive exposure to a client. This could lead the desk to hedge the
positive exposure, without knowing that there was a negative
exposure at another desk, which means the hedge wasn’t really
necessary. Because the CVA desk owns all the risk from all the
different derivatives desks, it has a full view of the risks with each
counterparty, across all desks, products and locations and can
price and hedge the risk appropriately.
   It has been suggested that a CVA desk is just a ‘smart middle
office’. Murphy doesn’t agree: “The main difference between CVA
other front office trading functions is that most of the risks are
originated internally from the bank’s other trading desks. But the
CVA desk must price, originate, and distribute those risks in
exactly the same way as any other front office trading desk.”                 …but this is not
   CVA desks have evolved to price, centralize, and manage a
bank’s counterparty risks, requiring sophisticated modeling of
hybrid risks encompassing every asset class that the bank is
                                                                          necessarily the case
involved in. When implemented correctly, CVA desks should
support an institution’s business and strategic vision, while helping    once Basel III is taken
banks maintain normalized relationships and control risk in an
ever more complex trading universe.                                            into account.




                                                                                                   25
26
The
OPTIMIZATION
of EVERYTHING

   OTC Derivatives, Counterparty
        Credit Risk and Funding
                                        By Jon Gregory



          The global financial crisis has created much excitement
          over counterparty credit risk (CCR) and, in recognition
          of this, banks have been improving their practices
          around CCR. In particular, the use of CVA (credit value
          adjustment) to facilitate pricing and management of
          CCR has increased significantly. Indeed, many banks
          have CVA desks that are responsible for pricing and
          managing CVA across trading functions. In addition
          to CVA, DVA (debt value adjustment) is often used
          as recognition of the “benefit” arising from one’s own
          default and funding aspects may be considered via
          funding value adjustment (FVA). Also, the impact that
          collateral has on CVA, DVA and FVA is important to
          quantify. Finally, there is a need to consider the impact
          of the funding requirements and systemic risk when
          trading with central counterparties (CCPs).



                                                                 27
TH!NK JUNE 2012




T
           he dynamics of trading OTC derivatives is becoming
           increasingly driven by the components mentioned
           above. Such a trend can only grow as regulation arising
           from Basel III creates the need for significantly
           increased amounts of capital to be held against CCR.
           It therefore seems likely that banks will not only invest
           significantly in building knowledge around the afore-
mentioned concepts but will also optimize their trading decisions.
For example, should one trade through a CCP or not? Is it pref-
erable to trade with a counterparty via a 2-way collateral agree-
ment (CSA)? Should we collateralize via cash or other securities?
What currency should I post cash collateral in?
  There are a number of considerations around optimizing OTC
derivatives trading with respect to CCR, funding, and systemic risk.     Collateral. A “margin
From the point of view of a bank, an OTC derivative transaction          period of risk” of 20 days
depends very much on the type of counterparty to the trade.              must be applied for trans-
Most unsophisticated users of OTC derivatives will not post collat-      actions where netting sets are
eral against positions while more sophisticated users will post          large (i.e. over 5,000 trades), have
collateral or trade through a central counterparty. This creates a       illiquid collateral, or represent hard-to-
wide spectrum of behaviour with respect to CCR and funding               replace derivatives. The current time frame
aspects that we will discuss. A bank then has the issue of deter-        on such transaction is 5-10 days. No benefit can
mining how best to optimize their trading across this spectrum.          be achieved from downgrade triggers (e.g. receiving more
                                                                         collateral if the rating of a counterparty deteriorates).
The impact of regulation                                                 In addition, additional haircuts for certain securities
The Basel III rules will be phased in from the beginning of 2013         and the liquidity coverage ratio will limit the amount of
and will force banks to hold a lot more equity capital, much of          rehypothecation (reuse of collateral) and encourage the
which is due to CCR requirements. Ballpark estimates are that            use of cash collateral. This ratio aims to ensure that a bank
most large banks will have to more than triple the amount of             maintains an adequate level of unencumbered, high-
equity held compared with pre-crisis. Loopholes to reduce capital        quality liquid assets that can be converted into cash to
requirements, such as off balance-sheet entities, are being              meet its liquidity needs.
closed. A trillion dollars or so of extra equity will need to be         CVA VAR. Banks must hold additional capital to capture the
raised by American banks by the end of the implementation of             volatility of CVA. This is in addition to the current rules that
Basel III (2019) with European banks needing to raise a similar          capitalise default risk.
figure. Basel III will have a profound effect on banking behaviour.      Central counterparties. A risk weighting of 2% will be
The changes will make all banking activities more expensive, in          given to exposures to a CCP, not only via margin posted but
particular exposures held in the trading book.                           also via the default fund contribution that must be made. In
   Under Basel III, the changes around CCR (that will apply to banks     addition, the CCP must meet various rigorous conditions,
from 1 January 2013) are particularly significant and include:           including the establishment of a high specific level of initial
   Stressed EPE. Banks which have permission to use the                  margin and ongoing collateral posting requirements, and
   internal models method (IMM) must calculate exposures                 that it has sufficient financial resources to withstand the
   using data that includes a period of stressed market conditions,      default of significant participants. While this represents an
   if this is higher than the standard calculation.                      increase (from zero) in capitalization of CCP exposures, it
   Wrong way risk. Banks must identify exposures that give               is intended to incentivise the clearing of OTC derivatives
   rise to a greater degree of “general” wrong-way risk and must         through CCPs.
   assume a higher exposure for transactions with “specific”
   wrong-way risk.                                                     Collateral and CCPs
   Systemic risk. Banks must apply a correlation multiplier of         Collateral arrangements involve parties posting cash or securities
   1.25 to all exposures to regulated financial firms with assets      to mitigate counterparty risk, usually governed under the terms
   of at least $100 billion and to all exposures to unregulated        of an ISDA Credit Support Annex (CSA). The typical frequency of
   financial firms.                                                    posting is daily and the holder of collateral pays an (typically




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Algo think0612-june12

  • 1. Volume 7 June 2012 The CVA Desk: Pricing the True Cost of Risk _ P.18 The Optimization of Everything: Derivatives, CCR and Funding _ P.26 Through the Looking Glass: Curve Fitting _ P.32 The Social Media World: What Risk Can Learn From It _ P.38 Stochastic and Scholastic: The Interconnectivity of Risk _ P.44 Not all risks are worth taking. Back to the Future Measuring risk along individual business lines can lead to a distorted picture of exposures. At Algorithmics, we help clients to see risk in its entirety. This unique perspective enables financial services companies to mitigate exposures, and identify new opportunities that maximize returns. Supported by a global team of risk professionals, our award-winning enterprise risk solutions allow clients to master the art of risk-informed decision making through the science of knowing better. algorithmics.com JUNE 2012 Revisiting capital and the bank of tomorrow
  • 2. Not all risks are worth taking. Not all risks are worth taking. Measuring risk along individual business lines can lead to a distorted picture of exposures. At Algorithmics, Measuring risk along individual business lines can lead to a distorted picture of exposures. At Algorithmics, we help clients to see risk in its entirety. This unique perspective enables financial services companies to we help clients to see risk in its entirety. This unique perspective enables financial services companies to mitigate exposures, and identify new opportunities that maximize returns. Supported by a global team of mitigate exposures, and identify new opportunities that maximize returns. Supported by a global team of risk professionals, our award-winning enterprise risk solutions allow clients to master the art of risk-informed risk professionals, our award-winning enterprise risk solutions allow clients to master the art of risk-informed decision making through the science of knowing better. decision making through the science of knowing better. algorithmics.com algorithmics.com
  • 3. Table of Contents Volume 7 June 2012 BACK TO THE FUTURE P12 BEST OF P02 Recent Awards and Recognitions Revisiting Capital and the Bank of Tomorrow Opening bell p03 Responses to Uncertainty THE CVA DESK P18 IN CONVERSATION P04 Pricing the Cost of Risk IBM’s Brenda Dietrich at Societe Generale IN REVIEW p08 Earth Audit THE OPTIMIZATION OF EVERYTHING P26 OTC Derivatives, express written permission of Algorithmics Software LLC or any other member of the Algorithmics group of companies. The materials presented herein are for informational purposes only and do not constitute financial, investment or risk management advice.  © 2012 Algorithmics Software LLC, an IBM Company. All rights reserved. You may not reproduce or transmit any part of this document in any form or by any means, electronic or mechanical, including photocopying and recording, for any purpose without the reading room p10 A Roundup of New and Counterparty Credit Risk Noteworthy Titles and Funding the last word p50 Risk Man’s Desk THROUGH THE LOOKING GLASS P32 An Empirical Look at Curve Fitting Counterparty PUBLISHER Michael Zerbs PRODUCTION & DISTRIBUTION Credit Risk Exposures EDITORIAL AND MANAGER Elizabeth Kyriacou ART DIRECTION Touchback Contact THE SOCIAL MEDIA WORLD P38 Information (and what risk can CONTRIBUTORS Algorithmics, Leo Armer an IBM Company Andy Aziz 185 Spadina Avenue learn from it) David Bester Bob Boettcher Toronto, Ontario, Canada Tom Chernaik M5T 2C6 STOCHASTIC AND SCHOLASTIC P44 Mike Earley 416-217-1500 Jon Gregory Assets, Liabilities and the Francis Lacan Alan King think@algorithmics.com algorithmics.com/think Interconnectivity of Risk Gary King John Macdonald Cesar Mora David Murphy Yaacov Mutnikas Martin Thomas
  • 4. TH!NK JUNE 2012 BEST OF Our commitment to innovation has helped Algorithmics earn a number of public recognitions from industry publications, reader surveys, and judged panels year after year. Below is a list of awards we recently received. Best Risk Management Technology Provider, HFMWeek’s European Hedge Fund Services 2012. Best Global Deployment for Algorithmics’ collateral management client BNY Mellon, American Financial Technology Awards (AFTAs) 2011. First place for Risk Management – Regulatory/economic capital calculation, Structured Products Technology Rankings 2012. First place overall for Enterprise-wide risk management and first place in enterprise- wide market risk management, risk dashboards, risk aggregation, risk capital calculation (economic) and collateral management in Risk magazine’s Risk Technology Rankings 2011. Readers’ Choice Winner (Highly Commended) for Best Risk Management Product or Service, Banking Technology Awards 2011. First place in Market risk management and ALM, Asia Risk Technology Rankings 2011. Best Risk Analytics Provider, Waters Rankings 2011. Best Solvency II software package, Life & Pension Risk Awards 2011. First place overall first place for Scenario Analysis, Key risk indicators, and Operational risk loss data collection, Operational Risk & Regulation Software Rankings 2011. Shortlisted, best post-trade risk management product for Algo Collateral, Financial News Awards for Excellence in Trading & Technology, Europe 2011. 2
  • 5. Opening Bell opening bell Recent elections in France and Greece have added a new chapter to the ongoing sovereign debt crisis in Europe. At the time of this issue going to print, Greek voters turned on the Conservative New Democracy and Socialist PASOK, two parties that have defined Greek politics for decades. New Greek parties from the left and right are divided in outlook but united in opposition to EU-IMF bailouts and their widely unpopular austerity measures. In France, François Hollande has replaced capitalization and risk profiles was owned former President Nicolas Sarkozy. “Europe by decision makers. The impact of this is watching us,” said Hollande during his framework on their business holds inter- victory speech. “At the moment when esting implications. the result was proclaimed, I am sure that Elsewhere in our pages are other features in many countries of Europe there was that explore new approaches to existing relief and hope: finally austerity is no challenges. These include a look at intercon- longer destiny.” Yet following both elections, nectivity and stochastic modeling, risk and Chancellor Angela Merkel of Germany social media, and the CVA desk’s function clearly stated that neither she nor her of pricing the true cost of risk. In “Through government were interested in reopening the Looking Glass” we return to the topic the eurozone fiscal pact, or the strategy of of curve fitting, with an empirical look at deficit-cutting austerity measures. how chief risk officers and supervisors can What is the appropriate response in times gain critical insights into major exposures of uncertainty and conflicting views on they would otherwise be unable to obtain. future direction? This has been an issue for In finance and politics, there will always financial service firms since the financial be an element of uncertainty. As an industry crisis. Regulators, governments and analysts and as global citizens, we will continue to have called for financial firms to change identify and respond to the challenges of the way they do business. our times by searching the past, and also for One way that firms may be able to respond solutions that have yet to be constructed. is by looking to how they have managed uncertainty in the past. In “Back to the Future”, this issue’s cover story revisits capital and its role in the bank of tomorrow. When early banks operated as partnerships with personal liability attached, every decision regarding Michael Zerbs Vice President, IBM Risk Analytics 3
  • 6. 4
  • 7. Brenda Dietrich has spent her professional TH!NK: You have been connected with IBM Research since the mid-1980s. Has the company’s approach to research changed over this span? career with IBM Research, and recently Brenda: It has changed quite a lot. In my early days with the group, IBM became the company’s first CTO of Analytics Research most closely resembled a think tank. Our job was to figure out cool things one could do with computing and computers first, and Software. In this issue’s conversation, Brenda then to try and establish a shared vision within the company’s product lines. In that period we invented some wonderful things and published discusses the nature of research, new data papers and patents. After we were done, it fell to others to find applications for our work. Over time, it has become more of a shared responsibility to streams, and how the way we think about connect our work with IBM product and service lines. information is changing. In the last decade or so, we in the Research division have been much more tightly engaged with actual end users. Part of our role is now to understand how people approach computing, how they would like to use computing, and doing experiments in the art of the possible with real people. And that is a huge amount of fun. 5
  • 8. TH!NK JUNE 2012 “The name of the TH!NK: What would be an example of that type of compliance? game right now Brenda: Think about the GPS in your car. I don’t always follow the instructions mine gives me. And I really wish that she would keep track of what I do and learn that “Brenda prefers this street to that route,” for whatever reason and be responsive to that, rather than is to find insight just yell at me and recalculate every time. TH!NK: I would too. The information GPS devices pick up represents new data streams, which are a big focus of the 2012 GTO. What streams are out there? faster than Brenda: We’re most familiar with structured data, which is generally numeric and tends to be nicely organized. You can find each of the pieces of it that you want, and nothing else. You can do queries against structured data. You can find averages, and ranges, and apply standard deviations. anybody else.” A lot of people say structured data is data you do arithmetic on, but a lot of properly formatted text data is also structured. For example, the name field in a client record. You can’t average two names or talk about a range of names; that doesn’t make any sense. But you can match names against one another in a way to say, “these two instances are actually the same person versus they are different people.” With geo-spatial data, we tend to be computing along varied types of metrics, so what you tend to do with location data is compute TH!NK: Why the emphasis on working with people? distances. People we try to count. And then we try to categorize them. Brenda: Ten years ago, the research lab was focused on the algorithm. Most catalog data is now also fairly well structured.You couldn’t do The operating model for the math team was, “someone gives me things like Amazon searches if their catalog data weren’t reasonably the mathematical representation of the business problem and I’ll well structured. Now, it still may be imperfect, but it’s far less imperfect work on the algorithm to solve it.” It would return a mathematical than five years ago. representation of the solution, or perhaps a code, and it was someone TH!NK: Catalog data takes us online, which is where most unstruc- else’s role to fit that into the business process. tured data exists. How do you define it, and what is its significance? For the relatively static problems we were looking at then, like flight schedules for airlines or production schedules for the manu- Brenda: I don’t have a concise definition for unstructured data, but facturing floor, this worked reasonably well. But we now live in a I would say that unstructured data is the data that we don’t yet know world where things are much more dynamic. The easy stuff has been how we are going to be computing (as opposed to just storing, copying, done. As we try to push the use of mathematics to support business and accessing) with. A lot of free form text data is unstructured. decision making, we are working in problem domains that are Once you have annotated it and tagged it with the associated metadata, much more subtle. Fine differences between the way two different you can say, “this sentence is about this person. It’s about where she enterprises in the same industry operate come into play. lives. It’s about where she works. It’s about her relationship with other people.” When you have all of that in the metadata it starts to TH!NK: Has the direction of research remained consistent, or has it move towards the structured space. evolved in surprising directions? But when it is just free form text, without the meta data, that’s Brenda: The GTO (Global Technology Outlook) is an annual unstructured. Most instances of data are very unstructured, like process in the Research division. I have been directly involved in voice data or tweets. Then it gets more complicated when we include every GTO since 1995. In the 1990s, our focus was much more about data coming off of sensor systems. We don’t know what we are going to speeds and feeds: how fast would storage be – how dense would storage do with it yet, and right now it is just drilling random measurements. be? How fast would access be? How many compact transistors and We know what each field means, we don’t yet know how we are going circuits would fit onto a chip? I’d estimate the split at that time to be computing on them. So that is kind of in a strange land. was around 50% hardware, 50% how the hardware will be used. TH!NK: So the next challenge is to incorporate these new data streams? This past year it was maybe 10% hardware, and 90% how the hardware will be used. Brenda: Yes. Let’s talk about a really simple example – retail forecasting. The easily acquired structured data comes off the point of sale device. TH!NK: What caused the change? You obtain huge amounts because every item is scanned. You can sort Brenda: For many applications hardware is good enough now, through that data and count the number of a given item bought each whereas that wasn’t the case 15 years ago. It’s not that we are day at each location. You can keep track of what combinations are struggling with the challenge of how to do the things we want to do bought together. And you can do simple time series to forecast how with computers. Now we are saying, “ok, we have this enormous much it will evolve in the future. wealth of data and computational power. What can we do with it?” You can also pull in other sources of data like weather, advertising In other words, it is much more focused on how we can use technology, events, or a single time event, and begin to understand how they impact less on how we can progress technology along its natural course. the consumption, demand, and purchasing of these items. If you extract We are also now more concerned with other perspectives. I would the effect from the underlying signal, you can propagate that signal call this modeling compliance: do the people do with computers forward using your usual time series methods. You can then look at things they should do, and can they (both the computers and the when these events are going to happen in the future, and put the people) adapt? multipliers back in. It can lead to an even better job of forecasting. 6
  • 9. In Conversation TH!NK: And data streams can be used to look at individual decision TH!NK: What would that role be? making as well. Brenda: Within a company like IBM, analytics touch almost every Brenda: To stay with retail for a moment, the area that everyone part of our internal operations. We use analytics in human resources. is looking at, especially for big-ticket items, is intent to buy. We use them in supply chain. We use them in our own financial This is what people tweet about, what they post on social media planning. We use them in our own risk analysis. We use them sites, on various blogs, and mention in comment fields on sites in facility planning. We use them in compensation planning. like Amazon. They’re everywhere. These are activities that tend to be done before the buy happens. There is a danger however, of each individual group using different A lot of them are probably noise. And so the two things you will tools and different data to make the same sets of decisions. And a want to do is to detect a mention of a product, and you want to Chief Data Officer, who may or may not be your CIO, may be charged keep track of the mentions of that product. You can review the with the one source of the truth. They are the keepers who know the data by source, by what type of person, by time, and then compare data of record for everything, as there should be. that to the actual buys of the product that occurred at some later But there’s more than one way to analyze data. There are different point. You want to understand – is there a decent correlation techniques. This is a field where it does require some understanding here? Is expression of intent a signal to buy? How powerful of a of the theory behind the methods. In an enterprise that is using signal is it? analytics and multiple business functions, having someone who will If this process gives differentiating insight to one of the actors in say, “this is our strategy; these are the tools we will use; this is how an economic ecosystem that the other actors don’t have, it helps we will share; this is how we will be more efficient.” create an advantage. And the name of the game right now is to find It’s about more than having the same data go into two analysis insight faster than anybody else. processes. It is about the assumptions and the methodologies that are used in the analysis processes. That’s why I think a Chief Analytics TH!NK: Moving away from retail, what does the future hold for high Officer is going to be very important in the future in companies. performance users? TH!NK: It sounds like the data we gather is changing, and the ways we Brenda: Over time I think it will become important to gain a better gather that data are changing too. Does this mean we need to change understanding of how data is used in combination. One of the first the way we think about data itself? uses of mathematical algorithms to control how a computer actually worked dates back to the big flat platter disc drives. For those drives, Brenda: As someone who grew up believing in the scientific method, you had to decide which track you were putting what data in. it’s my comfort zone. Researchers and scientists generally say, And you did the analysis up front of which data were you most likely “I want to look at the data, I want the data to inspire hypothesis, and to be accessing most often, so that data could go in the center track. then I want a way to test that hypothesis.” The least frequently accessed data was at the very center and at We can’t necessarily create new experiments with everything the very edge. that we come across today. What we can observe, which is vast, isn’t You did this because the center is the point from which, no matter the law of physics. We almost have to think of ourselves differently. where the head happens to be, on average it’s the shortest distance Perhaps as researchers we should no longer think of ourselves as to get to. So, this was a really important algorithm. It sped things up laboratory scientists, but more like astronomers. We have great tremendously in terms of data access. tools to observe the stars, but we can’t move them around. TH!NK: How does that relate to using data in combination? Brenda: Most of the advanced analytics we use pull in multiple sets of data. We might be pulling in bigger historical data sets when we are looking at one economic measurement versus another. Moving forward, we may want to pull in event information as well. “We should no longer think of We may want to see if an event, or a publication, or a blog, or some other signals affect our targets, and with what duration. The goal would be to pull multiple sources of data and try to determine if one piece informs another piece in any way. Can we ourselves as compute “a priori” the frequency with which two pieces or classes of data are going to be used together, and figure out some way to store them so that we can get them ready and together, at the same time? Because you can’t start the computations until you have both of laboratory those pieces. As long as the memory is in one place and the information is on a magnetic drive somewhere, you bring one in first and then wait for the other. If we could fetch them together because they were the same scientists, distance apart, if you will, it would be very interesting. That’s my notion of using data together. TH!NK: Let’s talk for a moment about titles. A Chief Risk Officer is common. I have heard people start to speak about a Chief but more like Data Officer. Brenda: I would want to be Chief Analytics Officer. astronomers.” 7
  • 10. TH!NK JUNE 2012 in review Earth Audit The finite supply of natural resources drives economies and influences pricing. Even though their costs may not be directly factored into all products and services, availability of key minerals can impact operational risk, markets, and capital. Using rough calculations, this energy audit illustrates how increases in living standards affect the rate of consumption, and brings an eye-opening perspective to the state of our planet. IF DEMAND GROWS… Some key resources will be exhausted more quickly if predicted new technologies appear and the population grows ANTIMONY 15-20 years SILVER 15-20 years HAFNIUM 10-years TANTALUM 20-30 years ˜ INDIUM 5-10 years URANIUM 30-40 years PLATINUM 15 years ZINC 20-30 years SOURCE: ARMIN RELLER, UNIVERSITY OF AUGSBURG; TOM GRAEDEL, YALE UNIVERSITY 8
  • 11. In Review © 2007 Reed Business Information - UK. All rights reserved. Distributed by Tribune Media Services 9
  • 12. TH!NK JUNE 2012 READING ROOM There’s a connection between our thought process and the courses of action we choose. These new and noteworthy titles explore the science of decision making, and the impact ideas can have on society as a whole. SCIENCE + James Gleick digs deep into The Information, a journey from the language of Africa’s talking drums to the origins of information theory. Gleick explores how our relationship to information has transformed the very nature of human consciousness. Charles Seife examines the peculiar power of numbers in Proofiness, an eye-opening look at the art of using pure mathematics for impure ends. In Being Wrong, Kathryn Schulz wonders why it’s so gratifying to be right and so maddening to be mistaken – and how attitudes towards error affect decision making and relationships. John Coates reveals the biology of financial boom and bust in The Hour Between Dog and Wolf. Coates, a trader turned neuroscientist, shows how risk-taking transforms our body chemistry and drives us to extremes of euphoria or depression. Poor Economics The Price of Civilization Paper Promises Finance and the by Abhijit Banerjee, Esther Duflo by Jeffrey Sachs by Philip Coggan Good Society PublicAffairs Random House Allen Lane by Robert Shiller Princeton University Press SCREENING ROOM + Based on the bestselling book by Andrew Ross Sorkin, Too Big To Fail reshapes the 2008 financial meltdown as a riveting thriller. Centering on U.S. Treasury Secretary Henry Paulson, the film goes behind closed doors for a captivating look at the men and women who decided the fate of the world’s economy in a few short weeks. Too Big to Fail Directed by Curtis Hanson HBO Films 10
  • 13. Reading Room The Information Proofiness Being Wrong The Hour Between by James Gleick by Charles Seife by Kathryn Schulz Dog and Wolf Vintage Viking Adult Ecco by John Coates Random House SOCIETY + Jeffrey Sachs has travelled the world to help diagnose and cure seemingly intractable economic problems. In The Price of Civilization, Sachs offers a bold plan to address the inadequacies of American-style capitalism. Abhijit Banerjee and Esther Duflo offer up a ringside view of Poor Economics, arguing that creating a world without poverty begins with understanding the daily decisions facing the poor. Philip Coggan’s Paper Promises examines debt, the global finance system, and how the current financial crisis has deep roots – going back to the nature of money itself. Robert Shiller believes that finance is more than the manipulation of money or management of risk. In Finance and the Good Society, Shiller calls for more innovation and creativity so that society can harness the power of finance for the greater good. 11
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  • 16. We are unlikely to return to the capi- talization levels or strict regional focus employed by the gentlemen of Pawtuxet. There are however crucial lessons to be learned when examining the structure and scope of early financial institutions. When we talk about addressing concerns over capital, funding, and liquidity, it just might be that what we need for the bank of tomorrow is not a new model, but rather one that takes inspiration from the bank of yesterday. FIRST DEPOSITS In the early history of banking, each partner made decisions knowing they shared liability if the bank failed. As a result, choices on whom credit should ver the decades there be extended to were not taken likely. Unambitious business owners have been many views on with a slight but steady production of widgets were considered what the bank of the future ideal customers. Less attractive was the dubious repayment would be. Some ideas have potential of innovative or entrepreneurial types. The latter group been radical and some have been represented the potential of a phenomenal return on investment, transitional, while others never really but only if an unproven product or process succeeded. took hold. This particular view begins in Pawtuxet, Rhode Island, This philosophy was in keeping with the dominant banking on the eastern seaboard of the United States. By all accounts a theory of the 18th & early 19th centuries. The real-bills doctrine lovely place to visit, Pawtuxet is well known for scenic harbour proposed that banks should restrict the extension of credit to views and boating along its historic river corridor. But in the early customers involved in the transfer of existing products only. 19th century, textile mills and coastal trade dominated its land- Real-bills supporters argued that by basing loans on the security scape. As the community thrived and local businesses grew, the of actual goods, any individual bank’s liquidity was ensured. Pawtuxet Bank emerged. Common for its time, the bank was a While there is an admirable simplicity in tying loans to tangible partnership and its directors mostly merchant-manufacturers. goods, the skyline of the 19th century was starting to change. There The Pawtuxet Bank’s directors shared personal liability in the were towers, factories and infrastructure projects that needed to event of loan default, or if the bank itself failed. In “The Struc- be built, without existing product to offer in exchange for funding. ture of Early Banks in Southeastern New England”, Naomi R. And these projects were poised to generate great returns. Lamoreaux recounts the events of June 1840 when the bank’s The unlimited liability model was ill-suited to finance these stockholders presented themselves before the Rhode Island projects. When shareholders’ money was directly on the line, banks General Assembly. The group appeared seeking permission to had good reason to avoid speculative projects. Because the incen- reduce the bank’s capitalization from $87,750 to $78,000 in tives for self-discipline were so high, banks often lent only to those order to cover losses sustained due to the death of John Pettis, they knew best. These could be local businessmen, often engaged one of the bank’s directors: in the same type of industry as the bank shareholders. Often bank Pettis died in 1838 with notes worth $8,800 outstanding at funds became personal resources for the shareholders themselves, the bank and endorsements amounting to at least another and this type of insider lending, or trading, would frequently make $1,500...(t)his loss was not sufficiently up the majority of a bank’s exposures. large to cause the bank to fail. Nor did The adjustment (towards As the world began to change, so did banks. depositors or the bank’s own noteholders By the late 1850s, Great Britain had moved suffer. Most (91 percent) of the bank’s limited liability) would towards limited liability, with France following loans were backed by capital rather than correct what had turned suit in 1867. As with unlimited liability, the notes or deposits, and the stockholders out to be the too successful logic was easy enough to follow: if a bank simply absorbed the loss. risk measure of personal could diversify its investor base, there would obligation: banks weren’t interested in funding anything risky. 14
  • 17. Back to the Future DYNAMIC CAPITAL MANAGEMENT BY FRANCIS LACAN To visualize the concept of dynamic capital management, think of flying an aircraft as close as possible to the ground. If you go too high, the be a greater availability of credit and capital. The cost of fuel becomes unreasonable. You cannot go negative because adjustment would correct what had turned out to be the option doesn’t really exist. The goal is to seek out the most efficient the too successful risk measure of personal obligation: middle ground that best mimics the changing landscape below. banks weren’t interested in funding anything risky. Managing capital dynamically would enable a bank to determine, In “Early American Banking: The Significance of on a day by day and month by month basis, the most efficient flight the Corporate Form,” Richard Sylla suggests that the path for capital and allocate it accordingly. Optimization and tipping point away from unlimited liability originated anticipation are the two extremes guiding such decisions, and in the with the New York Free Banking law of 1838 which middle reside a big set of constraints. Basel III and its liquidity coverage stated, “no shareholder of any such association shall ratio have restricted certain freedoms, particularly in terms of asset be liable in his individual capacity for any contract, qualification. The other set of constraints is risk management. debt or engagement of such association.” New York’s Liquidity is increasingly subject to risk management because there free banking law didn’t just make limited liability are lots of dependences in funding liquidity and the rest of the risk. possible. It opened the door for the incorporation of It seems liquidity is following a similar path to what happened banks and the freedom from personal obligation. with capital and solvency. Banks didn’t invest much in economic capital, but the strong requirement to look at regulatory capital ONE STEP FORWARD, TWO STEPS BACK acted as an incentive to build more analytics, more rigorous reporting, As banks moved beyond their villages in search of capital and to become more serious about addressing uncertainty with and opportunities, the strategies and measurements the right tools. What is more complex for capital management is used in their operation changed as well. Instead of to connect all the sources of information. Pulling cash flow across prudence being the only driver, customer profitability entities and supporting good cash management today still has a lot and shareholder value became ongoing concerns. of room to evolve. There are for example too many overlapping Expansions, mergers, and deregulation replaced local systems of information that are not very good at talking to one partnerships with a mandate to maximize customer another. Overcoming this hurdle would be a huge step towards bases and profitability. Operating at an extreme opposite active capital management, rebalancing, and optimization. of the early 19th century model were institutions The current baseline for automation is extremely rudimentary. like Alfinanz, an offshore administration factory that The only true mechanical element is the planning of short term inflows functioned as a global back office for a global network and outflows within the Treasury, because their contractual commit- of financial advisors, intermediaries, or brokers. ments are relatively easy to model. The rest is seen as shocks, and As banks moved from private partnerships to the focus seems to be on what the regulators are asking banks public corporations, shareholder demands added to address as the possibility of these shocks. As a result, banks are another voice to how bank capital and risk would be being pushed into modeling with greater consistency what may managed. Enhanced returns were a factor in banks’ happen with respect to different uncertainties tied to cash flows. decisions to pursue diversification, more complex In the short term, banks will have to continue on the foundations transactions such as structured products, and other of operational management of cash and collateral, addressing strategies that gained support from managers operating regulatory requirements for cash flow modeling and forecasting, with limited liability. asset qualification, and scenario modeling. Together, these elements In the modern era of banking, even the idea of ‘who will provide a rugged foundation to move towards automated is a customer’ was up for grabs. In the 1990s, First decision making, and eventually, a more automated approach to Manhattan Consulting Group took a leading role in at least some aspects of capital management. introducing profit-based segments to banking. First This prediction comes with a number of ‘ifs’: if you are able to Manhattan came to prominence with the now famous have very good and trustable aggregated pooling of all internal conclusion that only 20% of a bank’s customers were and external balances of cash in all currencies, and if you have profitable. Their idea to focus on profitable customers access to a very good repository for your treasury operations so only was an attractive one to banks who were seeking to you can see your money market for all these currencies, you could improve low revenue growth, particularly in core retail to an extent begin to automate capital allocations for particular products. The concept also encouraged mergers and the areas of the business. Decisions on how to refinance each of these creation of larger banks, who were better positioned to currencies, and perhaps rebalance positions into a smaller number take advantage of segmentation opportunities. of currencies to save costs or optimize even the risk profile of certain Today we see banks retreating from these drivers and transactions, could in theory be automated. measures, often forced to adjust strategy by regulation, This won’t happen tomorrow. But with the proper foundation, we and perhaps in retreat from acting “in loco parentis”. have the technology to make dynamic capital management part of the bank of the future. 15
  • 18. TH!NK JUNE 2012 It just might be that what we need for the bank of tomorrow is not a new model, but rather one that takes inspiration from the bank of yesterday. Shareholder demands, which focused exclusively on the creation as easy as flipping a switch. A lack of cheap availability and of shareholder value, must now be balanced against closer regulatory reduced funding sources have changed the capital landscape. scrutiny and the need to protect customer interests. Banks of the future must focus on the preservation and leverage Diversification led to its own set of challenges, as it did not help of available capital, and make that capital work harder. spread risk well. The credit crisis demonstrated that market risk and credit risk can appear in unexpected ways, and that the need to maintain strong liquidity positions was more crucial than realized. All the short term profitability in the world cannot help if the system isn’t stable. And today, if you want stability, every discus- sion must begin with the importance of access to capital. CAPITAL: THE ONCE AND FUTURE KING In his memoir On the Brink: Inside the Race to Stop the Collapse of the Global Financial System, former U.S. Secretary of the Treasury Henry Paulson reflects back on the credit crisis. One of his conclusions is that the financial system contained too much leverage, much of which was buried in complex structured products: Today it is generally understood that banks and investment banks in the U.S., Europe, and the rest of the world did not have enough capital. Less well understood is the important role that liquidity needs to play in bolstering the safety and stability of banks...(f )inancial institutions that rely heavily on short-term borrowings need to have plenty of cash on hand for bad times. And many didn’t. Politicians and regulators have joined hands on capital, proposing measures that would lead to banks holding more of it. Many banks have argued against this approach, claiming that additional capital requirements would affect performance and competition. Yet recent investigations into the correlation between bank capital and profitability suggest that holding additional capital may not Part of this focus must be organizational. Allocation of capital can be a bad thing. Which is encouraging, since banks will likely have no longer be controlled at a business unit, subsidiary, country, or to do it anyway. branch level. It needs to be allocated at the time of doing business Allen Berger and Christa Bouwman’s interests are reflected in with specific customers, business lines, and even at a transaction the title of their recent paper, “How Does Capital Affect Bank Per- level. Dynamic capital leads to a radically different structure, formance During Financial Crises?” The authors examine the where the treasury becomes the ‘owner’ of capital, lending it to effects of capital on bank performance, as well as how these deal makers on demand. effects might change during normal times as well as banking and A dynamic treasury requires great understanding of the uses and market crises. The empirical evidence led Berger and Bouwman cost of capital, connected to the technological ability to ‘solve’ the to the following conclusions: problem of Big Data. In the sidebars to this article, my colleagues First, capital enhances the performance of all sizes of banks have expanded on the linked topics of dynamic capital and during banking crises. Second, during normal times and managing complex data. market crises, capital helps only small banks unambiguously in all performance dimensions; it helps medium and large BANKING ON THE PAST banks improve only profitability during market crises and In the early 19th century, the UK limited banking partnerships to only market share during normal times. six members. No one is suggesting banks return to this restriction. Empirical evidence, regulatory measures, and perhaps common But if we think about various aspects of the unlimited liability sense dictate that holding additional capital is a worthy goal for banking model, it appears many of their tendencies are being banks. Yet even if banks wanted to raise capital thresholds, it isn’t echoed in calls from regulators and stakeholders. 16
  • 19. Back to the Future Data Complexity BY Leo Armer In the Pawtuxet model, a small number of operating partners owned the bank’s data. It was their responsibility to collect information about their clients, and use this knowledge to guide business decisions. Long-dated compensation reform and shareholder ‘say Banks today have challenges managing data, in large part on pay’ programs can be seen as measures intended because the acts of collecting and analyzing information have to update the shared liability and sense of ownership become so separated. The greater this disconnect, the more partners used to bring to banks. The credit crisis has important transparency becomes. driven home the importance of liquidity, and that gaining For both banks and clients, it’s crucial to be able to ask: “If this capital can be expensive – if it can even be acquired in is my risk number, where did it originate? How do I track it? How times of a crisis. In a way this reflects the notion early can I see which systems it passed through, and what happened bankers held that capital was expensive, and bringing to it along the way?” Being able to take a number from a balance in additional funds or partners would dilute earnings. sheet or a general ledger and drill back to its origin provides a huge Insider lending and specialization that gave way to amount of confidence. diversification and fewer restrictions on portfolios is In order to make good decisions, you need to see the big picture. being balanced by technologically enabled means to If data complexity is viewed purely as a technological issue, its better know customers. Enhanced collateral manage- strategic importance can be overlooked. When institutions attack ment and approaches like CVA can be used to gain data issues purely from an IT perspective, rules are created, trans- a deeper understanding of capital exposures before formations take place, and the data is considered ‘clean’ after entering into an agreement. going through a reconciliation process. Various systems and approaches are employed to ensure that the numbers coming out of the front system match numbers coming out of the general ledger system, and these match the numbers coming from treasury. The problem is, as much as you can clean the data on a Monday, unless you change the people or method of entering that data, it’s going to need cleaning up again on Tuesday. Today, a few firms are approaching data complexity from a business perspective. They have put their main focus on creating a single data warehouse where all the information is stored. This approach is based on the insight that every piece of data has a golden source: a reliable point of origin before it gets passed through different hands and different teams. It becomes as much about changing mental attitudes as it is about technical architectures. Creating a golden source for data becomes even more crucial when we see what has happened in the last couple of years. CVA charges for example occur when a bank puts a variable fee on top of a deal, depending on whom they are trading with. If your bank was to trade with another bank, and you had a long history with the other bank and deep insights into their credit status, the bank would likely get a better price for that trade than a small finance company from Greece who might be looking less solvent. In these transactions, where is the golden source? Is it in the middle office or front office data? Who is taking ownership over the trades? They can’t be processed the way they used to be, If banks are to thrive in the future, preservation and otherwise you’re swimming against the current demand for real leverage of available capital are crucial steps. Dynamic time responses. If it takes six or seven days to work out what allocation, enabled by a treasury that quickly and happened when a counterparty defaults, you’re too far behind effectively uses available capital in prudent ways, the curve. could perhaps be the defining characteristic of the bank It is becoming more common to run into or hear discussions of tomorrow. From the outside, these institutions about appointing a CDO, or Chief Data Officer. A CDO, or at least would look nothing like the stakeholders of the the mindset within an institution that data quality is crucial and Pawtuxet Bank, but they would be related in spirit. strategically relevant, can help banks evolve beyond workarounds and create a repository of golden source data. Through a framework where standards, direction, and architecture are provided to different departments throughout an organization, the bank of the future can overcome data complexity. 17
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  • 21. CVA THE DESK Pricing the cost of risk at Societe Generale By Bob Boettcher 19
  • 22. TH!NK JUNE 2012 , “I wouldn t want to overstate it – it’s not bringing the industry to a halt. But there is increasing focus on limiting exposures, even among global banks. And that is starting to affect the way we do business.” 20
  • 23. The CVA Desk THE SETUP Life used to be different – at least in terms of how counterparty credit risk was calcu- lated. In the past, an interest rate swap would have been priced the same for every client. But Lehman’s default, and more re- cently the Greek sovereign stress, has changed all that. Now, no client is assumed to be truly risk free. Different prices are now expected for different clients on that same interest rate swap, depending on variables including the client’s rating and the overall direction of existing trades be- A tween both parties. ny time one bank takes Noting their emergence, and particularly a risk against another their activity in the sovereign CDS market, the probability of default the Bank of England defined CVA desks in exists. To offset this their 2010 Q2 report as follows: concern, and to support A commercial bank’s CVA desk c ongoing stability within centralises the institution’s control the interbank market, of counterparty risks by managing banks have long emphasized the impor- counterparty exposures incurred tance of measuring and managing coun- by other parts of the bank...CVA terparty risk. Yet over the past few desks will charge a fee for managing months banks have becomes noticeably these risks to the trading desk, less comfortable trading with each other. which then typically tries to pass The recent deterioration in credit ratings this on to the counterparty through that has hit many U.S. and European banks the terms and conditions of the has led to a heightened sensitivity over trading contract. But CVA desks are counterparty risk. These apprehensions not typically mandated to maximise may not be voiced directly, but they profits, focusing instead on risk become evident when front office trades management. that would have cleared in the past The Bank of England’s summary captures no longer do because credit lines have the classic model for running a CVA desk, been reduced. which Murphy has implemented at SG As head of the CVA desk at Societe CIB. The classic approach incorporates Generale Corporate & Investment Banking three elements: (SG CIB), David Murphy has a unique 1. pricing of new trades vantage point on interbank relationships. 2. transferring risk to a centralized “I wouldn’t want to overstate it – it’s not desk from individual desks bringing the industry to a halt. But there is 3. hedging or otherwise mitigating the increasing focus on limiting exposures, even aggregated risk on a global basis among global banks. And that is starting to On all new interest rate, FX, equity, or affect the way we do business.” credit derivatives, CVA desks price the CVA desks have grown in popularity as marginal counterparty risk for inclusion banks seek more effective ways to manage into the overall price charged to the client. and aggregate counterparty credit risk. CVA is a highly complex calculation – and From his seat at SG CIB, David has a bird’s manually calculating that for the thou- eye view on the challenges associated with sands of trades and potential trades that establishing CVA desks, and the benefits pass through a bank every day isn’t realistic. banks can realize by gaining an active view An effective automated system therefore on their portfolio of credit risk. becomes crucial to a CVA desk’s viability. 21
  • 24. TH!NK JUNE 2012 UPSIDES OF AUTOMATION “Really, what our sales team are interested “For a plain vanilla trade with another bank done on an electronic trading in, is earning as much as possible net of the platform, our target delivery time for the price is approximately 10 milliseconds,” CVA. Through the automated system tools, says David. “On the other end of the complexity spectrum, highly-structured, we’ve empowered sales and traders to do long-dated trades may require two or three days to calculate the CVA price. trades with the lowest CVA possible. So it’s Within this range we deliver CVA pricing within timescales that don’t delay worth their while spending time looking the overall trade completion.” for that price, and they can now do that While automated pricing copes well with vanilla products and the speeds themselves quickly and efficiently – without required for those trades, there will always be exotic trades, trades where the delays or extra resources required clients have a non-standard credit story, or a trade with special risk mitigation. when using the manual pricing process.” In these cases, David has a team of four who provide this manual pricing to These ‘pre-deal’ checks are purely Sales and Traders on request. indicative – and optional for Sales and “We try to reduce the need for manual pricing as much as possible, but the Traders. But if they don’t do this check, they business will always have trades where they need someone to take a closer face a big risk, because every time a new look. There may also be situations where we think the automated pricing trade is booked, an ‘official’ CVA fee is then isn’t good enough, so we want to take a look anyway and we don’t allow the auto-calculated – which is then recorded sales or trades to use the automated pricing provided,” he explains. alongside the trade – and will be deducted In the manual process, the CVA desk team often passes along suggestions from the Sales/Trader performance. to the salesperson for improving the credit risk in a trade and enabling the sales person to offer the trade at a lower credit price. Examples of that would PERILS OF PRICING include improving the collateral agreement with a client, or inserting a “A key challenge of building a CVA pricing break clause. system is ensuring real-time access to data “Via the manual process, we have educated our sales team and traders how in three categories: trade details; static they can change the credit risk (and reduce the price). With this knowledge, data (client data such as rating, details they now use the automated pre-deal CVA calculations to provide several of all pre-existing trades, netting status, CVA prices for different versions of the same trade. This allows them to collateral details etc), and market data.” achieve the best price for the client – while minimizing the counterparty risk.” “Designing the system which has reliable and timely data in all 3 categories is crucial, given the impact that will have on pricing and/or hedging decisions. It’s a tough market with sophisticated competitors: if we under-price a risk, you can be sure we will start attracting a large market share. And if we over-price, then we lose business unnecessarily.” “We try to reduce the need for manual pricing as much as possible, but the business will always have trades where they need someone to take a closer look.” 22
  • 25. The CVA Desk “Through the automated system tools, we’ve empowered sales and traders to do trades with the lowest CVA possible.” HALFWAY TO HEDGING “If you take SG CIB’s total portfolio of clients, just over Murphy’s first priority for SG CIB has been 10% have a liquid CDS curve. In other words, for 90% of to ensure the CVA desk correctly prices our clients, if we wanted to go and buy CDS protection, the risk in all new trades. Now that this we couldn’t do it because there’s no market. To hedge process is well-advanced, the desk will these illiquid risks, banks would need to use some start to focus more on hedging – or otherwise kind of credit index.” mitigating – its legacy portfolio of credit But this is an imperfect hedge. In ‘normal’ times, the risks. “For hundreds of years banks have credit spread of the index and the ‘generic’ spread managed reasonably well hedging 0% of applied to calculate the client’s CVA will move in tandem. their counterparty risk. So instead of an The hedge is therefore effective in reducing earnings instant seismic shift to 100% hedging of all volatility from day-to-day changes in the CVA Reserve. risks, we will be hedging selected segments But, if the client deteriorates – or even defaults – due to of the credit and market risk – which an idiosyncratic reason, then the index hedge may not avoids paying away all CVA income to the be affected at all (i.e. the hedge doesn’t work). So the market,” says Murphy. decision whether to hedge illiquid names depends on For banks evolving the CVA function, what you want to protect against: actual losses following there are two main reasons hedging is not default… or earnings volatility caused by changes in further along. The first is technology: they market credit spreads. may not yet be fully confident in their risk measurement system, which requires a complex and time-intensive development period. The second reason is strategic: the bank might not think that all of its potential hedges are very useful. 23
  • 26. TH!NK JUNE 2012 In the traditional CVA ACCOUNTING FOR BASEL In the traditional CVA approach, a bank accepts a new trade, takes a fee and uses that fee to buy good hedges for all the risks in that approach, a bank accepts trade. These hedges should eliminate all of the bank’s risk, but this is not necessarily the case once Basel III is taken into account. a new trade, takes a fee Basel III does not recognize all types of hedges that the bank might want to use. Therefore the regulatory capital for certain and uses that fee to buy trades will not be zero, even if the bank has used the full CVA fee to hedge all its risks. The first impact Basel III has on CVA desks is on pricing. good hedges for all the Pre-deal pricing needs to be reviewed to ensure the costs of imposed regulatory capital are covered. If not, additional pricing risks in that trade. These may need to be added. And the decision on which risks are efficient to hedge also becomes affected not just by strategic or hedges should eliminate business reasons, but also by the regulatory capital impact. As part of Basel III’s updated regulatory capital guidelines, a new element has been added: VaR on CVA. Regulators have all of the bank’s risk… specified very precisely how the underlying CVA must be calcu- lated for this charge. Banks will therefore need to decide whether to adjust their pricing and balance sheet CVA to match the BIII rules, or to use different CVA calculations for pricing and Regulatory purposes. 24
  • 27. The CVA Desk A DEFINING ROLE When individual trading desks own risk, one desk may have had a positive exposure to a client. This could lead the desk to hedge the positive exposure, without knowing that there was a negative exposure at another desk, which means the hedge wasn’t really necessary. Because the CVA desk owns all the risk from all the different derivatives desks, it has a full view of the risks with each counterparty, across all desks, products and locations and can price and hedge the risk appropriately. It has been suggested that a CVA desk is just a ‘smart middle office’. Murphy doesn’t agree: “The main difference between CVA other front office trading functions is that most of the risks are originated internally from the bank’s other trading desks. But the CVA desk must price, originate, and distribute those risks in exactly the same way as any other front office trading desk.” …but this is not CVA desks have evolved to price, centralize, and manage a bank’s counterparty risks, requiring sophisticated modeling of hybrid risks encompassing every asset class that the bank is necessarily the case involved in. When implemented correctly, CVA desks should support an institution’s business and strategic vision, while helping once Basel III is taken banks maintain normalized relationships and control risk in an ever more complex trading universe. into account. 25
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  • 29. The OPTIMIZATION of EVERYTHING OTC Derivatives, Counterparty Credit Risk and Funding By Jon Gregory The global financial crisis has created much excitement over counterparty credit risk (CCR) and, in recognition of this, banks have been improving their practices around CCR. In particular, the use of CVA (credit value adjustment) to facilitate pricing and management of CCR has increased significantly. Indeed, many banks have CVA desks that are responsible for pricing and managing CVA across trading functions. In addition to CVA, DVA (debt value adjustment) is often used as recognition of the “benefit” arising from one’s own default and funding aspects may be considered via funding value adjustment (FVA). Also, the impact that collateral has on CVA, DVA and FVA is important to quantify. Finally, there is a need to consider the impact of the funding requirements and systemic risk when trading with central counterparties (CCPs). 27
  • 30. TH!NK JUNE 2012 T he dynamics of trading OTC derivatives is becoming increasingly driven by the components mentioned above. Such a trend can only grow as regulation arising from Basel III creates the need for significantly increased amounts of capital to be held against CCR. It therefore seems likely that banks will not only invest significantly in building knowledge around the afore- mentioned concepts but will also optimize their trading decisions. For example, should one trade through a CCP or not? Is it pref- erable to trade with a counterparty via a 2-way collateral agree- ment (CSA)? Should we collateralize via cash or other securities? What currency should I post cash collateral in? There are a number of considerations around optimizing OTC derivatives trading with respect to CCR, funding, and systemic risk. Collateral. A “margin From the point of view of a bank, an OTC derivative transaction period of risk” of 20 days depends very much on the type of counterparty to the trade. must be applied for trans- Most unsophisticated users of OTC derivatives will not post collat- actions where netting sets are eral against positions while more sophisticated users will post large (i.e. over 5,000 trades), have collateral or trade through a central counterparty. This creates a illiquid collateral, or represent hard-to- wide spectrum of behaviour with respect to CCR and funding replace derivatives. The current time frame aspects that we will discuss. A bank then has the issue of deter- on such transaction is 5-10 days. No benefit can mining how best to optimize their trading across this spectrum. be achieved from downgrade triggers (e.g. receiving more collateral if the rating of a counterparty deteriorates). The impact of regulation In addition, additional haircuts for certain securities The Basel III rules will be phased in from the beginning of 2013 and the liquidity coverage ratio will limit the amount of and will force banks to hold a lot more equity capital, much of rehypothecation (reuse of collateral) and encourage the which is due to CCR requirements. Ballpark estimates are that use of cash collateral. This ratio aims to ensure that a bank most large banks will have to more than triple the amount of maintains an adequate level of unencumbered, high- equity held compared with pre-crisis. Loopholes to reduce capital quality liquid assets that can be converted into cash to requirements, such as off balance-sheet entities, are being meet its liquidity needs. closed. A trillion dollars or so of extra equity will need to be CVA VAR. Banks must hold additional capital to capture the raised by American banks by the end of the implementation of volatility of CVA. This is in addition to the current rules that Basel III (2019) with European banks needing to raise a similar capitalise default risk. figure. Basel III will have a profound effect on banking behaviour. Central counterparties. A risk weighting of 2% will be The changes will make all banking activities more expensive, in given to exposures to a CCP, not only via margin posted but particular exposures held in the trading book. also via the default fund contribution that must be made. In Under Basel III, the changes around CCR (that will apply to banks addition, the CCP must meet various rigorous conditions, from 1 January 2013) are particularly significant and include: including the establishment of a high specific level of initial Stressed EPE. Banks which have permission to use the margin and ongoing collateral posting requirements, and internal models method (IMM) must calculate exposures that it has sufficient financial resources to withstand the using data that includes a period of stressed market conditions, default of significant participants. While this represents an if this is higher than the standard calculation. increase (from zero) in capitalization of CCP exposures, it Wrong way risk. Banks must identify exposures that give is intended to incentivise the clearing of OTC derivatives rise to a greater degree of “general” wrong-way risk and must through CCPs. assume a higher exposure for transactions with “specific” wrong-way risk. Collateral and CCPs Systemic risk. Banks must apply a correlation multiplier of Collateral arrangements involve parties posting cash or securities 1.25 to all exposures to regulated financial firms with assets to mitigate counterparty risk, usually governed under the terms of at least $100 billion and to all exposures to unregulated of an ISDA Credit Support Annex (CSA). The typical frequency of financial firms. posting is daily and the holder of collateral pays an (typically 28