Accelerating Portfolio Level Analysis with Third Party AnalyticsA technology perspective<br />PRESENTERS:<br />Dr. Marc Vl...
Agenda<br />2<br />Enabling your system for portfolio level analysis:<br /><ul><li>What is portfolio level analysis?
What makes a good portfolio analysis solution?
Characteristics of an effective analytics library
Technical demonstration
Q&A</li></li></ul><li>About the speakers<br /><ul><li>Holds a PhD in fluid dynamics, an MBA, and other degrees, from Unive...
11 years of quant experience, in a variety of asset classes</li></ul>Dr. Tony Webb<br />Director, Analytics<br /><ul><li>H...
15 years of industry experience at UBS, Merrill Lynch, CréditAgricole Indosuez, Commerzbank, West LB, and Nomura Research ...
“Accelerating portfolio level analysis with third party analytics”<br />How can you efficiently produce a portfolio analys...
What is portfolio level analysis?<br />Any calculation that needs to be done at the portfolio level – some function f(P)<b...
Examples of portfolio level analysis<br />Total value<br />Aggregated cash flows<br />Risk sensitivity / DV01 / Hedge fact...
What makes a good portfolio analysis solution?<br />7<br />Improve<br />business decisions<br />Adapt to<br />market condi...
What makes a good portfolio analysis solution? cont.<br />Enable better business decisions<br />Complete visibility<br />F...
Adapt to changes in market conditions<br />Flexibility<br />Ease of integration<br />Extensibility<br />9<br />What makes ...
Cost effectiveness<br />Building a best of breed solution out of best of breed components<br />Get to market faster<br />R...
Risk Analysis<br />What-if and sensitivity analysis<br />Reusable models<br />Scenario analysis / rapid simulations<br />S...
The technology required for an effective solution<br />12<br />Portfolio Manager <br />Reporting Tools<br />Market Data<br...
Cost of in-house analytics development <br />   Source: Celent 2010<br />13<br />
POLL<br />14<br />
Characteristics of an effective analytics library<br />An analytics library for portfolio solutions must:<br />Offer power...
Characteristics of an effective analytics library cont.<br />1. Powerful portfolio specification<br />Portfolio as a coher...
2. Portfolio level analysis provision<br />Example: Risk sensitivity for a swap portfolio<br />Characteristics of an effec...
USD 3m LIBOR, quarterly<br />Party A<br />Party B<br />Swap 1<br />Example<br />USD 8mio notional<br />Matures To +5y<br /...
Example<br />19<br />
USD 3m LIBOR, quarterly<br />USD 3m LIBOR, quarterly<br />Party A<br />Party B<br />Swap 1<br />Party A<br />Party B<br />...
Example<br />21<br />
Example<br />22<br />
Example<br />23<br />
Characteristics of an effective analytics library cont.<br />2. Portfolio level analysis provision<br />	Example: Risk sen...
Characteristics of an effective analytics library cont.<br />3. Decoupled trades and models:<br />Decoupling of trade/port...
Characteristics of an effective analytics library cont.<br />3. Decoupled trades and models:An object oriented architectur...
Characteristics of an effective analytics library cont.<br />+<br />Model<br />Product/Trade<br />Market Models<br />Black...
Characteristics of an effective analytics library cont.<br />Method<br />+<br />+<br />Model<br />Product/Trade<br />Marke...
Characteristics of an effective analytics library cont.<br />Method<br />+<br />+<br />Model<br />Product/Trade<br />Marke...
Characteristics of an effective analytics library cont.<br />4. Object re-use<br />Model calibration  <br />Curves<br />Ma...
Characteristics of an effective analytics library cont.<br />5. IT considerations<br />Platform support (deploy anywhere)<...
A worked example<br />Building a simple desktop portfolio analysis tool that illustrates:<br />Powerful portfolio specific...
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Accelerating Portfolio Level Analysis with Third Party Analytics : A technology perspective

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Dr. Tony Webb, Director Analytics and Dr. Marc Vlitos, Product Manager Enterprise Solutions on February 24th, 2011 discuss how innovations in third party analytics technology are enabling the deployment of highly efficient solutions for portfolio level valuation and risk analytics for even the most complex, sizable or dynamic portfolios.

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  • Carole’s slideHello and welcome to our webinar on “Accelerating Portfolio Level Analysis with Third Party Analytics: A Technology Perspective”. I’d like to thank you for joining us. My name is Carole Jantzen and I will be moderating today’s session. Before we begin, I’d like to cover a few housekeeping items:Today’s webinar will last approximately 60 minutes. If you have any questions during the presentation, feel free to type them into the “Question” box on the GoToWebinar toolbar on the right-hand side of your screen. We have set aside 10 minutes for Q&amp;A following the presentation and will try to answer all your questions at that time.We will also be recording today’s session. You will receive a link to that recording in a follow-up email.So now let’s look at today’s Agenda….
  • Carole’s slideEnabling your system for portfolio level analysis:What is Portfolio level analysis?What makes a good Portfolio Analysis solution?A portfolio analysis system should:- support better business decisions, - be adaptable to changing market conditions, - be cost effective, and provide risk analysis.What are the characteristics of an effective analytics library?- Powerful portfolio specification- Provide portfolio-level analysis- Decouple the concepts of trades and models- Reuse objects, and - satisfy IT considerationsTechnical demonstrationAddressing emerging business opportunities (end-use benefit)Organizational consistency (end user benefit)Implementation effectiveness … Maintain, extend and scale[Client Case Studies – Clifton? Aberdeen?] i.e. FAS customers to illustrate challenges Q&amp;A
  • Carole’s slideI’m pleased to introduce our speakers for today’s session….Dr. Tony Webb is FINCAD’s Director of Analytics. He has more than a decade of quant experience and holds anMBA with a specialization in finance and derivatives and an PhD in computational fluid dynamics. Dr. Marc Vlitos is FINCAD’s Product Manager of Enterprise Solutions. He joined FINCAD in 2008 following 15 years of industry experience with various financial institutions in London. Marc holds a PhD from King’s College in London.---------------------------New innovations in third party analytics technology enable solutions for even the most complex, sizable or changing portfolios to be built and managed efficiently.In today’s webinar, Dr. Tony Webb and Dr. Marc Vlitos with provide their insight into the technology required for effective portfolio analysis. Utilizing a technical demonstration they will review best practices in calculating portfolio level metrics such as hedging factors, CVA, PFE, DV01, and aggregate risk, and will show how you can develop portfolio systems that are easy to maintain, extend and scale.And now let me turn the webinar over to Tony.
  • Thanks Carole.The title of our webinar is “Accelerating Portfolio Level Analysis with Third Party Analytics”What we are talking about in this webinar is how you can efficiently produce a portfolio analysis solution that is easy to maintain, extend and scale. Such as solution will, by its very nature, carry out portfolio-level analysis internally.
  • What do we mean by Portfolio Level Analysis? What we mean - is any calculation that needs to be done at the portfolio level, as opposed to, for example, calculations done at the trade level.This distinction is important when the aggregation is not trivial.For example, aggregating the value is easily accomplished by summing the value of the individual positions in the portfolio – to calculate the value of the portfolio you simply need to iterate through the positions within the portfolio, value each one, and sum them. In other words f(P) equals the sum of the individual f(Ti).But as you will see on the next slide there are some aggregations that are not so simple, and its not necessarily true that f(P) can easily be calculated from the individual values of f(Ti). It all depends on the function ‘f’.
  • Here are some other examples of analysis carried out at the portfolio level. In other words, these are different types of the function f.Total value we have already discussed.Aggregated cash flows – you might need all the future cash flows for a portfolio sorted in chronological order. Again, this aggregation is not difficult if the cash flows for each trade were available; it involves marshalling the data and applying a sort algorithm. However, it’s a common operation that would be best delegated to a single implementation.So far, the aggregations have not been complicated, and do not require sophisticated calculations at the portfolio level as long as it exists at the trade level. But as we proceed down the list, the aggregations are not so simple, and most require information about the risk factors that are shared by multiple positions within the portfolio.Risk sensitivity – this is the rate of change of the value of the portfolio with respect to the market data. Being able to calculate this first order sensitivity is a vital component of any risk system. For example, it can be used to calculate DV01, which is the sensitivity of the portfolio value to a small shift in interest rates – a parallel shift of the whole yield curve. It can also be used to calculate hedge factors, which are the positions in liquid vanilla instruments that would be required to add to the portfolio to provide an instantaneous hedge of all market and credit risk.Stress testing – this involves revaluing the portfolio under a number of different scenarios for the market data, to quantify the exposure to more extreme market movements than are captured using first order risk sensitivity. Credit Value Adjustment and Potential Future Exposure are calculations performed on a set of trades with the same counterparty – CVA is an adjustment to the value of a portfolio to reflect the credit risk of the counterparty; PFE is a calculation of the maximum possible loss that would be realised if the counterparty were to default, for a given confidence level such as 95%. The final example, Value at Risk is the maximum possible loss over a given time horizon that is suffered as a result of market fluctuations, again for a given confidence level. It is a measure of market risk, whereas PFE is a measure of counterparty credit risk.There are many other examples of Portfolio level Analysis.
  • So what makes a good portfolio analysis solution?At a high level a portfolio analysis system should :support better business decisions based on timely and accurate forecasts, even when historical data is scarce provide the flexibility to rapidly assess the impact on the user’s business resulting from changing market conditions be cost effective for both you (the developer) and your customers (the users), and provide risk analysis - this is certainly true for risk management systems, but even for solutions whose primary purpose is not risk management it is often that case that risk parameters are required.
  • Better business decisions are only possible when up to date and timely information is available. The challenge is to provide tools that help your customers, or your customers’ customers, gather the knowledge and insight needed to make more informed decisions.The ability to evaluate all possible outcomes, even if your customer only needs or wishes to analyze a small subset, is a key capability for any portfolio analysis solution. Decision makers need complete visibility into the inputs and analysis performed. All projected impacts on a portfolio should be fully quantified and available in multiple timeframes and, where appropriate, currencies. Any aggregated report should allow the user to drill down into the details of how the rolled-up values were derived. Transparency in how numbers are generated is critical in building confidence in the accuracy and reliability of those outputs.And finally, as I mentioned earlier, in order to make better business decisions, customers need timely reporting, often in real or near real time.
  • With modern portfolio solutions, it is now possible for you or your customers to create new derivative products that have been brought to market without a single line of new software being written. It is possible for those new instruments to be integrated into existing trade processing and portfolio analysis systems as soon as they have been created. This level of flexibility is unprecedented and was, only a few years ago, thought unattainable.  The advent of Service Oriented Architectures promised tomake business services broadly available and re-usable. The aim was to eliminate boundaries between business functions by making integration of the disparate and often legacy systems found in many customer sites possible. But a real-world SOA , not to forget the latest SOA-in-the-Cloud, can be challenging. Your customers systems often span departments and geographies. Your portfolio analysis solution must integrate data sources and applications , and provide visibility and business insight across many different platforms, both internally and externally. Not only does your system needto be fast, secure and always available, you also want your solution to be extensible and capable of coping easilythe inevitability of a continually changing landscape.
  • As a software developer it is often tempting to build everything from scratch. It is tempting to think that you can then tailor your solution to meet your specific needs, and it must be quicker that way. This thinking is usually proven to be wrong.Industry experience shows that you are more likely to build a best of breed solution by using best of breed components and concentrating your development effort on those areas where your company can deliver its unique expertise and value to your customers.In today’s more cost sensitive market you need to spend as little time in development as possible. Therefore, the careful choice of off-the-shelf components is critical to ensure that any solution you build is delivered in a timely and cost effective manner. The cost of a solution over its lifetime is often overlooked. It is almost always less expensive to buy components, particularly specialist components, instead of building your own. The further away from your core competency you are the more this makes sense both for you and your customers.
  • As I said before, even for solutions whose primary purpose is not risk management it is often that case that risk sensitivities are required, or that the portfolio must be revalued under various scenarios.If your aim is to make what-if and scenarios analysis straight forward, the ability to re-value a portfolioby simply selecting a different “model “is a critical capability. By the term “model”, here we mean an object that represents the set of market data, and mathematical model parameters, that describe a given scenario. You should be able to re-value the portfolio without having to change any other inputs other than the model object.The ability for your customers to cache,reuse and transform models of their financial world provides them with the means to perform rapid simulations of large portfolios without the need to reconstruct the model for each trade.Your applications need to be capable of meeting your customers’ performance needs. It does not matter if they have a small portfolio of simple vanilla products or a large mixed portfolio containing complex derivatives in multiple asset classes. Finally, in order to cope your solutions need to be configurable, grid enabled and computationally efficient.
  • Having talked about what makes a good portfolio analysis solution, lets quickly look at some of the components of such a solution.First you need data repositories – for datasets such as the Trades in the various portfolios, and for Market Data. There might be live market data as well as historical data, depending on the purpose of the solution.Next there are various Architectural components – such as computational servers, grids, web services, data services, and so on.There will be UI components – for example, what I have called a Portfolio Manager, to organise the trade and portfolio data, and Reporting Tools to present and organise the results of the calculations.The Analytics is last but not least – this piece is necessary, but all the other components are also vital for the whole solution. The Analytics is a small component from an architectural perspective, but a very high cost component if you build a one-off analytics library from scratch.
  • A recent report from the analyst firm Celentstates:“Our findings suggest that firms pursuing in-house efforts for derivativesanalytics require an upfront investment of at least $9 million.Moreover, depending on the “aggressiveness” of an institution, recurringannual costs can range between 25% and 50% of initial investmentto keep pricing and risk analytics relevant. This translates to $11 millionto $22 million over a five-year production lifecycle to enhance andkeep libraries current with ongoing market requirements across multipleasset classes. …. Aggregated over the total softwarelifecycle, firms adopting in-house strategies for OTC pricing willrequire investments between $25 million and $36 million alone tobuild, maintain, and enhance a complete derivatives library.”Now, you might argue with the specific numbers being suggested there, but we believe that they have the correct order of magnitude based on our in-house experience at FINCAD.We believe that it makes a lot more economic sense for this type of development effort to benefit many systems developers, not just one. That is FINCAD’s mission, to make complex finance affordable and widely available.So, as I said before, the analytics is a small component from an architectural perspective, but a very high cost component if you build a one-off analytics library from scratch.POLL
  • Carole’s slide- So far we’ve learned what portfolio level analysis is in the context of this webinar We’ve also learned ‘what makes a good portfolio analysis solution’This poll will help us gain a better understanding of why you are joining us today.Our question is:Which of the following poses the greatest challenge for you when building a portfolio analysis solution?Requirements specificationSystem designMarket dataAnalytics development ORAnalytics integrationWe’ll be closing the poll now……Tony
  • Having placed the analytics piece in the overall context of the various components of a portfolio solution, we will now drill down into the analytics library, as ask – what characteristics would enable a portfolio valuation or risk solution to be built efficiently and extended easily?We will be going into the characteristics in each of the 5 areas in the following slides.
  • There are several issues in the area of portfolio specification, which is all about defining the portfolio in terms of its constituent trades and positions. There are no modelling issues here, or any analytics; its just a question of how to represent the trade information on the deal sheets in a form that can be processed by the analytics engine.Firstly, its important to define a portfolio as a single coherent object; this makes it easy to implement calculations at the portfolio level, and operate on the portfolio as easily as if it were a single trade. Every calculation that can be performed at the trade level should be available at the portfolio level. It should be easy to form a portfolio from a collection of trades, or as a union of other portfolios, to an arbitrary level of recursion.It should be easy to support new types of trade within a portfolio system, and the analytics library should enable that. If the analytics library has a trade class, then by creating a instance of a trade with the new type, then all analyses should automatically be available – both at the trade level and also at the portfolio level. This is a very powerful feature, being able to leverage portfolio-level calculations that are available within the library, for portfolios that contain the new trade type. As new trade types are added to a portfolio system, the cost of adding support for an extra trade type should decrease for each new trade type that is added. These efficiency gains are possible by taking advantage of the trade infrastructure that would be initially built to support the generic trade class.I myself have been involved in projects to build systems for portfolio-level risk analysis and hedge accounting – ones where the effort and cost to add each new trade type was about the same for each trade-type; from the first one that was implemented, to the last one that was added. This is because the underlying analytics library was more trade-centric, and the market data and model data had to be organised externally to the library, sometimes differently for different trade types.Thirdly, it should be possible to represent any trade. For vanilla trades, there should be templates available to conveniently create them. On the other hand, there should be no restriction on the complexity of trades that can be supported – this can be achieved by using a Domain Specific language for trade representation for exotic trades.Finally, it must be possible for a portfolio to contain trades from more than one asset class. For example, it must be possible for a portfolio to contain any mixture of FX trades, Equity trades, Fixed Income, credit derivatives, commodity derivatives, and so on. Such mixed portfolios are common and require analysis.
  • Once the portfolio has been represented, we need to perform some analyses on it, such as calculate its value, its cashflows, its Potential Future Exposure, its Value-at-Risk, and so on.Its important that these portfolio-level analyses are available within the analytics library, and that the library is not constrained to perform trade-level calculations only. If the analytics library were only capable of performing trade-level calculations, then these aggregations need to be implemented externally to the library. This is inefficient, and sometimes impossible if information about the relationships between the trades is not available.For example lets look at the sensitivity of a swap portfolio.
  • The first swap in the portfolio is a 5 year swap, 8 million notional, where Party A is paying 3m libor quarterly and receiving 2.4% semiannually.This swap in terms of its risk exposure is almost entirely dependent on the 5-year swap rate, which is quoted in the swap markets.
  • Here is a graph of the value of that swap as a function of the 5y swap rate. The blue dot is the current value of the swap, for the current value of the 5y swap rateThe slope of that line at the blue dot is the sensitivity of the value to the 5y swap rate.
  • Now lets add a second swap to the portfolio, Swap 2. This swap is almost a perfect offset to Swap 1. However, the notional does not quite match, nor the maturity date or the fixed rate. In Swap 2, Party A is receiving fixed, in contrast to Swap 1 in which Party A is paying fixed. So Swap 2 is almost offsetting Swap 1 but not quite. This portfolio is very idealised just to get the point across.
  • The brown line is the sensitivity of the value of swap 2 to the 5y swap rate. Both swaps would also have some sensitivity to other parts of the yield curve, although to a much lesser extent.The value of the portfolio is given by the green dashed line. It is not quite horizontal because there is still some dependency on the 5y swap rate – the swaps are not exactly offsetting.But how is that that sensitivity to be calculated? The value of each swap, and the value of the portfolio, can be calculated for the current value of the market quote for the 5 y swap rate. That gives the position of the 3 dots. The question is, how to calculate the slope of the green dashed line as it passes through the green dot.
  • A common way of calculating the slope is by so-called “bumping” – you bump the 5y swap rate up rate by a small amount, and recalculate the portfolio value, giving the green dot to the right. You then bump the 5 y swap rate down by a small amount, and recalculate the portfolio value – giving the green dot to the left. The slope is then obtained by taking the difference in portfolio value and dividing by the small change in swap rate. Using the right-hand and left hand dots is more accurate than using a one-sided difference.This is a common approach, but not very efficient. It requires the value of the portfolio to be calculated 3 times altogether -- once in the middle to get the current value, and twice more to get the slope. This can be very computationally expensive, especially if model recalibration had been involved.
  • A more efficient analytics library would operate at the portfolio level – it would know the relationships between the trades within that portfolio and automatically be aware of which market data they were exposed to. For example, Swap 1 and Swap 2 both depend on the 5y swap rate, and not much on anything else.The library would be able to calculate that sensitivity analytically since it knows all the mathematical operations involved in pricing. In other words, it would be able to calculate the slopes on this chart using calculus, rather than by finite difference or bumping, and without the user having to specify which risk factors to bump.
  • We were talking about the importance of the availability of portfolio-level analyses within the analytics library, and we used first order risk sensitivity as an example. These ideas are particularly powerful when applied to more complex portfolios, whose pricing requires model calibration. The ability to propagate first order risk sensitivity to market data through the model calibration process generates huge efficiency savings, compared with bumping the market data and recalibrating the model.Also, if the portfolio-level analysis is performed within the analytics library, then the need for additional external infrastructure to build a portfolio system is reduced; infrastructure such as external mapping of sensitivities to pre-defined risk factors or curve points would potentially no longer be necessary.These ideas are also very important when applied to other types of analysis, such as CVA or VaR. These analyses are highly non-linear, and it is just not possible to derive their values for a portfolio simply by knowing their values for each constituent trade. For example, its not possible to calculate the CVA for a portfolio just by knowing the CVA for each individual trade in the portfolio since there may be offsetting positions.
  • Continuing with what characteristics of an analytics library would facilitate building a portfolio solution, the next area is the idea of decoupled trades and models.Separating the concepts of trade, model and solution method provides many advantages.For example, its possible to control “model risk” by substituting different models and solution methodologies.And if the analytics library has a trade or product class, then consistent calculation types will be available on all instruments, leading to more efficient solution implementations.Here is a picture of what I talking about…
  • This is an high-level view of a natural approach to separating the concepts.Starting with the Product (or Trade) class in the middle, this object represents the financial contract, or a set of trades in the case of a portfolio. This is information that is found on a Term Sheet, or collection of term sheets. This object knows nothing about quantitative finance.---------------------------next slideThe box on the left represents the model – and here I do not just mean a mathematical equationdescribing the dynamics of certain random variables – it also includes market data and the values of the parameters embedded in the definition of the mathematical models. So here the Model includes the market data as well as calibrated or bootstrapped parameter values, such as discount factor curves, implied rate curves, CDS spreads, default probability curves, and the covariance matrix for forward rates in the Libor Market Model.The grey box on the right represents the solution method – for example, closed form expressions such as the Black-Scholes formula, or Monte Carlo simulation, or backwards evolution (meaning approaches using trees or finite difference methods to solve partial differential equations). This object would also contain the data needed to run these algorithms, such as step-size for a grid, or the number of Monte Carlo paths to be simulated. Not all solution methods would necessarily be applicable for all combinations of trade and model. For example, for a vanilla European option in the Black-Scholes model, many solution methods would be available, such as closed form, Monte Carlo, or backward evolution on a finite difference grid or on a tree. However, for exotic trades, or for complex models, only Monte Carlo would be available.Once these 3 objects have been defined, and an output request has been formed, it is then just a question of making a call to a single universal function that can process those objects with a single interface.This approach has many advantages. For example, as I said its possible to assess “model risk” by substituting different models and solution methodologies. For the same product/trade object, or portfolio object in the middle, different instances of model and solution method could easily be substituted, and the effect on the value could be quantified.
  • The box on the left represents the model – and here I do not just mean a mathematical equationdescribing the dynamics of certain random variables – it also includes market data and the values of the parameters embedded in the definition of the mathematical models. So here the Model includes the market data as well as calibrated or bootstrapped parameter values, such as discount factor curves, implied rate curves, CDS spreads, default probability curves, and the covariance matrix for forward rates in the Libor Market Model.----------------------------Next slideThe grey box on the right represents the solution method – for example, closed form expressions such as the Black-Scholes formula, or Monte Carlo simulation, or backwards evolution (meaning approaches using trees or finite difference methods to solve partial differential equations). This object would also contain the data needed to run these algorithms, such as step-size for a grid, or the number of Monte Carlo paths to be simulated. Not all solution methods would necessarily be applicable for all combinations of trade and model. For example, for a vanilla European option in the Black-Scholes model, many solution methods would be available, such as closed form, Monte Carlo, or backward evolution on a finite difference grid or on a tree. However, for exotic trades, or for complex models, only Monte Carlo would be available.Once these 3 objects have been defined, and an output request has been formed, it is then just a question of making a call to a single universal function that can process those objects with a single interface.This approach has many advantages. For example, as I said its possible to assess “model risk” by substituting different models and solution methodologies. For the same product/trade object, or portfolio object in the middle, different instances of model and solution method could easily be substituted, and the effect on the value could be quantified.
  • The grey box on the right represents the solution method – for example, closed form expressions such as the Black-Scholes formula, or Monte Carlo simulation, or backwards evolution (meaning approaches using trees or finite difference methods to solve partial differential equations). This object would also contain the data needed to run these algorithms, such as step-size for a grid, or the number of Monte Carlo paths to be simulated. Not all solution methods would necessarily be applicable for all combinations of trade and model. For example, for a vanilla European option in the Black-Scholes model, many solution methods would be available, such as closed form, Monte Carlo, or backward evolution on a finite difference grid or on a tree. However, for exotic trades, or for complex models, only Monte Carlo would be available.----------------------------------Next slideOnce these 3 objects have been defined, and an output request has been formed, it is then just a question of making a call to a single universal function that can process those objects with a single interface.This approach has many advantages. For example, as I said its possible to assess “model risk” by substituting different models and solution methodologies. For the same product/trade object, or portfolio object in the middle, different instances of model and solution method could easily be substituted, and the effect on the value could be quantified.
  • Once these 3 objects have been defined, and an output request has been formed, it is then just a question of making a call to a single universal function that can process those objects with a single interface.This approach has many advantages. For example, as I said its possible to assess “model risk” by substituting different models and solution methodologies. For the same product/trade object, or portfolio object in the middle, different instances of model and solution method could easily be substituted, and the effect on the value could be quantified.
  • Continuing with the analytics library characteristics, an area related to the previous slide is that of object re-use.For example, since the Model object contains the calibrated parameters and market data, then it can be cached and reused for various different portfolios or types of output request. This can represent a huge savings in computational speed, since model calibration is a very computationally intensive step. If new market data became available, it would only be necessary to recalibrate the affected components of the model.Other classes that would benefit greatly from caching and re-use would be curves, market conventions, and portfolio definitions.For example, stress testing a portfolio would be accomplished by setting up the portfolio object once, and revaluing it under several different scenarios, each of which would be represented by a different instance of a model.
  • The final set of considerations for the analytics library is in the area of IT.No matter how powerful your analytics platform is, in order to be useful to you and your customers, it must be capable of being deployed into a production environment.Supporting multiple operating systems and deployment options is critical, allowing you to build flexible applications that can be used in your customers IT infrastructure, regardless of what that might be.You must be able to integrate it into your solutions quickly and easily, while providing a long term and supportable solution that minimizes the effort and cost required to add new trade and model constructs to you software.Having discussed the attributes of portfolio analysis solutions, and of the analytics libraries used to build them, I will now hand it over to Marc, who will illustrate these ideas with a worked example.
  • Thank you Tony…In today&apos;s demonstration I will illustrate how the use of a powerful modern analytics platform can rapidly integrated into a desktop portfolio analysis tool and in doing so illustrate the power features that make portfolio specification and analysis straight forward… as well as showing the benefits of decoupling trade and model specifications..
  • I will build the GUI components of my application using Java Swing and will use FINCAD’s new F3 SDK analytics platform to provide the financial calculations needed. I am by no means restricted to using Java for this application, I could have chosen to build my application in C# .NET or C++, the effort required to integrate F3 would have remained more or less the same.
  • In this short demonstration I hope that I have been able to give you a flavor how using a modern, high flexible analytics platform can accelerate your delivery of a portfolio management solution and in doing so have illustrated the benefits of having a library that provides:Powerful portfolio specificationCombined with provision portfolio-level analysis Having fully decoupled trade and model representation And how that lends itself to object reuse andThe flexibility that provides you with future proofingAt this point I will hand it over to Carole, who take us through our Q&amp;A session
  • In conclusion......As you’ve seen from Marc’s short demo, by utilizing an analytics library that met the criteria outlined earlier in this webinar, he was able to efficiently develop a small yet highly flexible portfolio analysis solution.This solution, by design will be easy to maintain, extend and scale for both simple and complex, sizable or dynamic portfolios.
  • Carole’s slideI’d now like to open the floor for your questions. If you have a question, please maximize your GoToWebinar toolbar and type your question in the question box. We’ll try to get to all your questions in the time we have remaining. Thank you very much for joining us today.
  • Carole’s slideWe’ve reached then end of our webinar. Thank you for joining us.Please note the contact details on your screen if you have any further questions and be sure to watch your email inbox over the next couple of days for a note giving you access to the webinar recording.Again, thank you and we wish you all a good day.
  • Transcript of "Accelerating Portfolio Level Analysis with Third Party Analytics : A technology perspective"

    1. 1. Accelerating Portfolio Level Analysis with Third Party AnalyticsA technology perspective<br />PRESENTERS:<br />Dr. Marc Vlitos<br />Product Manager, Enterprise Solutions<br />FINCAD<br />Dr. Tony Webb<br />Director, Analytics<br />FINCAD<br />Moderator:<br />Carole Jantzen, Strategic Marketing Manager FINCAD Alliance Program<br />
    2. 2. Agenda<br />2<br />Enabling your system for portfolio level analysis:<br /><ul><li>What is portfolio level analysis?
    3. 3. What makes a good portfolio analysis solution?
    4. 4. Characteristics of an effective analytics library
    5. 5. Technical demonstration
    6. 6. Q&A</li></li></ul><li>About the speakers<br /><ul><li>Holds a PhD in fluid dynamics, an MBA, and other degrees, from University of British Columbia, University of Wales and Cambridge University
    7. 7. 11 years of quant experience, in a variety of asset classes</li></ul>Dr. Tony Webb<br />Director, Analytics<br /><ul><li>Holds a PhD from King’s College, London
    8. 8. 15 years of industry experience at UBS, Merrill Lynch, CréditAgricole Indosuez, Commerzbank, West LB, and Nomura Research Institute</li></ul>Dr. Marc Vlitos<br />Product Manager, Enterprise Solutions<br />3<br />
    9. 9. “Accelerating portfolio level analysis with third party analytics”<br />How can you efficiently produce a portfolio analysis solution that is easy to maintain, extend and scale?<br />4<br />
    10. 10. What is portfolio level analysis?<br />Any calculation that needs to be done at the portfolio level – some function f(P)<br />In contrast with trade-level calculations - f(Ti)<br />Its not necessarily true thatf(P) can be calculated from values of f(Ti)<br />Portfolio P<br />Trade T1<br />Trade T2<br />Trade T3<br />Trade T4<br />Trade T5<br />Trade T6<br />5<br />
    11. 11. Examples of portfolio level analysis<br />Total value<br />Aggregated cash flows<br />Risk sensitivity / DV01 / Hedge factors<br />Stress testing<br />Credit Value Adjustment (CVA)<br />Potential Future Exposure (PFE)<br />Value at Risk<br />6<br />
    12. 12. What makes a good portfolio analysis solution?<br />7<br />Improve<br />business decisions<br />Adapt to<br />market conditions<br />Analyse &<br />manage risk<br />Portfolio Analysis<br />Solution<br />Control<br />solution costs<br />
    13. 13. What makes a good portfolio analysis solution? cont.<br />Enable better business decisions<br />Complete visibility<br />Fully quantified impact analysis<br />Multiple time frames and currencies<br />Real time results<br />8<br />Improve<br />business decisions<br />Adapt to<br />market conditions<br />Analyse &<br />manage risk<br />Portfolio Analysis<br />Solution<br />Control<br />solution costs<br />
    14. 14. Adapt to changes in market conditions<br />Flexibility<br />Ease of integration<br />Extensibility<br />9<br />What makes a good portfolio analysis solution? cont.<br />Improve<br />business decisions<br />Adapt to<br />market conditions<br />Analyse &<br />manage risk<br />Portfolio Analysis<br />Solution<br />Control<br />solution costs<br />
    15. 15. Cost effectiveness<br />Building a best of breed solution out of best of breed components<br />Get to market faster<br />Reduce the total cost of ownership<br />10<br />What makes a good portfolio analysis solution? cont.<br />Improve<br />business decisions<br />Adapt to<br />market conditions<br />Analyse &<br />manage risk<br />Portfolio Analysis<br />Solution<br />Control<br />solution costs<br />
    16. 16. Risk Analysis<br />What-if and sensitivity analysis<br />Reusable models<br />Scenario analysis / rapid simulations<br />Scalability<br />11<br />What makes a good portfolio analysis solution? cont.<br />Improve<br />business decisions<br />Analyse &<br />manage risk<br />Adapt to<br />market conditions<br />Portfolio Analysis<br />Solution<br />Control<br />solution costs<br />
    17. 17. The technology required for an effective solution<br />12<br />Portfolio Manager <br />Reporting Tools<br />Market Data<br />Analytics Library<br />Trade/portfolio<br /> Data<br />Market Data Feeds<br />
    18. 18. Cost of in-house analytics development <br /> Source: Celent 2010<br />13<br />
    19. 19. POLL<br />14<br />
    20. 20. Characteristics of an effective analytics library<br />An analytics library for portfolio solutions must:<br />Offer powerful portfolio specification<br />Provide portfolio-level analysis<br />Decouple trades and models<br />Re-use objects<br />Meet IT criteria<br />Portfolio Manager <br />Reporting Tools<br />Analytics Library<br />Market Data<br />Trade/portfolio<br /> Data<br />Market Data Feeds<br />15<br />
    21. 21. Characteristics of an effective analytics library cont.<br />1. Powerful portfolio specification<br />Portfolio as a coherent object<br />Easy to add new trade types<br />Handle any trade type – vanilla or exotic<br />Trade representation language for exotics<br />Cross asset class portfolios<br />Trade T1<br />Trade T2<br />Portfolio P<br />Trade T3<br />Trade T4<br />Trade T5<br />Trade T6<br />16<br />
    22. 22. 2. Portfolio level analysis provision<br />Example: Risk sensitivity for a swap portfolio<br />Characteristics of an effective analytics library cont.<br />17<br />
    23. 23. USD 3m LIBOR, quarterly<br />Party A<br />Party B<br />Swap 1<br />Example<br />USD 8mio notional<br />Matures To +5y<br />2.4% semi-annually<br />18<br />
    24. 24. Example<br />19<br />
    25. 25. USD 3m LIBOR, quarterly<br />USD 3m LIBOR, quarterly<br />Party A<br />Party B<br />Swap 1<br />Party A<br />Party B<br />Swap 2<br />Example<br />USD 8mio notional<br />Matures To +5y<br />USD 9mio notional<br />Matures To +5y1m<br />2.4% semi-annually<br />2.6% semi-annually<br />20<br />
    26. 26. Example<br />21<br />
    27. 27. Example<br />22<br />
    28. 28. Example<br />23<br />
    29. 29. Characteristics of an effective analytics library cont.<br />2. Portfolio level analysis provision<br /> Example: Risk sensitivities<br />Automatic first-order risk sensitivity even via a complex model calibration; not using bumping (greater speed, accuracy)<br />No mapping of risk points needed externally<br /> Other Examples: CVA, PFE, VaR<br />A portfolio-level approach is mandatory<br />24<br />
    30. 30. Characteristics of an effective analytics library cont.<br />3. Decoupled trades and models:<br />Decoupling of trade/portfolio, model, solution method <br />Control “model risk”<br />Consistent calculation types available on all instruments<br />25<br />
    31. 31. Characteristics of an effective analytics library cont.<br />3. Decoupled trades and models:An object oriented architecture provides you with the building blocks to value any trade<br />Product/Trade<br />Options<br />Swaps<br />Portfolios<br />………………….<br />26<br />
    32. 32. Characteristics of an effective analytics library cont.<br />+<br />Model<br />Product/Trade<br />Market Models<br />Black Scholes<br />Libor Market Model<br />Heston<br />………………….<br />Options<br />Swaps<br />Portfolios<br />………………….<br />27<br />3. Decoupled trades and models:An object oriented architecture provides you with the building blocks to value any trade<br />
    33. 33. Characteristics of an effective analytics library cont.<br />Method<br />+<br />+<br />Model<br />Product/Trade<br />Market Models<br />Black Scholes<br />Libor Market Model<br />Heston<br />………………….<br />Options<br />Swaps<br />Portfolios<br />………………….<br />Default Closed form<br />Monte Carlo<br />Backward Evolution<br />………………….<br />28<br />3. Decoupled trades and models:An object oriented architecture provides you with the building blocks to value any trade<br />
    34. 34. Characteristics of an effective analytics library cont.<br />Method<br />+<br />+<br />Model<br />Product/Trade<br />Market Models<br />Black Scholes<br />Libor Market Model<br />Heston<br />………………….<br />Options<br />Swaps<br />Portfolios<br />………………….<br />Default Closed form<br />Monte Carlo<br />Backward Evolution<br />………………….<br />Value<br />Risk<br />Cash Flows<br />Par Rate/Greeks<br />………………….<br />Output<br />29<br />3. Decoupled trades and models:An object oriented architecture provides you with the building blocks to value any trade<br />
    35. 35. Characteristics of an effective analytics library cont.<br />4. Object re-use<br />Model calibration <br />Curves<br />Market conventions<br />Portfolio/trade<br />Output<br />Method<br />Product/Trade<br />Model<br />Value<br />Risk<br />Cash Flows<br />Par Rate/Greeks<br />………………….<br />Black Scholes<br />Libor Market Model<br />Heston<br />………………….<br />Options<br />Swaps<br />Portfolios<br />………………….<br />Default Closed form<br />Monte Carlo<br />Backward Evolution<br />………………….<br />30<br />
    36. 36. Characteristics of an effective analytics library cont.<br />5. IT considerations<br />Platform support (deploy anywhere)<br />Ease of integration<br />Future-proof<br />Support<br />31<br />
    37. 37. A worked example<br />Building a simple desktop portfolio analysis tool that illustrates:<br />Powerful portfolio specification<br />Portfolio-level analysis provided<br />Decoupled trades and models<br />Object re-use<br />32<br />
    38. 38. A worked example<br />Underlying technology of the demonstration:<br />Written in Java using Swing<br />Employs the F3 financial analytics platform<br />33<br />
    39. 39. 34<br />Demonstration<br />34<br />
    40. 40. A worked example – what we saw<br />Building a simple desktop portfolio analysis tool that illustrates:<br />Powerful portfolio specification<br />Portfolio-level analysis provided<br />Decoupled trades and models<br />Object re-use<br />35<br />
    41. 41. 36<br />“Accelerating portfolio level analysis with third party analytics”<br />How can you efficiently produce a portfolio analysis solution that is easy to maintain, extend and scale?<br />
    42. 42. Q & A<br />37<br />
    43. 43. Contacts<br />38<br />USA/Canada:1.800.304.0702<br />Europe Toll-Free: 00.800.304.07020<br />Other Regions: 1.604.957.1200<br />Email: info@fincad.com<br />Or visit our website www.fincad.com to: <br />View on-demand product demos<br />Download datasheets and brochures<br />Read press releases and newsletters<br />

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