The document discusses the challenges of implementing a counterparty risk management process. It outlines key objectives such as centralized data storage, CVA pricing that incorporates collateral and netting agreements, and regulatory capital calculations. Significant challenges include gathering transaction data from multiple systems, performing complex CVA calculations on the entire portfolio, and meeting objectives like near real-time pricing while managing regulatory requirements.
CH&Cie - Fundamental Review of the Trading BookC Louiza
Arbitrage opportunity Banking book vs Trading book • The classification of assets between the banking book and trading book was unclear allowing arbitrage opportunity for RWA
Overview of the Basel Committee's revised "Minimum capital requirements for market risk" (formerly FRTB), with notes and tips for technical implementation.
CH&Cie - Fundamental Review of the Trading BookC Louiza
Arbitrage opportunity Banking book vs Trading book • The classification of assets between the banking book and trading book was unclear allowing arbitrage opportunity for RWA
Overview of the Basel Committee's revised "Minimum capital requirements for market risk" (formerly FRTB), with notes and tips for technical implementation.
Quantifi Whitepaper: The Evolution Of Counterparty Credit Riskamoini
Written by David Kelly (Head of Credit and Counterparty Risk Product Development, Quantifi) and Jon Gregory (former Head of Counterparty Risk at Barclays Capital)
Counterparty Credit Risk and CVA under Basel IIIHäner Consulting
Financial institutions which apply for an IMM waiver under Basel III need to fullfill a broad set of requirements. We present the quantitative, organizational and operational implications and provide some hand-on guidance how to fulfill the regulatory requirements.
Because the VaR starts to be « old fashioned » and not so "Normal" :-), CH&Co. and its GRA team wanted to pay a last tribute to this world famous Market Risk Method.
This paper comes along with a Excel Tool
Outlook and market survey on the fresh Standards for Minimum capital requirements for market risk, published January 14th, 2016.
FRTB will deeply impact banks on IT, process, organization and human aspects.
CH&Co can help banks cope with these changes.
Operational Risk Loss Forecasting Model for Stress TestingCRISIL Limited
Presentation on ‘Operational Risk Loss Forecasting Model for Stress Testing – A Three-Stage Approach’ made by Dr. James Lu, Director, Risk & Analytics, CRISIL Global Research & Analytics (GR&A) at The 17th Annual OpRisk North America 2015, New York
CH&CO - VaR methodology whitepaper - 2015 C Louiza
In the framework of knowledge promotion and expertise sharing, Chappuis Halder & Co. decided to give free access to the “Value-at-Risk Valuation tool” named in our paper “VaR spreadsheet estimator”. It contains the detail sheets simulations for the three main Value-at-Risk methods: Variance/covariance VaR, Historical VaR and Monte-Carlo VaR. The presented methodologies are not exhaustive and more exist and can be adapted depending on the process constraints.
This paper aims to have a theoretical approach of VaR and define all relevant steps to compute VaR according to the defined methodology. And to go further, it seems important to define VaR for a linear financial instrument. Thus, illustrations to monitor the VaR for an equity stock has been performed with a European call option VaR simulations for a better understanding of the concept and the tool. This article only focuses on VaR but will provide opportunities to open to more quantitative risk indicators as Stress-tests, Back-testing, Comprehensive risk measure (CRM), Expected Tail Loss (ETL) or Conditional VaR… more or less linked with the VaR methodologies…
MODULE 3:
Credit Risks Credit Risk Management models - Introduction, Motivation, Funtionality of good credit. Risk Management models- Review of Markowitz’s Portfolio selection theory –Credit Risk Pricing Model – Capital and Rgulation. Risk management of Credit Derivatives.
Solving the FRTB Challenge: Why You Should Consider an Aggregation SolutionFIS
Many banks face multiple challenges around market risk, with outdated infrastructure, fragmented systems, and inflexible reporting tools. And now FRTB raises the stakes. The Fundamental Review of the Trading Book is the biggest change in market risk rules that we’ve seen in a generation.
The answer to the FRTB challenge is a centralized aggregation solution that allows you to source required prices from one or more front-office and risk engines, perform bank-wide FRTB calculations using those inputs, and combine the results with intermediate data and expose inputs via reporting and analysis tools.
View our slideshow to learn more about aggregation challenges and why you should consider an external solution.
everis Marcus Evans FRTB Conference 23Feb17Jonathan Philp
everis was Gold Sponsor of the Marcus Evans Conference ‘4th Edition: Impact of the Fundamental Review of the Trading Book’ at Canary Wharf, London on 23-24th February 2017.
This was a timely opportunity to catch up with banks and solution partners as we move into the implementation phase of Fundamental Review of the Trading Book (FRTB) programmes. We heard views and case studies across a range of topics including market risk methodology, operating model definition and data and systems architecture design.
Our presentation at the conference focused on the architectural challenges posed by FRTB.
Key learnings of recent AQR & CCAR exercises suggest that some significant moves are required to fulfil market & regulators expectations. In this context, CH&Cie is pleased to share with you the latest developments in implementing stress testing as well as best practices
Technical challenges of CVA implementation
Objectives of a CVA information system
Main technical challenges
Implementation use cases and lessons learnt
Counterparty Credit RISK | Evolution of standardised approachGRATeam
In this Article, we have made a focus on the new standard methodology (SA-CCR) for computing the EAD related to Counterparty Credit Risk portfolios. The implementation of a SA-CCR approach will become increasingly important for the Banks given the publication of the finalised Basel III reforms; in which it will require from financial institutions to compute an output floor to compare their level of RWAs between Internal and Standard approaches.
Counterparty Credit Risk | Evolution of
the standardised approach to determine the EAD of counterparties
This article focuses on Counterparty Credit Risk. The topic of this article is on the evolution and need of standardised method for the assessment of Exposure at Default of counterparties and their Capitalisation under regulatory requirements.
Quantifi Whitepaper: The Evolution Of Counterparty Credit Riskamoini
Written by David Kelly (Head of Credit and Counterparty Risk Product Development, Quantifi) and Jon Gregory (former Head of Counterparty Risk at Barclays Capital)
Counterparty Credit Risk and CVA under Basel IIIHäner Consulting
Financial institutions which apply for an IMM waiver under Basel III need to fullfill a broad set of requirements. We present the quantitative, organizational and operational implications and provide some hand-on guidance how to fulfill the regulatory requirements.
Because the VaR starts to be « old fashioned » and not so "Normal" :-), CH&Co. and its GRA team wanted to pay a last tribute to this world famous Market Risk Method.
This paper comes along with a Excel Tool
Outlook and market survey on the fresh Standards for Minimum capital requirements for market risk, published January 14th, 2016.
FRTB will deeply impact banks on IT, process, organization and human aspects.
CH&Co can help banks cope with these changes.
Operational Risk Loss Forecasting Model for Stress TestingCRISIL Limited
Presentation on ‘Operational Risk Loss Forecasting Model for Stress Testing – A Three-Stage Approach’ made by Dr. James Lu, Director, Risk & Analytics, CRISIL Global Research & Analytics (GR&A) at The 17th Annual OpRisk North America 2015, New York
CH&CO - VaR methodology whitepaper - 2015 C Louiza
In the framework of knowledge promotion and expertise sharing, Chappuis Halder & Co. decided to give free access to the “Value-at-Risk Valuation tool” named in our paper “VaR spreadsheet estimator”. It contains the detail sheets simulations for the three main Value-at-Risk methods: Variance/covariance VaR, Historical VaR and Monte-Carlo VaR. The presented methodologies are not exhaustive and more exist and can be adapted depending on the process constraints.
This paper aims to have a theoretical approach of VaR and define all relevant steps to compute VaR according to the defined methodology. And to go further, it seems important to define VaR for a linear financial instrument. Thus, illustrations to monitor the VaR for an equity stock has been performed with a European call option VaR simulations for a better understanding of the concept and the tool. This article only focuses on VaR but will provide opportunities to open to more quantitative risk indicators as Stress-tests, Back-testing, Comprehensive risk measure (CRM), Expected Tail Loss (ETL) or Conditional VaR… more or less linked with the VaR methodologies…
MODULE 3:
Credit Risks Credit Risk Management models - Introduction, Motivation, Funtionality of good credit. Risk Management models- Review of Markowitz’s Portfolio selection theory –Credit Risk Pricing Model – Capital and Rgulation. Risk management of Credit Derivatives.
Solving the FRTB Challenge: Why You Should Consider an Aggregation SolutionFIS
Many banks face multiple challenges around market risk, with outdated infrastructure, fragmented systems, and inflexible reporting tools. And now FRTB raises the stakes. The Fundamental Review of the Trading Book is the biggest change in market risk rules that we’ve seen in a generation.
The answer to the FRTB challenge is a centralized aggregation solution that allows you to source required prices from one or more front-office and risk engines, perform bank-wide FRTB calculations using those inputs, and combine the results with intermediate data and expose inputs via reporting and analysis tools.
View our slideshow to learn more about aggregation challenges and why you should consider an external solution.
everis Marcus Evans FRTB Conference 23Feb17Jonathan Philp
everis was Gold Sponsor of the Marcus Evans Conference ‘4th Edition: Impact of the Fundamental Review of the Trading Book’ at Canary Wharf, London on 23-24th February 2017.
This was a timely opportunity to catch up with banks and solution partners as we move into the implementation phase of Fundamental Review of the Trading Book (FRTB) programmes. We heard views and case studies across a range of topics including market risk methodology, operating model definition and data and systems architecture design.
Our presentation at the conference focused on the architectural challenges posed by FRTB.
Key learnings of recent AQR & CCAR exercises suggest that some significant moves are required to fulfil market & regulators expectations. In this context, CH&Cie is pleased to share with you the latest developments in implementing stress testing as well as best practices
Technical challenges of CVA implementation
Objectives of a CVA information system
Main technical challenges
Implementation use cases and lessons learnt
Counterparty Credit RISK | Evolution of standardised approachGRATeam
In this Article, we have made a focus on the new standard methodology (SA-CCR) for computing the EAD related to Counterparty Credit Risk portfolios. The implementation of a SA-CCR approach will become increasingly important for the Banks given the publication of the finalised Basel III reforms; in which it will require from financial institutions to compute an output floor to compare their level of RWAs between Internal and Standard approaches.
Counterparty Credit Risk | Evolution of
the standardised approach to determine the EAD of counterparties
This article focuses on Counterparty Credit Risk. The topic of this article is on the evolution and need of standardised method for the assessment of Exposure at Default of counterparties and their Capitalisation under regulatory requirements.
RISK-ACADEMY’s guide on risk appetite in non-financial companies. Free downloadAlexei Sidorenko, CRMP
Risk appetite refers to an individual or organization’s willingness to take on risks in pursuit of potential returns. It is an important consideration for businesses, as it can determine the types of investments and strategic decisions they make. A high risk appetite may lead to a focus on high-growth, speculative investments, while a low risk appetite may result in a preference for more conservative, steady returns. It is important for businesses to carefully assess and manage their risk appetite in order to make informed decisions and achieve their financial goals.
But before beginning the conversation about risk appetite, it is important to remember that most non financial organizations have already documented their appetites for different common decisions or business activities. Segregation of duties, financing and deal limits, vendor selection criteria, credit limits, treasury limits on banks, investment criteria, zero tolerance to fraud or safety risks – are all examples of how organizations set risk appetite.
What is risk appetite:
10% of the time risk appetite is imposed by laws and regulations, not set – Often risk appetite is imposed by government, regulators, markets, not set by management. Examples include zero-tolerances or limits on safety, bribery and corruption, AML, pollution, sanctions, privacy.
10% of the time risk appetite is the gentlemen’s agreement between Board and management – Boards have an important oversight role and help them set the direction and boundaries for management decision making. Those management decision making boundaries is risk appetite. Examples include deal approvals only by Board above a certain limit, limits on holding percentage of cash in certain pre-approved banks, market risk limits, credit risk limits, insurance thresholds, rules on credit limits for certain types of customers, limits on investments in different countries, etc.
80% of the time risk appetite is the risk reward trade-off for a specific decision – The key is making uncertainty around decisions presented to the Board transparent to allow decision makers choose the alternative which offers the most appropriate risk reward balance according to their individual appetites.
Download the full guide to read about documenting risk appetite, reviewing risk appetite, case studies and examples and addition video resources: Guide to risk appetite 2023
Building out a Robust and Efficient Risk Management - Alan CheungLászló Árvai
Credit Derivatives are off-balance sheet financial statements that permit one party to transfer the risk of a reference asset, which it typically owns, to another one party (the guarantor) without actually selling the assets.
Quantifi whitepaper how the credit crisis has changed counterparty risk man...Quantifi
This paper will explore some of the key changes to internal counterparty risk management processes by tracing typical workflows within banks before and after CVA desks, and how increased clearing due to regulatory mandates, affects these workflows. Since CVA pricing and counterparty risk management workflows require extensive amounts of data, as well as a scalable, high-performance technology, it is important to understand the data management and analytical challenges involved.
• Current trends and best practices
• Key data and technology challenges
Strategic Intraday Liquidity Monitoring Solution for Banks: Looking Beyond Re...Cognizant
Managing intraday liquidity monitoring is an essential task for banks facing potential shortfalls in cash flow due to highly complex collaborations with other institutions and clients. To go beyond mere compliance with regulatory strictures, we offer a path toward an intraday liquidity platform based on integrated, real-time data.
The spring 2015 Insight newsletter from Quantifi, discussing microservices and the recently published consultative document ‘Review of the Credit Valuation Adjustment (CVA) risk framework’
by the Basel Committee
By 1st December 2015, BCBS-IOSCO rules mean that all eligible financial and non-financial counterparties must be able to exchange bilateral Variation Margin (VM) and Initial Margin (IM) with their OTC derivatives counterparties. The consequences of this extend far beyond methodology, requiring a re-evaluation of the whole end to end workflow.
Evaluation of Capital Needs in Insurancekylemrotek
Presentation on capital adequacy analysis for property casualty insurance companies, as presented to Milliman\'s 2008 Casualty Consultants Forum in Denver
The spring 2017 Insight newsletter from Quantifi, discussing FRTB and whether it is strengthening market risk practices, and whether banks are prepared for the changes it will bring
The July 2015 Insight newsletter, discussing the changing regulatory landscape and including a conversation with Matthew Lynes, Senior Investment Manager at Aberdeen Asset Management
The spring 2014 Insight newsletter from Quantifi, including a conversation with Hannan Mohammed, deputy head of the funding and markets division of AFD, and a Q&A with Mark Traudt, CTO of Quantifi.
The spring 2013 Insight newsletter from Quantifi, discussing the management of counterparty credit risk.
A conversation with Arne Loftingsmo, Portfolio Manager at KLP Kapitalforvaltning AS.
The autumn 2012 newsletter from Quantifi, discussing alternative methods for calculating CVA charges under Basel III. Robert Goldstein, Director of Client Services at Quantifi talks about Quantifi V10.3 and we chat with Joost Zuidberg, Managing Director of The Currency Exchange Fund
Conversation with Matthew Lynes, Aberdeen Asset Management. Buy-Side System Requirements - Whitepaper by Quantifi and OTC Partners. The Cost of Collateral - Webinar Survey.
In the last few years, the financial markets have undergone dramatic change. While some of this is down to natural evolution, much of the change can be directly attributed to new rules introduced in the wake of the 2007 crisis. Regulators, legislators and central bank governors have been determined to avert another bubble bursting or an unexpected event that could threaten markets. Lawmakers have targeted key financial practices for reform, radically altering the expectations and behavior of industry participants. The combination of the Dodd-Frank Act, European Markets Infrastructure Regulation (EMIR), MiFID ll and Basel lll signify the biggest regulatory change in decades. These reforms have resulted in major change to how financial products are traded, settled, collateralized and reported, resulting in deep and ongoing structural changes to the markets.
There is no doubt that these new rules are directly impacting buy-side firms — be they asset managers, hedge funds, insurance companies or pension funds. But while the changes have certainly brought challenges, they have also brought opportunities. Firms that can proactively evaluate structural and operational dislocations in the marketplace and tailor business models to leverage the opportunities while addressing the challenges will be in the best position to stand apart from their competitors. Revised business models call for revisions to supporting processes and systems. Buy-side firms should look to re-architect their processes and technology infrastructure, with a goal to strengthen risk control and oversight, enhance transparency and improve efficiency of front-to-back office control functions.
The credit crisis, and the regulatory response it spawned have fundamentally reshaped financial markets for buy-side firms. But while the changes have brought about challenges, they have also ushered in opportunities. The key to success will be the speed with which firms are able to understand the changing marketplace and adapt their business models to align with the changes.
IFRS 13 CVA DVA FVA and the Implications for Hedge Accounting - By Quantifi a...Quantifi
International Financial Reporting Standard 13: fair value measurement (IFRS 13) was originally issued in May 2011 and applies to annual periods beginning on or after 1 January 2013. IFRS 13 provides a framework for determining fair value, clarifies the factors to be considered for estimating fair value and identifies key principles for estimating fair value. IFRS 13 facilitates preparers to apply, and users to better understand, the fair value measurements in financial statements, therefore helping improve consistency in the application of fair value measurement.
What price will pi network be listed on exchangesDOT TECH
The rate at which pi will be listed is practically unknown. But due to speculations surrounding it the predicted rate is tends to be from 30$ — 50$.
So if you are interested in selling your pi network coins at a high rate tho. Or you can't wait till the mainnet launch in 2026. You can easily trade your pi coins with a merchant.
A merchant is someone who buys pi coins from miners and resell them to Investors looking forward to hold massive quantities till mainnet launch.
I will leave the telegram contact of my personal pi vendor to trade with.
@Pi_vendor_247
What website can I sell pi coins securely.DOT TECH
Currently there are no website or exchange that allow buying or selling of pi coins..
But you can still easily sell pi coins, by reselling it to exchanges/crypto whales interested in holding thousands of pi coins before the mainnet launch.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and resell to these crypto whales and holders of pi..
This is because pi network is not doing any pre-sale. The only way exchanges can get pi is by buying from miners and pi merchants stands in between the miners and the exchanges.
How can I sell my pi coins?
Selling pi coins is really easy, but first you need to migrate to mainnet wallet before you can do that. I will leave the telegram contact of my personal pi merchant to trade with.
Tele-gram.
@Pi_vendor_247
Turin Startup Ecosystem 2024 - Ricerca sulle Startup e il Sistema dell'Innov...Quotidiano Piemontese
Turin Startup Ecosystem 2024
Una ricerca de il Club degli Investitori, in collaborazione con ToTeM Torino Tech Map e con il supporto della ESCP Business School e di Growth Capital
Financial Assets: Debit vs Equity Securities.pptxWrito-Finance
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
Exploring Abhay Bhutada’s Views After Poonawalla Fincorp’s Collaboration With...beulahfernandes8
The financial landscape in India has witnessed a significant development with the recent collaboration between Poonawalla Fincorp and IndusInd Bank.
The launch of the co-branded credit card, the IndusInd Bank Poonawalla Fincorp eLITE RuPay Platinum Credit Card, marks a major milestone for both entities.
This strategic move aims to redefine and elevate the banking experience for customers.
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the contact information for my personal pi vendor.
Telegram: @Pi_vendor_247
where can I find a legit pi merchant onlineDOT TECH
Yes. This is very easy what you need is a recommendation from someone who has successfully traded pi coins before with a merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi network coins and resell them to Investors looking forward to hold thousands of pi coins before the open mainnet.
I will leave the telegram contact of my personal pi merchant to trade with
@Pi_vendor_247
how can I sell pi coins after successfully completing KYCDOT TECH
Pi coins is not launched yet in any exchange 💱 this means it's not swappable, the current pi displaying on coin market cap is the iou version of pi. And you can learn all about that on my previous post.
RIGHT NOW THE ONLY WAY you can sell pi coins is through verified pi merchants. A pi merchant is someone who buys pi coins and resell them to exchanges and crypto whales. Looking forward to hold massive quantities of pi coins before the mainnet launch.
This is because pi network is not doing any pre-sale or ico offerings, the only way to get my coins is from buying from miners. So a merchant facilitates the transactions between the miners and these exchanges holding pi.
I and my friends has sold more than 6000 pi coins successfully with this method. I will be happy to share the contact of my personal pi merchant. The one i trade with, if you have your own merchant you can trade with them. For those who are new.
Message: @Pi_vendor_247 on telegram.
I wouldn't advise you selling all percentage of the pi coins. Leave at least a before so its a win win during open mainnet. Have a nice day pioneers ♥️
#kyc #mainnet #picoins #pi #sellpi #piwallet
#pinetwork
The secret way to sell pi coins effortlessly.DOT TECH
Well as we all know pi isn't launched yet. But you can still sell your pi coins effortlessly because some whales in China are interested in holding massive pi coins. And they are willing to pay good money for it. If you are interested in selling I will leave a contact for you. Just telegram this number below. I sold about 3000 pi coins to him and he paid me immediately.
Telegram: @Pi_vendor_247
Currently pi network is not tradable on binance or any other exchange because we are still in the enclosed mainnet.
Right now the only way to sell pi coins is by trading with a verified merchant.
What is a pi merchant?
A pi merchant is someone verified by pi network team and allowed to barter pi coins for goods and services.
Since pi network is not doing any pre-sale The only way exchanges like binance/huobi or crypto whales can get pi is by buying from miners. And a merchant stands in between the exchanges and the miners.
I will leave the telegram contact of my personal pi merchant. I and my friends has traded more than 6000pi coins successfully
Tele-gram
@Pi_vendor_247
How to get verified on Coinbase Account?_.docxBuy bitget
t's important to note that buying verified Coinbase accounts is not recommended and may violate Coinbase's terms of service. Instead of searching to "buy verified Coinbase accounts," follow the proper steps to verify your own account to ensure compliance and security.
Challenges in Implementing a Counterparty Risk Management Process
1. WHITE PAPER
CHALLENGES IN IMPLEMENTING
A COUNTERPARTY RISK
MANAGEMENT PROCESS
Authored by David Kelly (Quantifi)
• Key data and technology challenges
• Current trends and best practices
www.quantifisolutions.com
2. About the Author
David Kelly
David Kelly, Director of Credit Product Development, Quantifi, brings almost 20
years of experience as a trader, quant, and technologist to Quantifi. He has
previously held senior positions at some of the largest financial institutions
including Citigroup, JPMorgan Chase, AIG and CSFB. At Citigroup, he was the
senior credit trader on the Global Portfolio Optimisation desk, responsible for
actively managing the credit risk in derivatives positions. Prior to this, he ran
the JPMorgan Chase Global Analytics group, where he was responsible for
front-office pricing models and risk management tools for the global derivatives
trading desks including the firm’s first CVA system. An enrolled member of
the Society of Actuaries, Mr.Kelly holds a B.A. in economics and mathematics
from Colgate University and has completed graduate work in mathematics at
Columbia University and Carnegie Mellon.
3. Challenges in Implementing a
Counterparty Risk Management Process
Introduction
Most banks are in the process of setting up counterparty risk management processes or improving
existing ones. Unlike market risk, which can be effectively managed by individual trading desks or traders,
counterparty risk is increasingly being priced and managed by a central CVA desk or risk control group since
the exposure tends to span multiple asset classes and business lines. Moreover, aggregated counterparty
exposure may be significantly impacted by collateral and cross-product netting agreements.
Gathering transaction and market data from potentially many trading systems, along with legal agreements
and other reference data, involves significant and often underestimated data management issues.
The ability to calculate credit value adjustments (CVA) and exposure metrics on the entire portfolio,
incorporating all relevant risk factors, adds substantial analytical and technological challenges. Furthermore,
traders and salespeople expect near real-time performance of incremental CVA pricing of new transactions.
Internal counterparty risk management must also be integrated with regulatory processes.
In short, the data, technological, and operational challenges involved in implementing a counterparty
risk management process can be overwhelming. This paper outlines the key challenges, starting with an
overview of the main business objectives, followed by a discussion of data and technology issues, and
current trends in best practices.
4. Objectives
The objectives in setting up a counterparty risk management process can be split into three categories – CVA
pricing, exposure management, and regulatory requirements. The following is a summary of the objectives to
provide context for the data and technology discussions.
CVA Pricing
CVA is the amount banks charge their counterparties to compensate for the expected loss from default. Since
both counterparties can default, the net charge should theoretically be the bilateral CVA, which includes a debt
value adjustment (DVA) or gain from the bank’s own default. CVA pricing can be split into the inter-bank and
corporate customer markets. New legislation, including the Dodd-Frank Bill in the U.S. and the European Market
Infrastructure Regulations (EMIR), along with Basel III, are mandating or incentivising clearing and increased use
of collateral over CVA as the principal means for managing counterparty risk in the inter-bank market.
Uncollateralised exposure is more prevalent in the corporate derivatives market and banks compete
aggressively on CVA pricing. CVA pricing is inherently complex for two reasons. First, the incremental (or
marginal) CVA for each trade should reflect the application of collateral and netting agreements across all
transactions with that counterparty. Second, CVA pricing models not only need to incorporate all of the
risk factors of the underlying instrument, but also the counterparty’s ‘option’ to default and the correlation
between the default probability and the exposure, i.e., right- or wrong-way risk.
Given the complexity, two problems arise. Some banks are not able to compete for lucrative corporate
derivatives transactions because they do not take full advantage of collateral and netting agreements with their
counterparties in calculating CVA. Or, they win transactions because their models under-price some of the risks
and subject the bank to losses. The complexity is compounded by the need for derivatives salespeople to make
an executable price in near real-time.
Risk Management
While CVA covers the expected loss from counterparty defaults, there is also the risk of unexpected losses,
as well as mark-to-market gains and losses on the CVA. These risks are managed through a combination
of exposure limits, reserves and replication or hedging. Unexpected losses are calculated by simulating
exposures through time, taking into account netting and collateral agreements, and using the potential
future exposure (PFE) profile at a specified percentile, e.g. 99%, and the counterparty’s default probability to
calculate the unexpected loss or economic capital (EC), net of CVA.
Many banks strictly rely on reserves and exposure limits to manage these risks but the trend is towards
more active management. Hedging CVA has become increasingly important to offset significantly higher
regulatory capital requirements and to reduce the impact of CVA volatility on the bank’s earnings. The main
problem is that some of the risks can’t be effectively hedged. For example, there may be limited or no
liquid CDS referencing some of the counterparties and there are complex correlation and second-order risks
that can be difficult to quantify and hedge. The difficulties are amplified by the computational constraints in
running Monte Carlo simulations on the entire portfolio for each perturbed input.
Regulatory Requirements
Subject to approval, the Internal Model Method (IMM) specified in the Basel accord allows banks to use their
own models to calculate regulatory capital. The counterparty default risk charge is calculated using current
market data, either implied or calibrated from historical data. Banks using market-implied or risk-neutral
calibrations, appropriate for hedging, must also run the simulations with historical calibrations. Three-years
of historical data are required, including a period of stress to counterparty credit spreads. The data must be
updated quarterly or more frequently if warranted by market conditions. The counterparty default risk charge
is the greater of the charge based on current market data and the charge based on the stress calibration.
5. Basel III introduces a CVA risk capital charge, as CVA losses were greater than unexpected losses in many
cases during the recent crisis. The charge is the sum of the non-stressed and stressed CVA VaR, based
on changes in credit spreads over a three-year period. For the stressed CVA VaR, the three-year period
includes a one-year period of stress to counterparty credit spreads. Banks need not include securities
financing transactions (SFTs) in the CVA risk capital charge unless the risk is deemed material. Cleared
transactions are also omitted. However, eligible credit hedges should be included in all calculations. The
total regulatory capital charge for counterparty risk is the sum of the counterparty default risk charge and
CVA risk capital charge.
Regulatory approval is contingent on model validation. Back-testing of representative portfolios over several
historical dates covering a wide range of market conditions is the primary mechanism for validating the
capital model. Back-testing involves comparing exposure projections with realised exposures for selected
time buckets, e.g., one year. Banks must also perform stress tests on the principal market risk factors to
identify general wrong-way risks, concentrations of risks among industries or regions, and large directional,
basis and curve risks. Reverse stress testing to identify plausible loss scenarios may also be required.
Summary
The key objectives of the counterparty risk management process can be summarised as follows:
• Central storage of counterparty legal entity structures, including a history of corporate actions
and credit events
• Central storage of collateral & netting legal agreements by counterparty
• Ability to load all OTC derivatives and other counterparty transactions from various booking systems
• Collateral position management and integration with counterparty risk calculations
• Market data required to value counterparty transactions, plus market-implied and historical
volatilities and correlations for generating exposure distributions, and default probabilities for
calculating CVA, DVA and economic capital
• Near-time incremental CVA pricing of new trades, reflecting collateral & netting agreements
• CVA pricing models that incorporate all dimensions of risk, including right- and wrong-way risk
• Expected exposure (EE) and potential future exposure (PFE) profiles for counterparty limit
monitoring on a daily basis
• CVA, DVA and economic capital calculations for expected and unexpected loss management
• CVA sensitivities across all risk factors for actively managing counterparty risk
• Credit hedges reflected in exposure and loss metrics, and market hedges reflected in CVA sensitivities
• Regulatory counterparty risk capital calculation using market-implied and historical calibrations,
including a stress calibration
• Historical CVA VaR using the recent three-year period and a stress period
• Back-testing of representative portfolios over multiple time periods to validate exposure projection model
• Stress testing to identify wrong-way risks and concentrations
• Reporting of all counterparty risk metrics minimally by counterparty, industry, and region with
diagnostic drill-down capabilities
6. Data
Based on the objectives, data requirements can be segregated into reference data, market data,
counterparty transactions and collateral positions.
Reference Data
Reference data generally means detailed terms and conditions for securities and information on legal entities,
including historical ratings, corporate actions and credit events. For counterparty risk purposes, the scope
expands to netting and collateral agreements, through master agreements and credit support annexes (CSAs).
There may be additional netting agreements to facilitate cross-product netting across master agreements.
Collateral agreements impose a cap on counterparty exposure by collateralising the exposure above a
specified threshold. For exchange-traded and cleared transactions, the threshold is effectively zero. The
threshold(s) may be unilateral or bilateral and may adjust according to the counterparty’s rating or other
trigger. Collateral agreements also specify the frequency of collateral calls and the closeout period. The
closeout period, or ‘margin period of risk’, is the amount of time between requesting and receiving collateral
before default is assumed. The collateral agreement may also specify acceptable types of collateral and
corresponding haircuts.
Market Data
A fundamental counterparty risk measure is current exposure (CE), or the mark-to-market value of all
positions by counterparty, which may be netted and collateralised according to legal agreements stored
in the reference database described above. In order to calculate CE, all market data required by curve
calibrators and pricing models must be sourced. For a large trading book, this can mean quoted prices and
volatilities across interest rates, FX, commodities, equities and credit.
The market-implied volatilities used by pricing models to mark positions can also be inputs to the simulation
engine that generates market scenarios and the distribution of counterparty exposures through time. Risk
metrics such as EE and PFE are calculated from these exposure distributions. CVA, DVA and economic
capital also require counterparty default probabilities, derived from credit spreads or historical data.
Correlations between the counterparty’s default probability and each risk factor are needed to value right-
and wrong-way risk. Correlations between risk factors may also be needed to manage the dimensionality
of the simulations.
The Basel guidelines impose further requirements on market data. While current market-implied data may
be used for some calculations, Basel III forces banks to use historical volatilities and correlations, calibrated
over a three-year period, in generating exposure distributions. An additional three-year period that
includes a period of significant stress to counterparty credit spreads is required for the default risk capital
and CVA VaR calculations. The historical data set should also support back-testing of the exposure model.
Banks are expected to ensure that market data is scrubbed and verified, stored in a secure database, and
independent of business lines.
Transactions
In most cases, OTC derivatives account for the largest share of counterparty risk. Securities financing
transactions typically make up another significant portion of transactions but are often less risky due to shorter
maturities. Hedges such as CDS and other credit derivatives should also be included. While only specific credit
hedges are recognised for regulatory capital relief, other products referencing underlying market risk factors
may be used to hedge CVA sensitivities.
7. OTC transactions pose the biggest challenge. For most banks, the vast majority are interest rate and FX
derivatives. OTC derivatives may contain customised cash flows and/or exotic payoffs, which must be
communicated to the counterparty risk system. Some of the economic terms and conditions may also come
from the reference database.
Counterparty collateral positions are also important. Most collateral is cash and highly rated securities,
although the increased emphasis on collateral funding is causing banks to explore other forms. Ideally,
the counterparty risk system would revalue the collateral along with the transactions in calculating net
counterparty exposure in order to reflect mark-to-market risk of the collateral.
• Securities • IR, FX, commodities
• Legal entities & equities prices
• Ratings • Credit spreads (PDs)
• Corporate actions • Market volatilities
• Credit events • Historical voltilities
• Master agreements • Correlations
& CSAs • Historical data
• Cross-product
netting agreements Reference Market
Data Data
Transactions Collateral
• OTC derivatives • Cash
• Securities Financing • Securities
Transactions • New types
• Credit hedges • Haircuts
• CVA market hedges • Funding
8. Technology
The counterparty risk management objectives and corresponding data requirements summarised in the
previous two sections translate into a very challenging technology agenda. Data management is certainly
a top priority, as well as robust CVA pricing and risk analytics. Centralising counterparty risk management
for the entire trading book places a high priority on scalability, while providing near-time performance
for marginal pricing of new trades. Given the large amount of information, a configurable reporting
environment is a necessity. For regulatory approval, the infrastructure must also support back-testing,
stress testing and VaR. Each of these issues is addressed below.
Data Management
Most banks have multiple systems for reference data, market data, and transactions, which may be different
for each business unit. These systems may be further sub-divided into front-office analytical tools and back-
office booking systems, each with its own repository of reference data. There may also be several internal and
external sources for market data. The counterparty risk system must integrate with potentially many of these
systems in order to extract the data needed to produce a comprehensive set of counterparty risk metrics.
Systems integration presents several technical challenges. Source systems may be built with a variety of
technologies, such as C++, Java, and .NET, which means the counterparty risk system may have to ‘speak’
many languages. Some systems may have well-developed interfaces and APIs while others may not, which not
only adds to the cost of development but also increases the failure rate in terms of missing or erroneous data.
Related issues include symbology mapping and data conversion. Source systems may use their own
symbols to identify securities and other parameters, such as holiday codes and reference indices. Use of
industry standard ticker symbols lessens the problem but invariably, the counterparty risk system must be
able to interpret a variety of naming conventions. Source systems also store data in proprietary formats.
While loading the data, the counterparty risk system must convert it into its own format. Expanded use
of open XML formats, such as FIXML and FpML, as more products become standardised for clearing, is a
positive development.
Once the systems have been connected and data translations in place, the counterparty risk system must
receive incremental updates from the upstream systems. Daily updates are sufficient for most purposes but
incremental CVA pricing depends on up to date position and market data.
Analytics
Analytical components can be categorised into pricing models, the simulation engine, and market data
calibrators. Most institutions have their own proprietary libraries of pricing models for front-office pricing and
hedging. Ideally, the bank would use the same pricing models for counterparty risk to ensure consistency.
Market data calibrators provide the market-implied and historical volatilities and correlations as inputs to the
simulation engine.
The simulation engine uses pricing models in generating the exposure distribution over a specified set of
future dates. The first step is to simulate market scenarios using a risk neutral or historical calibration. The next
step is to value each transaction over all scenarios and time steps to create the exposure distribution. The
exposures are aggregated by counterparty ‘netting set’, according to legal netting agreements. Collateral
terms are then applied to determine uncollateralised exposure by netting set.
9. In applying collateral thresholds, the ‘margin period of risk’ and collateral mark-to-market risk must be
considered. EE, PFE and various Basel metrics including expected positive exposure (EPE) can be directly
calculated from the exposure distribution. The final step is to ‘integrate’ the exposure distribution to calculate
CVA, DVA and economic capital. Incorporating right- and wrong-way risk into these calculations is important,
especially for credit derivative transactions, and non-trivial to implement.
Several analytical issues arise in performing the above steps. First and foremost, pricing models must be
able to perform forward valuations for the simulated market scenarios. Early termination or mutual ‘put’
features should be reflected. For path-dependent and other exotic payoffs, the simulation engine should
provide sufficient path-level information to the pricing model to prevent valuation inconsistencies (and
performance bottlenecks) from having to run simulations within the simulation.
In order for pricing models to re-use market scenarios or paths, the simulation engine should use risk neutral
calibrations of the various risk factors. For example, if pricing models use a Libor market model, e.g., BGM,
to price interest rate derivatives, the simulation engine should use the same model, which means calibrating
forward rates, volatities and correlations from current market data. This calibration can also be used to
calculate CVA sensitivities for hedging purposes. The simulation engine must also support calibrations
based on historical data to calculate the various metrics required by Basel.
Performance & Scalability
For a large bank, the counterparty risk system may price something on the order of one million transactions
over one thousand scenarios and one hundred time steps, or 100 billion valuations. If the bank actively
hedges CVA, the number of valuations is roughly multiplied that by the number of sensitivities required.
Pricing models must be as efficient as possible in order to generate counterparty risk metrics on a daily
basis. Since banks prefer to use their own pricing models for consistency, additional tuning or substitution
of less sophisticated models for valuation of complex products, at the expense of some accuracy, may be
necessary. Another approach is to use pre-calculated pricing grids to reduce the number of valuations.
Scalability can also be addressed through parallel processing. Distributing computations across servers and
processor cores using grid technology and multi-threading should allow acceptable levels of performance
to be achieved by adding hardware resources. Of course, complexity and potential for failure increases with
the amount of hardware.
Re-running simulations on the entire portfolio to determine the impact (marginal price) of a new transaction
is not practical for intra-day pricing. The typical solution involves saving valuations at the netting set level for
each scenario and time step in the database. Marginal pricing is then reduced to simulating the new trade
along the same scenarios and time steps and aggregating with the saved netting set valuations, re-applying
netting and collateral agreements. Of course, adjustments or re-simulation may be necessary for changes in
portfolio composition or significant market moves.
10. Reporting
With the huge amount of data involved and analytical complexity, the ability to view the various counterparty
risk metrics across a variety of dimensions is absolutely essential. At the very least, the system should show
CE, EE, PFE, CVA, DVA and economic capital by counterparty, industry and region. The system should also
display EE and PFE profiles along specified future time buckets out to the maximum maturity date. The ability
to inspect reference, market and transaction data inputs is vital in verifying calculated results and tracking
down errors. The system must also provide reports for back-testing, stress testing and VaR outputs with similar
aggregation and drill-down capabilities.
Back-testing, Stress Testing & VaR
The counterparty risk system’s infrastructure must provide back-testing, stress testing and full-reval historical
VaR. For back-testing, the system should record all data necessary to simulate exposures for representative
portfolios over multiple historical dates. Stess testing functionality should support configuration of a set of shifts
to any or all of the current market data used to value the portfolio. Flexibility to ‘steepen’ or ‘flatten’ curves
must be provided, as well as shifting basis spreads. VaR is similar to stress testing in that it involves a set of
shifts. The shifts are derived from a time series of historical market data, typically daily. In some institutions,
the market risk group provides a set of shift ‘files’ that the risk system should load to run VaR and stress tests.
Analytics Reporting
• CE, EE, PFE, CVA, EC
• Large cross-asset • Back-test exposure
by counterparty,
• Multiple data sources • Simulation engine portfolios projections vs.
industry & region
• Systems integration • Risk-neutral & historical • Pricing model Realizations
• EE & PFE profiles
• Symbology mapping calibrations performance • Curve steepening
over time
• Data format • Netting & collateral • Near-time CVA pricing & flattening stress
• Data inspection &
conversions • Right/wrong-way risk • CVA sensitivities scenarios
drill-downs
• Incremental updates • Exotic payoffs • Daily and intra-day • Stress basis spreads
• Back-testing, stress
metrics • Full reval historical VaR
testing, VaR outputs
Performance & Back-testing, Stress
Data Management Scalability Testing & VAR
11. Trends
Post crisis, the ability for senior management to get a comprehensive view of the bank’s counterparty risks
is a critical priority. Consolidated risk reporting has been elusive due to front-office driven business models.
As influential revenue producers, trading desks have maintained a tight grip on data ownership, model
development and front-office technology. This has resulted in a proliferation of systems, making the job of
aggregating risks across business lines excessively complicated. Continuous development of new types of
derivative payoffs and structured products has exacerbated the problem. But the failures and near failures of
several global banks have changed the traditional mentality. Banks are now taking a ‘top-down’ approach to
risk management. Decision-making authority is transitioning from the front-office to central market and credit
risk management groups. This authority includes tighter controls on data and technology.
A key component of the top-down approach to risk management is the central CVA desk or counterparty
risk group. This group is responsible for marginal CVA pricing of new trades originated by the individual
business units and then managing the resultant credit risk. In practice, the CVA desk sells credit protection
to the originating trading desk, insuring them against losses in the event of a counterparty default. There
are several advantages to this approach. Housing counterparty risk in one place allows senior management
to get a consolidated picture of the exposures and proactively address risk concentrations and other
issues. It also addresses the competitive CVA pricing issues described in a previous section. As banks
continue to ramp up active management of CVA, having a specialised group allows careful management of
complex risks arising from liquidity, correlation and analytical limitations.
The reasons for not creating a central CVA desk or counterparty risk group tend to be practical issues
particular to the institution. Decentralised infrastructures may make the data and technology challenges
too great to ensure provision of meaningful consolidated counterparty risk metrics on a timely basis. Some
banks have aligned counterparty risk management by business line in order to more effectively manage
the data and analytical issues at the expense of certain benefits, like netting. For centralised CVA desks,
there is also the challenge of internal pricing and P&L policies. Most banks position CVA desks as utility
functions that simply attempt to recover hedging costs in CVA pricing.
Recent regulatory activity has also had a profound impact on counterparty risk management, mostly due
to central clearing requirements and higher capital ratios. Mandating central clearing for an expanding
scope of derivative products effectively moves counterparty risk out of complex CVA and economic capital
models and into more deterministic and transparent margining formulas. The heavily collateralised inter-
dealer market is also undergoing significant changes due to the widening of Libor basis spreads during the
crisis. A new standard for pricing collateralised trades is emerging, based on OIS discounting. Institutions
are now looking more closely at optimising collateral funding through cheapest-to-deliver collateral, re-
couponing existing trades to release collateral, and moving positions to central counterparties in order to
access valuation discrepancies or more favorable collateral terms.
It is expected that most corporate derivatives transactions will remain exempt from clearing mandates since
banks provide valuable hedging services in the form of derivative lines. The cost of extending these lines is
increasing due to significantly higher regulatory capital requirements. Therefore, competitive CVA pricing and
economic capital optimisation will remain priorities for corporate counterparty risk management alongside
collateral and clearing processes.
12. ABOUT QUANTIFI
Quantifi is a leading provider of analytics, trading and risk management software for the Global Capital Markets. Our
suite of integrated pre and post-trade solutions allow market participants to better value, trade and risk manage their
exposures and respond more effectively to changing market conditions.
Founded in 2002, Quantifi has over 120 top-tier clients including five of the six largest global banks, two of the three
largest asset managers, leading hedge funds, insurance companies, pension funds and other financial institutions across
15 countries.
Renowned for our client focus, depth of experience and commitment to innovation, Quantifi is consistently first-to-
market with intuitive, award-winning solutions.
For further information, please visit www.quantifisolutions.com
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