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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: Wiley Finance Series
- Sprache: Englisch
A practical guide to counterparty risk management and credit value adjustment from a leading credit practitioner
Please note that this second edition of Counterparty Credit Risk and Credit Value Adjustment has now been superseded by an updated version entitled The XVA Challenge: Counterparty Credit Risk, Funding, Collateral and Capital.
Since the collapse of Lehman Brothers and the resultant realization of extensive counterparty risk across the global financial markets, the subject of counterparty risk has become an unavoidable issue for every financial institution. This book explains the emergence of counterparty risk and how financial institutions are developing capabilities for valuing it. It also covers portfolio management and hedging of credit value adjustment, debit value adjustment, and wrong-way counterparty risks. In addition, the book addresses the design and benefits of central clearing, a recent development in attempts to control the rapid growth of counterparty risk. This uniquely practical resource serves as an invaluable guide for market practitioners, policy makers, academics, and students.
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Veröffentlichungsjahr: 2012
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Contents
Cover
Title Page
Copyright
Dedication
Acknowledgements
List of Spreadsheets
List of Appendices
Section I: Introduction
Chapter 1: Introduction
Chapter 2: Background
2.1 Introduction
2.2 Financial Risk
2.3 Value-at-Risk
2.4 The Derivatives Market
2.5 Counterparty Risk in Context
2.6 Summary
Chapter 3: Defining Counterparty Credit Risk
3.1 Introducing Counterparty Credit Risk
3.2 Components and Terminology
3.3 Control and Quantification
3.4 Summary
Section II: Mitigation of Counterparty Credit Risk
Chapter 4: Netting, Compression, Resets and Termination Features
4.1 Introduction
4.2 Netting
4.3 Termination Features and Trade Compression
4.4 Conclusion
Chapter 5: Collateral
5.1 Introduction
5.2 Collateral Terms
5.3 Defining the Amount of Collateral
5.4 The Risks of Collateralisation
5.5 Summary
Chapter 6: Default Remote Entities and the Too Big to Fail Problem
6.1 Introduction
6.2 Special Purpose Vehicles4
6.3 Derivative Product Companies
6.4 Monolines and Credit DPCs
6.5 Central Counterparties
Chapter 7: Central Counterparties
7.1 Centralised Clearing
7.2 Logistics of Central Clearing
7.3 Analysis of the Impact and Benefits of CCPs
7.4 Conclusions
Chapter 8: Credit Exposure
8.1 Credit Exposure
8.2 Metrics for Credit Exposure
8.3 Factors Driving Credit Exposure
8.4 Understanding the Impact of Netting on Exposure
8.5 Credit Exposure and Collateral
8.6 Risk-Neutral or Real-World?
8.7 Summary
Section III: Credit Value Adjustment
Chapter 9: Quantifying Credit Exposure
9.1 Introduction
9.2 Methods for Quantifying Credit Exposure
9.3 Monte Carlo Methodology
9.4 Models for Credit Exposure
9.5 Netting Examples
9.6 Allocating Exposure
9.7 Exposure and Collateral
9.8 Summary
Chapter 10: Default Probability, Credit Spreads and Credit Derivatives
10.1 Default Probability and Recovery Rates
10.2 Credit Default Swaps
10.3 Curve Mapping
10.4 Portfolio Credit Derivatives
10.5 Summary
Chapter 11: Portfolio Counterparty Credit Risk
11.1 Introduction
11.2 Double Default
11.3 Credit Portfolio Losses
11.4 Summary
Chapter 12: Credit Value Adjustment
12.1 Definition of CVA
12.2 CVA and Exposure
12.3 Impact of Default Probability and Recovery
12.4 Pricing New Trades Using CVA
12.5 CVA with Collateral
12.6 Summary
Chapter 13: Debt Value Adjustment
13.1 DVA and Counterparty Risk
13.2 The DVA Controversy
13.3 How to Monetise DVA
13.4 Further DVA Considerations
13.5 Summary
Chapter 14: Funding and Valuation
14.1 Background
14.2 OIS Discounting
14.3 Funding Value Adjustment
14.4 Optimisation of CVA, DVA and Funding Costs
14.5 Future Trends
14.6 Summary
Chapter 15: Wrong-Way Risk
15.1 Introduction
15.2 Overview of Wrong-way Risk
15.3 Portfolio Wrong-way Risk
15.4 Trade-level Wrong-way Risk
15.5 Wrong-way Risk and Credit Derivatives
15.6 Summary
Section IV: Managing Counterparty Credit Risk
Chapter 16: Hedging Counterparty Risk
16.1 Background to CVA Hedging
16.2 Components of CVA Hedging
16.3 Exposure Hedges
16.4 Credit Hedges
16.5 Cross-dependency
16.6 The Impact of DVA and Collateral
16.7 Summary
Chapter 17: Regulation and Capital Requirements
17.1 Introduction
17.2 Basel II
17.3 Exposure Under Basel II
17.4 Basel III
17.5 Central Counterparties
17.6 Summary
Chapter 18: Managing CVA – The “CVA Desk”
18.1 Introduction
18.2 The Role of a CVA Desk
18.3 CVA Charging
18.4 Technology
18.5 Practical Hedging of CVA
18.6 Summary
Chapter 19: The Future of Counterparty Risk
19.1 Key Components
19.2 Key Axes of Development
19.3 The Continuing Challenge for Global Financial Markets
References
Index
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Library of Congress Cataloging-in-Publication Data
Gregory, Jon, PhD
Counterparty credit risk and credit value adjustment : a continuing challenge for global financial markets / Jon Gregory. – 2nd ed.
p. cm.
Rev. ed. of: Counterparty credit risk. c2010.
Includes index.
ISBN 978-1-118-31667-2 (cloth) – ISBN 978-1-118-31665-8 (ebk) – ISBN 978-1-118-31664-1 (ebk)
1. Derivative securities–Mathematical models. 2. Risk management. I. Gregory, Jon, Ph. D. Counterparty credit risk. II. Title.
HG6024.A3G74 2012
332.64′57—dc23
2012023249
A catalogue record for this book is available from the British Library.
ISBN 978-1-118-31667-2 (hardback) ISBN 978-1-118-31665-8 (ebk)
ISBN 978-1-118-31666-5 (ebk) ISBN 978-1-118-31664-1 (ebk)
To Ginnie, George and Christy
Acknowledgements
Less than three years have passed since the first edition of this book was published and yet the subject area has changed and expanded dramatically. I hope his second edition will serve as a timely and thorough update with respect to the subject of counterparty credit risk and all related aspects. Indeed, this is much more than a second edition, most of the subject matter has been re-written and expanded, with several new chapters. To avoid increasing the size of this book significantly, the mathematical appendices are not included but are freely available with the accompanying spreadsheets on my website at www.cvacentral.com. Since many readers do not need to study this material in depth then I hope this is a reasonable separation to make.
I have been fortunate to have been able to obtain feedback on various chapters from various experts within the industry and academia, notably Ronnie Barnes, Karin Bergeron, Liesbeth Bodvin, Alexandre Bon, Christoph Burgard, Andrew Green, Matthew Leeming, Michael Pykhtin, Nicolas Rabeau, Colin Sharpe and David Wigan. I hope it is obvious that any remaining errors are the responsibility of the author.
Thanks go to Aimee Dibbens, Sam Hartley, Lori Laker and Jennie Kitchin at Wiley for helping me through the process. I am very grateful to Rebecca Newenham and Desiree Marie Leedo at Get Ahead VA for much help around administration and proofreading. I would like to also thank my colleagues at Solum Financial Partners in London, notably Vincent Dahinden, Thu-Uyen Nyugen and Rowan Alston.
In the last two and a half years, I have been fortunate to visit Amsterdam, Barcelona, Berlin, Boston, Brussels, Chicago, Dubai, Dusseldorf, Finland, Frankfurt, Geneva, Hong Kong, Iceland, Madrid, Melbourne, Milan, Mumbai, New York, Paris, Rome, Sao Paolo, Singapore, Sydney, Taipei, Toronto, Turkey and Warsaw in relation to counterparty risk assignments. I thank everyone that I have connected with at conferences, training courses and consulting projects for the ideas and questions that kept me thinking about this vast but fascinating subject.
Jon Gregory, August 2012
Spreadsheets
One of the key features of the first edition of this book was the accompanying spreadsheets that were prepared to allow the reader to gain some simple insight into some of the quantitative aspects discussed in the main text. Many of these examples have been used for training courses and have therefore evolved to be quite intuitive and user-friendly (I hope). For this second edition, I have completely updated these spreadsheets to include more sophisticated and additional examples. The spreadsheets can be downloaded freely from my website www.cvacentral.com under the counterparty risk section. New examples will be added over time. Any questions then please contact me via the above website.
Spreadsheet 3.1Counterparty risk for a forward contract-type exposure.Spreadsheet 4.1Simple netting calculation.Spreadsheet 6.1Simple monoline example.Spreadsheet 8.1EE and PFE for a normal distribution.Spreadsheet 8.2EPE calculation.Spreadsheet 8.3EPE and effective EPE example.Spreadsheet 8.4Simple example of a cross-currency swap profile.Spreadsheet 8.5Simple calculation of the exposure of a CDS.Spreadsheet 8.6Call and return collateral example with logic relating to independent amounts, thresholds, collateral held, minimum transfer amount and rounding.Spreadsheet 9.1Simulation of an interest rate swap exposure with a one-factor Vasicek model.Spreadsheet 9.2Illustration of the impact of netting for the examples considered.Spreadsheet 9.3Example marginal exposure calculation.Spreadsheet 9.4Incremental exposure calculations.Spreadsheet 9.5Marginal exposure calculations.Spreadsheet 9.6Quantifying the impact of collateral on exposure.Spreadsheet 10.1Analysis of historical default probabilities.Spreadsheet 10.2Calculating market-implied default probabilities.Spreadsheet 11.1Calculation of joint default probabilities with a bivariate normal distribution function.Spreadsheet 11.2Calculation of unexpected losses and “alpha” factor for a credit portfolio with random exposures.Spreadsheet 12.1Simple CVA calculation.Spreadsheet 12.2Semi-analytical calculation of the CVA for a swap.Spreadsheet 13.1Simple BCVA calculation.Spreadsheet 14.1Example FVA calculation.Spreadsheet 15.1Wrong-way risk calculations of expected exposure.Spreadsheet 15.2Black–Scholes formula with counterparty risk.Appendices
The following is a list of Appendices which contain additional mathematical detail. These Appendices can be downloaded freely from my website www.cvacentral.com under the counterparty risk section. Any questions then please contact me via the above website.
Appendix 6ASimple monoline formula.Appendix 8AFormulas for EE, PFE and EPE for a normal distribution.Appendix 8BExample exposure calculation for a forward contract.Appendix 8CExample exposure calculation for a swap.Appendix 8DExample exposure calculation for a cross-currency swap.Appendix 8ESimple netting calculation.Appendix 9AAdditional mathematical detail on exposure models.Appendix 9BMarginal exposure calculation.Appendix 9CExample calculations of the impact of collateral on exposure.Appendix 10ADefinition of cumulative default probability function.Appendix 10BMathematics behind the default process and calculation of market-implied default probabilities.Appendix 11ACalculation of joint default probabilities with a bivariate normal distribution function and description of credit portfolio models.Appendix 12ADeriving the standard CVA formula.Appendix 12BComputation of the CVA formula and simple spread-based approximation.Appendix 12CCVA formula for an option position.Appendix 12DSemi-analytical calculation of the CVA for a swap.Appendix 12EIncremental CVA formula.Appendix 13ADeriving the bilateral CVA formula.Appendix 14AFunding value adjustment (FVA) formula and comparison to the “discount curve method”.Appendix 15AComputing the EE of a typical forward exposure with correlation to a time of default and description of the more general correlation approach to wrong-way risk.Appendix 15BDevaluation approach for FX wrong-way risk.Appendix 15CBlack-Scholes formula for counterparty risk.Appendix 15DMethods for pricing single-name CDS and tranches with counterparty risk.Appendix 15EIllustration of the value created in a CDO structure.Appendix 17AThe large homogeneous pool (LHP) approximation.Appendix 17BAsset correlation and maturity adjustment factor formulas in Basel II.Appendix 17CTreatment of netting and collateral in the current exposure method (CEM).Appendix 17DThe standardised method.Appendix 17EEffective maturity calculation and double default formula.Section I: Introduction
This first section covers introductory aspects in relation to counterparty credit risk and CVA and is aimed at providing the background for readers new to the area.
Chapter 1 sets the scene and explains the emergence of counterparty credit risk, especially in relation to the global financial crisis that began in 2007. This discusses the basic problems such as the “too big to fail” phenomenon and the complexities of the activities of banks and the OTC derivative markets. The role of regulation is also introduced.
Some of the important background concepts are covered in Chapter 2. This discusses financial risk management in general, outlining the different types of financial risk and their relationship to counterparty risk. The important concept of value-at-risk (VAR) is also defined and explained with the dangers of VAR carefully noted. This chapter discusses OTC (over-the-counter) derivatives markets and their benefits and drawbacks, paying particular attention to credit derivative instruments which allow hedging of counterparty risk but contain significant counterparty risk themselves. The mitigation of counterparty risk, in particular in relation to central clearing, is introduced also.
Chapter 3 is dedicated to defining counterparty risk. This includes outlining the underlying products for which counterparty risk is relevant and discussing the development and nature of exchange traded and OTC derivative markets. The different players in the counterparty risk world are outlined, from large global dealer banks to end-users such as sovereigns and corporates. The components of counterparty risk, such as credit exposure, default probability, credit spreads, recovery rates and replacement costs, are defined. The control and quantification of counterparty risk through credit limits and credit value adjustment (CVA) is discussed. Finally, portfolio effects and hedging are introduced.
Chapter 1
Introduction
Between 2004 and 2006, US interest rates rose from 1% to over 5%, triggering a slowdown in the US housing market. Many homeowners, who had been barely able to afford their payments when interest rates were low, began to default on their mortgages. Default rates on subprime loans (made to those with a poor or no credit history) hit record levels. US households had also become increasingly in debt, with the ratio of debt to disposable personal income rising. Many other countries (although not all) had ended up in a similar situation. Years of poor underwriting standards and cheap debt were about to catalyse a global financial crisis.
Many of the now toxic US subprime loans were held by US retail banks and mortgage providers such as Fannie Mae and Freddie Mac. However, the market had been allowed to spread due to the fact that the underlying mortgages had been packaged up into complex structures (using financial engineering techniques), such as mortgage-backed securities (MBSs), which had been given good credit ratings from the rating agencies. As a result, the underlying mortgages ended up being held by institutions that did not originate them, such as investment banks and institutional investors outside the US. Financial engineering had created a global exposure to US mortgages.
In mid-2007, a credit crisis began, caused primarily by the systematic mispricing of US mortgages and MBSs. Whilst this caused excessive volatility in the credit markets (which had been quiet for a number of years), it was not believed to be a severe financial crisis (for example, the stock market did not react particular badly). The crisis, however, did not go away.
In July 2007, Bear Stearns informed investors they would get very little of their money back from two hedge funds due to losses in subprime mortgages. In August 2007, BNP Paribas told investors that they would be unable to take money out of two funds because the underlying assets could not be valued due to “a complete evaporation of liquidity in the market”. Basically, this meant that the assets could not be sold at any reasonable price. In September 2007, Northern Rock, a British Bank, sought emergency funding from the Bank of England as a “lender of last resort”. This prompted the first run on a bank1 for over a century. Northern Rock, in 2008, would be taken into state ownership to save depositors and borrowers.
By the end of 2007, some insurance companies, known as “monolines”, were in serious trouble. Monolines provided insurance to banks on mortgage and other related debt. The Triple-A ratings of monolines had meant that banks were not concerned with a potential monoline default, despite the obvious misnomer that a monoline insurance company appeared to represent. Banks' willingness to ignore the counterparty risk had led them to build up large monoline exposures without the requirement for monolines to post collateral, at least as long as they maintained their excellent Triple-A credit ratings. However, monolines were now reporting large losses and making it clear that any downgrading of their credit ratings may trigger collateral calls that they would not be able to make. Such downgrades began in December 2007 and banks were forced to take losses totalling billions of dollars due to the massive counterparty risk they now faced. This was a particularly bad form of counterparty risk, known as wrong-way risk, where the exposure to a counterparty and their default probability were inextricably linked.
By the end of 2007, although not yet known, the US economy was in recession and many other economies would follow. The crisis was now affecting the general public and yet this was only the tip of the iceberg.
In March 2008, Bear Stearns was purchased by JP Morgan Chase for just $2 a share, assisted by a loan of tens of billions of dollars from the Federal Reserve, who were essentially taking $30 billion of losses from the worst Bear Stearns assets to catalyse the sale. This clearly represented a form of bailout, with the US taxpayer essentially funding some of the purchase of Bear Stearns. The sale price of Bear Stearns was shocking, considering that it had been trading at $93 a share only a month previously. Something was clearly going very wrong. In early September 2008, mortgage lenders Fannie Mae and Freddie Mac, who combined accounted for over half the outstanding US mortgages, were placed into conservatorship (a sort of short-term nationalisation) by the US Treasury. Treasury secretary Henry Paulson stated that the combined debt levels posed a “systemic risk” to financial stability.
In September 2008 the unthinkable happened when Lehman Brothers, a global investment bank and the fourth largest investment bank in the US with a century-long tradition, filed for Chapter 11 bankruptcy protection (the largest in history). This occurred after teams of bankers failed during the weekend spent in the Federal Reserve Building to agree any better solution, in particular with Barclays and Bank of America pulling out of buying Lehman. The US government was reluctant to rescue Lehman due to the moral hazard that such bailouts encourage. The bankruptcy of Lehman had not been anticipated, with all major rating agencies (Moody's, Standard & Poor's and Fitch) all giving at least a Single-A rating right up to the point of Lehman's failure and the credit derivative market not pricing an actual default.
Saving Lehman's would have cost the US taxpayer again and exasperated moral hazard problems since a bailout of Lehman's would not punish their excessive risk taking (their exposure to the mortgage market and risky behaviour in understating the need for new funding). However, a Lehman default was not an especially pleasant prospect either. Firstly, there was estimated to be around $400 billion of credit default swap (CDS) insurance written on Lehman Brothers debt. Since the debt was now close to worthless, this would trigger massive payouts on the underlying CDS contracts and yet the opacity of the OTC derivatives market meant that it was not clear who actually owned most of the CDS referencing Lehman. Another counterparty might now have financial problems due to suffering large losses because of providing CDS protection on Lehman. Secondly, Lehman had around one and a half million derivatives trades with around 8,000 different counterparties that all needed to be unwound, a process that would take years and lead to many legal proceedings. Most counterparties probably never considered that their counterparty risk to Lehman's was a particular issue nor did they realise that the failure of counterparty risk mitigation methods such as collateral and special purpose vehicles (SPVs) would lead to legal problems.
On the same day as Lehman's failed, Bank of America agreed a $50 billion rescue of Merrill Lynch. Soon after the remaining two investment banks, Morgan Stanley and Goldman Sachs, opted to become commercial banks. Whilst this would subject them to more strict regulation, it allowed full access to the Federal Reserve's lending facilities and prevented them suffering the same fate as the bankrupt Lehman Brothers or the sold Bear Stearns or Merrill Lynch.
In case September 2008 was not exciting enough, the US government provided American International Group (AIG) with loans of up to $85 billion in exchange for a four-fifths stake in AIG.2 Had AIG been allowed to fail through bankruptcy, not only would bondholders have suffered but also their derivative counterparties (the major banks) would have experienced significant losses. Prior to the crisis, the counterparty risk of AIG was typically considered minimal due to their size, excellent credit rating and the fact that (unlike monoline insurers) they did post collateral. The reason for the rescue of AIG and non-rescue of monolines was partly timing – the AIG situation occurred at the same time as the Lehman bankruptcy and immediately after the Fannie Mae and Freddie Mac rescues. However, another important fact was that AIG had an exposure that was not dissimilar to the total exposure of the monoline insurers but concentrated within a single financial entity. AIG was “too big to fail”.
Three of the largest US investment banks had now either gone bankrupt (Lehman Brothers) or been sold at fire sale prices to other banks (Bear Stearns and Merrill Lynch). The remaining two had given up their prized investment bank status to allow them to be bailed out. Later in September 2008 Washington Mutual, America's biggest savings and loan company, was sold to JP Morgan for $1.9 billion and their parent company, Washington Mutual Inc., filed for Chapter 11 bankruptcy protection. A CDS contract purchased from any of the aforementioned US banks on any of the other US banks was now clearly seen to have enormous, almost comical amounts of wrong-way counterparty risk.
By now, trillions of dollars had simply vanished from the financial markets and therefore the global economy. Whilst this was related to the mispricing of mortgage risk, it was also significantly driven by the recognition of counterparty risk.
On October 6, the Dow Jones Industrial Average dropped more than 700 points and fell below 10,000 for the first time in four years. The systemic shockwaves arising from the failure of the US banking giants led to the Troubled Asset Relief Program (TARP) of not too much short of $1 trillion to purchase distressed assets and support failing banks. In November 2008, Citigroup, prior to the crisis the largest bank in the world but now reeling following a dramatic plunge in its share price, needed TARP assistance, via a $20 billion cash injection and government backing for around $300 billion of loans.
The contagion had spread far beyond the US. In early 2009, the Royal Bank of Scotland (RBS) reported a loss of £24.1 billion, the biggest in British corporate history. The majority of this loss was borne by the British government, now the majority owner of RBS, having paid £45 billion3 to rescue RBS in October 2008. In November 2008 the International Monetary Fund (IMF), together with other European countries, approved a $4.6 billion loan for Iceland after the country's banking system collapsed in October. This was the first IMF loan to a Western European nation since 1976.
By now, it was clear that no counterparty (Triple-A entities, global investment banks, retail banks, sovereigns) could ever be regarded as risk-free. Counterparty risk, previously hidden via spurious credit ratings, collateral or legal assumptions, was now present throughout the global financial markets. CVA (credit value adjustment), which defined the price of counterparty risk, had gone from a rarely used technical term to a buzzword constantly associated with financial markets. The pricing of counterparty risk into trades (via a CVA charge) was now becoming the rule and not the exception. Whilst the largest investment banks had built trading desks and complex systems and models around managing CVA, all banks (and some other financial institutions and large derivatives users) were now focused on expanding their capabilities in this respect.
By 2009, new fast-tracked financial regulation was beginning to take shape around the practices of banks. The Basel III global regulatory standard (developed in direct response to the crisis) was introduced to strengthen bank capital bases and introduce new requirements on liquidity and leverage. The US Dodd–Frank Wall Street Reform and Consumer Protection Act 2009 and European Market Infrastructure Regulation (EMIR) were aimed at increasing the stability of the over-the-counter (OTC) derivative markets. Regulatory response to the global financial crisis (as it was now known) revolved very much around counterparty credit risk, with the volatility of CVA, collateral management and wrong-way risk all receiving attention.
The regulatory focus on CVA seemed to encourage active hedging of counterparty risk so as to obtain capital relief. However, the market that would be most important for such hedging, credit derivatives, was having its own problems. Whilst credit derivatives, such as single-name and index credit default swaps, allowed counterparty risk transfer, being OTC instruments, they also introduced their own form of counterparty risk, which was the wrong-way type highlighted by the monoline failures. Indeed, the CDS market was almost seizing up due to this severe wrong-way risk. Counterparty risk was the principle linkage among participants in the CDS market that could cause systemic failures. Regulatory proposals to deal with this problem involved pushing heavily towards the central clearing of certain standard OTC derivatives, notably CDSs. Whilst, prior to the crisis, much of the interest rate swap market was already moving towards central clearing, without regulatory intervention the CDS market was arguably years away.
Whilst central counterparties (CCPs) provided advantages such as transparency that the OTC derivatives market clearly lacked, this also introduced the question of what would happen if a CCP ever failed. Since CCPs were likely to take over from the likes of Lehman, Citigroup and AIG as the hubs of the complex financial network, such a question was clearly key, and yet not particularly extensively discussed. Furthermore, other potential unintended consequences of increased regulation on counterparty risk could be seen as early as 2010 when, for example, the Bank of England commented that “CVA desks” hedging counterparty risk were causing European sovereign spreads to widen “away from levels solely reflecting the underlying probability of sovereign default”.4 Although the need to ensure investment banks were better capitalised for risk taking was not under debate, arguments developed over the correct level of capitalisation and the potential adverse and unintended consequences of new regulation.
At the same time as a renewed focus on counterparty risk, other changes in derivatives markets were taking place. A fundamental assumption in the pricing of derivative securities had always been that the risk-free rate could be appropriately proxied by LIBOR. However, practitioners realised that the OIS (overnight indexed spread) was actually a better proxy for the risk-free rate. The LIBOR–OIS spread had historically hovered around 10 basis points, showing a close linkage. However, this close relationship had broken down, even spiking to around 350 basis points around the Lehman bankruptcy. This showed that even the simplest types of derivative, which had been priced in the same way for decades, needed to be valued differently, in a more sophisticated manner. Since CVA is an adjustment to the risk-free value of a transaction, this topic was clearly closely related to counterparty risk.
Another, almost inevitable dynamic was that the spreads of banks (i.e., where they could borrow unsecured cash on a longer term than in a typical LIBOR transaction) had increased. Historically, this borrowing cost of a bank was in the region of a few basis points but had now entered the realms of hundreds of basis points in most cases. It was clear that these now substantial funding costs should be quantified alongside CVA. The cost of funding was named FVA (funding value adjustment). Funding costs were also clearly linked to counterparty risk in terms of the calculation and also their similarity to something now known as DVA (debt value adjustment).
DVA for some banks was the only silver lining of the counterparty risk cloud. DVA allowed banks to account for their own default in the value of transactions and therefore acted to counteract counterparty risk-related losses due to an increase in CVA driven by the widening credit spread environment. However, many commentators believed this to be nothing more than an accounting trick as banks reported billions of dollars of profits from DVA simply due to the fact that their own credit spread implied they were more likely to default in the future. Basel III capital rules moved to remove DVA benefits to avoid effects such as “an increase in a bank's capital when its own creditworthiness deteriorates”.
The net result of the financial crisis and the impact of regulation led banks to consider the joint impact of risk-free valuation, counterparty risk and funding costs in the valuation of derivatives. Not surprisingly, the increase in funding costs and counterparty risk naturally led banks to tighten up collateral requirements. However, this created a knock-on effect for typical end-users of derivatives that historically had not been able or willing to enter into collateral agreement for liquidity and operational reasons. Some sovereign entities considered posting collateral, not only to avoid the otherwise large CVA and funding costs levied upon them, but also to avoid the issue that banks hedging their counterparty risk may buy CDS protection on them, driving their credit spread wider and potentially causing them more problems. A sovereign posting their own bonds in collateral would ease the problems but this would not be the ideal answer due to another manifestation of wrong-way risk. Furthermore, corporates other than non-collateral posting entities had issues, for example an airline predicted more volatile earnings “not because of unpredictable passenger numbers, interest rates or jet fuel prices, but because it does not post collateral in its derivatives transactions”.5 End-users of derivatives, although not responsible, were now being hit as badly as the orchestrators of the financial crisis.
Meanwhile, many taxpayers were experiencing poor economic conditions and counting the cost of bailouts via higher taxes and reduced government spending. Businesses and individuals were struggling to borrow money from the heavily capitalised banks. All of this was created by a crisis fuelled to a large extent by counterparty risk.
Because of the above, counterparty risk has become a major subject for global financial markets. It is necessary to consider how to define and quantify counterparty risk. Counterparty risk mitigation methods need to be understood, and their side-effects and any residual risks need to be defined. The question of the role of central counterparties must be examined, alongside the consideration of the risks they will represent. It is important to define how CVA can be quantified and managed, together with other related components such as DVA and FVA. Wrong-way risk must be understood and mitigated or avoided completely. The role and positioning of a CVA desk within a bank or other institution must be defined. The regulation around counterparty risk must be understood, together with the likely impact this will have on banks and the financial markets in which they operate. Finally, the consideration of how all of the above changes are likely to define counterparty risk practices in the future is important.
If any of the above are of interest, then please read on.
Notes
1. This occurs when a large number of customers withdraw their deposits because they believe the bank is, or might become, insolvent.
2. AIG would receive further bailouts.
3. Hundreds of billions of pounds were provided in the form of loans and guarantees.
4. This would trigger the consideration of a sovereign exemption with respect to CVA capital charges.
5. “Corporates fear CVA charge will make hedging too expensive”, Risk, October 2011.
Chapter 2
Background
2.1 Introduction
Financial risk is broken down into a number of different types, one of which is counterparty risk. Counterparty risk is arguably the most complex financial risk to deal with since it is driven by the intersection of different risk types (for example, market and credit) and is highly sensitive to systemic traits, such as the failure of large institutions. Counterparty risk also mainly involves the most complex financial instruments, derivatives. Derivatives can be extremely powerful and useful, have aided the growth of global financial markets and have aided economic growth. However, as almost every average person now knows, derivatives can be highly toxic and cause massive losses and financial catastrophes if misused.
In this chapter, we review some of the background to counterparty risk and discuss other forms of financial risk. Counterparty risk should be considered and understood in the context of other financial risks, which we briefly review first. We also discuss the value-at-risk (VAR) concept, which is similar to PFE (potential future exposure), used to assess counterparty risk.
2.2 Financial Risk
Financial risk management has experienced a revolution over the last two decades. This has been driven by infamous financial disasters due to the collapse of large financial institutions such as Barings (1995), Long-Term Capital Management (1998), Enron (2001), Worldcom (2002), Parmalat (2003) and Lehman Brothers (2008). Such disasters have proved that huge losses can arise from insufficient management of financial risk and cause negative waves throughout the global financial markets. Financial risk is typically sub-divided into a number of different types that will be described below.
2.2.1 Market Risk
Market risk arises from the (short-term) movement of market prices. It can be a linear risk, arising from an exposure to the movement of underlying variables such as stock prices, interest rates, foreign exchange rates, commodity prices or credit spreads. Alternatively, it may be a non-linear risk arising from the exposure to market volatility as might arise in a hedged position. Market risk has been the most studied financial risk of the past two decades, with quantitative risk management techniques widely applied in its measurement and management. This was catalysed by some serious market risk-related losses in the 1990s (e.g., Barings) and the subsequent amendments to the Basel I capital accord in 1995 that allowed financial institutions to use proprietary mathematical models to compute their capital requirements for market risk. Indeed, market risk has mainly driven the development of the value-at-risk (described later) approach to risk quantification.
Market risk can be eliminated by entering into an offsetting contract. However, unless this is done with the same counterparty as the original position(s), then counterparty risk will be generated. If the counterparties to offsetting contracts differ, and either counterparty fails, then the position is no longer neutral. Market risk therefore forms a component of counterparty risk.
2.2.2 Credit Risk
Credit risk is the risk that a debtor may be unable or unwilling to make a payment or fulfil contractual obligations. This is often known generically as default, although this has slightly different meanings and impact depending on the jurisdiction involved. The default probability must be characterised fully throughout the lifetime of the exposure (e.g., bond maturity) and so too must the recovery value (or equivalently the loss given default). Less severe than default, it may also be relevant to consider deterioration in credit quality, which will lead to a mark-to-market loss (due to the increase in future default probability). In terms of counterparty risk, characterising the term structure of the counterparty's default probability is a key aspect.
2.2.3 Liquidity Risk
Liquidity risk is normally characterised in two forms. Asset liquidity risk represents the risk that a transaction cannot be executed at market prices, perhaps due to the size of the position and/or relative illiquidity of the underlying. Funding liquidity risk refers to the inability to fund contractual payments or collateral requirements, potentially forcing an early liquidation of assets and crystallisation of losses. Since such losses may lead to further funding issues, funding liquidity risk can manifest itself via a “death spiral” caused by the negative feedback between losses and cash requirements. Reducing counterparty risk often comes at the potential cost of increasing funding liquidity risk via mechanisms such as collateralisation or central clearing.
2.2.4 Operational Risk
Operational risk arises from people, systems, internal and external events. It includes human error (such as trade entry mistakes), failed processes (such as settlement of trades or posting collateral), model risk (inaccurate or badly calibrated models), fraud (such as rogue traders) and legal risk (such as the inability to enforce legal agreements such as those covering netting or collateral terms). Whilst some operational risk losses may be moderate and common (incorrectly booked trades for example), the most significant losses are likely to be a result of highly improbable scenarios or even a “perfect storm” combination of events. Operational risk is therefore extremely hard to quantify, although quantitative techniques are increasingly being applied. Counterparty risk mitigation methods, such as collateralisation, inevitably give rise to operational risks.
2.2.5 Integration of Risk Types
A particular weakness of financial risk management over the years has been the lack of focus on the integration of different risk types. It has been well known for many years that crises tend to involve a combination of different financial risks. Given the difficulty in quantifying and managing financial risks in isolation, it is not surprising that limited effort is given to combining them. Counterparty risk itself is already a combination of two different risk types, market and credit. Furthermore, the mitigation of counterparty risk can create other types of risk such as liquidity and operational. It is important not to lose sight of counterparty risk as an intersection of many types of financial risk.
2.3 Value-at-Risk
2.3.1 Definition
Quantitative approaches to financial risk management have been widely adopted in recent times, in particular with the popularity of the value-at-risk (VAR) concept. Initially designed as a metric for market risk, VAR has subsequently been used across many financial areas as a means for efficiently summarising risk via a single quantity. A VAR number has a simple and intuitive explanation as the worst loss over a target horizon to a certain specified confidence level. The VAR at the α% confidence level gives a value that will be exceeded with no more than a probability. An example of the computation of VAR is shown in Figure 2.1. The VAR at the 99% confidence level is 125 (by convention the “worst loss” is expressed as a positive number) since the probability that this will be exceeded is no more than 1% (it is actually 0.92% due to the discrete1 nature of the distribution). To find the VAR, one looks for the minimum value that will be exceeded with no more than the specified probability.
Figure 2.1 Illustration of the value-at-risk (VAR) concept at the 99% confidence level. The VAR is 125, since the chance of a loss (negative return) greater than this amount is no more than 1%.
VAR may be used to set regulatory capital requirements. For example, banks with the relevant approval to use their own internal models may compute their capital requirements for market risk directly via their calculated VAR2 multiplied by a minimum supervisory factor of three. VAR is also used for internal limit setting and analysis of risks across different risk types.
2.3.2 The Dangers of VAR
VAR is a very useful way in which to summarise the risk of an entire distribution in a single number that can easily be understood. It also makes no assumption as to the nature of the distribution itself, such as that it is a normal (Gaussian) distribution.3 It is, however, open to problems of misinterpretation since VAR says nothing at all about what lies beyond the defined (1% in the above example) threshold. Figure 2.2 shows a slightly different distribution with the same VAR. In this case, the probability of losing 250 is 1% and hence the 99% VAR is again 125 (since there is zero probability of other losses in-between). We can see that changing the loss of 250 does not change the VAR since it is only the probability of this loss that is relevant. Hence, VAR does not give an indication of the possible loss outside the confidence level chosen. A certain VAR number does not mean that a loss of 10 times this amount is impossible (as it would be for a normal distribution). Over-reliance upon VAR numbers can be counterproductive as it may lead to false confidence.4
Figure 2.2 Distribution with the same VAR as Figure 2.1.
2.3.3 Models
The use of metrics such as VAR encourages a reliance on quantitative models in order to derive the distribution of returns from which such metrics can be calculated. The use of complicated models facilitates combining many complex market characteristics such as volatility and dependence into one or more simple numbers that can represent risk. Models can compare different trades and quantify which is better, at least according to certain predefined metrics. All of these things can be done in minutes or even seconds to allow institutions to make fast decisions in rapidly moving financial markets.
However, the financial markets have a somewhat love/hate relationship with mathematical models. In good times, models tend to be regarded as invaluable, facilitating the growth in complex derivatives products and dynamic approaches to risk management adopted by many large financial institutions. Only in bad times, and often after significant financial losses, is the realisation that models are only simple approximations to the reality of financial markets fully appreciated. Most recently, following the financial crisis beginning in 2007, mathematical models have been heavily criticised for the incorrect modelling of mortgage-backed securities and other structured credit products that led to significant losses.
The potential for “blowups” in financial markets, especially derivatives, has led to models being either loved or berated depending on the underlying market conditions. Take the most famous model of them all, the Black–Scholes–Merton (BSM) option-pricing formula (Black and Scholes, 1973) as an example. The financial markets took a while to warm to this approach but by around 1977, traders were treating the formula as gospel. On Black Monday (19th October 1987), US stocks collapsed by 23%, wiping out $1 trillion in capital and this was partly due to dynamic-hedging strategies such as CPPI (constant proportion portfolio insurance) made possible by the BSM theory. Nevertheless, in 1995, Myron Scholes and Robert Merton were awarded the Nobel Prize for Economic Sciences.5 The danger is that models tend to be viewed either as “good” or “bad” depending on the underlying market conditions. Whereas, in reality, models can be good or bad depending on how they are used. An excellent description of the intricate relationship between models and financial markets can be found in MacKenzie (2006).
The changing and inconsistent view of quantitative models within finance also arises from the fact that models are applied to many different problems, some of which are reasonable to model and some of which are not. The rating agencies' willingness to rate highly complex structured credit products with rapidly developed models is an example of the latter category. In this case, the data available was so scarce that statistical modelling, in some cases, should never even have been attempted. VAR provides another good example of the application-of-models dilemma. A 99% VAR over one day6 is reasonable to model since a one in a hundred daily event is not particularly extreme. Such a measure is also easy to “backtest” as even a year of daily observations gives a reasonable statistical test as to whether the numbers of days the VAR has actually been exceeded is approximately correct.7 However, when higher confidence levels and/or longer time horizons are involved then risk quantification becomes more complex and difficult to test.
The modelling of counterparty risk is an inevitable requirement for financial institutions and regulators. This can be extremely useful and measures such as potential future exposure (PFE), the counterparty risk analogue of VAR, are important components of counterparty risk management. However, like VAR, the quantitative modelling of counterparty risk is complex and prone to misinterpretation and misuse. Furthermore, unlike VAR, counterparty risk involves looking years into the future rather than just a few days, which creates further complexity not to be underestimated. Not surprisingly, regulatory requirements over backtesting of counterparty risk models8 have been introduced to assess performance. In addition, a greater emphasis has been placed on stress testing of counterparty risk, to highlight risks in excess of those defined by models.
2.3.4 Correlation and Dependency
Probably the most difficult aspect in understanding and quantifying financial risk is that of dependency between different financial variables. This is well known in VAR methodologies where a large correlation matrix essentially drives the resulting VAR number. Errors in the estimation of the underlying correlations increase the uncertainty of the final VAR number. It is well known that historically estimated correlations may not be a good representation of future behaviour. This is especially true in a more volatile market environment, or crisis, where correlations have a tendency to become very large.9
Counterparty risk takes difficulties with correlation to another level, for example compared to traditional VAR models. Firstly, correlations are inherently unstable and can change significantly over time. This is important for counterparty risk assessment, which must be made over many years compared to market risk VAR that is measured over just a single day. Secondly, correlation is not the only way to represent dependency and other statistical measures are possible. Particularly in the case of wrong-way risk (Chapter 15), the treatment of dependencies via measures other than correlation is important.
2.4 The Derivatives Market
2.4.1 Uses of Derivatives
Derivatives contracts represent agreements either to make payments or to buy or sell an underlying contract at a time or times in the future. The times may range from a few weeks or months (for example, futures contracts) to many years (for example, long-dated swaps). The value of a derivatives contract will change with the level of one of more underlying assets or indices and possibly decisions made by the parties to the contract. In many cases, the initial value of a derivative traded will be contractually configured to be zero for both parties at inception.
Derivatives are not a particularly new financial innovation; for example, in medieval times, forward contracts were popular in Europe. However, derivatives products and markets have become particularly complex in the last couple of decades. One of the advantages of derivatives is that they can provide very efficient hedging tools, for example, consider the following risks that an institution, such as a corporate, may experience:
FX risk. Due to being paid in various currencies, there is a need to hedge cash inflow in these currencies.IR risk. They may wish to transform fixed- into floating-rate debt.Commodity. The need to lock in oil prices when energy costs represent a significant portion of gross margin.In many ways, derivatives are no different from the underlying cash instruments. They simply allow one to take a very similar position in a synthetic way. For example, an airline wanting to reduce their exposure to a potentially rising oil price can buy oil futures, which are cash-settled and therefore represent a very simple way to go “long oil” (with no storage or transport costs). An institution wanting to reduce their exposure to a certain asset can do so via a derivative contract, which means they do not have to sell the asset directly in the market.
2.4.2 Exchange-Traded and OTC Derivatives
Within the derivatives markets, many of the simplest products are traded through exchanges. An exchange has the benefit of facilitating liquidity and therefore making trading and unwinding of positions easy. An exchange also mitigates all counterparty risk concerns since the default of a member of the exchange would be absorbed by the exchange (in theory at least, this point is discussed in depth in Chapter 7). Products traded on an exchange must be well standardised to facilitate liquidity and transparent trading. This standardisation typically develops over a lifecycle of many years before a given derivative is suitable for exchange trading.
Compared to exchange-traded derivatives, OTC derivatives tend to be less standard structures and are typically traded bilaterally, i.e., between two parties. They are private contracts and not protected by any government insurance programme or customer asset protection programme. Hence, each party takes counterparty risk with respect to the other party. Many players in the OTC derivatives market do not have exceptional credit quality nor are they able to post collateral to reduce counterparty risk. This counterparty risk is therefore an unavoidable consequence of the OTC derivatives market. This also tends to create highly connected counterparties such as in interbank trading.
In 1986, OTC derivatives fell slightly behind exchange-traded instruments with $500 billion notional outstanding.10 By 1995, OTC derivatives' notional exceeded that of exchange-traded instruments by a ratio of more than 5 to 1, a ratio maintained in 2005.11 The OTC interest rate market is by far the largest component, having grown since the early 1980s to $284 trillion in notional value. OTC derivatives are significant in other asset classes such as foreign exchange, equities and commodities. Credit derivatives products were first developed to supplement the cash bond market but in many ways are now even more significant than cash bonds. The OTC derivatives market has grown exponentially over the last two decades, offering effective opportunities for risk management and financial innovation, which are key ingredients for economic growth. OTC derivatives dominate exchange-traded derivatives due to their inherent customisation.
In the last few years, there has been a growing trend to centrally clear derivatives, primarily aimed at reducing counterparty risk. Centrally cleared derivatives retain some OTC features (such as being initiated bilaterally) and therefore represent a halfway house between OTC and exchange trading. A derivative has to be standardised to permit central clearing but it does not need to have all the features that would allow exchange trading, such as sufficient liquidity. Central clearing (Chapter 7) will be a key component in defining the future counterparty risk landscape.
2.4.3 Risks of Derivatives
Of course, not all derivatives transactions can be classified as “socially useful”. Some involve regulatory arbitrage (i.e., reducing the regulatory capital a bank has to keep without reducing its exposures); some are concerned with changing the tax or accounting treatment of an item; occasionally an OTC derivative is designed by a dealer to appear more attractive than it is to unwary end-users.12
The use of derivatives as synthetic versions of cash assets is not particularly worrying. However, a key difference of derivatives instruments is leverage. Since most derivatives are executed with only a small (with respect to the notional value of the contract) or no upfront payment made, they allow significant leverage. If an institution has the view that US interest rates will be going down, they may buy US treasury bonds.13 There is natural limitation to the size of this trade, which is the cash that the institution can raise in order to invest in bonds. However, entering into a receiver interest rate swap in US dollars will provide approximately the same exposure to interest rates but with no initial investment.14 Hence, the size of the trade, and the effective leverage, must be limited by the institution themselves, their counterparty in the swap transaction or a regulator. Inevitably, it will be significantly bigger than that in the previous case of buying bonds outright.
Derivatives have been repeatedly shown to be capable of creating major market disturbances. They have been given such labels as “financial weapons of mass destruction”. The fact is that, as with any invention that offers significant advantages such as commercial aircraft or nuclear power, derivatives can be extremely dangerous. Some take the view that derivatives should be wholly exchange-traded or even, in some cases, outlawed (e.g., see Soros, 2009). On the other hand, many express the opposite sentiment, for example: “The only thing more dangerous than having too many derivatives floating around the financial system, it seems, is having too few of them.”15
2.3.4 Too Big to Fail and Systemic Risk
Systemic risk in financial terms concerns the potential failure of one institution that creates a chain reaction or domino effect on other institutions and consequently threatens the stability of the entire financial markets and even the global economy. Systemic risk may not only be triggered by actual losses; just a heightened perception of risk and resulting “flight to quality” away from more risky assets causes serious disruptions. Derivatives have always been strongly linked to systemic risk, due to the relatively large number of dominant counterparties, the leverage in the market together, unfortunately, with the short-sighted greed of many of the participants within these markets.
A key, but subtle, problem serves as a threat to the stability of derivatives. OTC derivatives have evolved into a market dominated by a relatively small number of financial intermediaries (often referred to as dealers). These financial intermediaries act as common counterparties to large numbers of end-users of derivatives and actively trade with each other to manage their positions. The centralisation of OTC derivatives with a small number of high-quality counterparties was perceived by some as actually adding stability – after all, surely none of these counterparties would ever fail.
It seems to have been a widely held view for many years prior to 2007 that large firms would not fail, since they could hire the best staff and adopt the best risk management practices. Such a view ignores the political, regional and management challenges within a large institution that can lead to opaque representation and communication of risks, especially at a senior level. Recent events have taught the financial markets that this concept is a fundamentally flawed one. A dramatic point in the global financial crisis was the realisation that a number of counterparties were “too big to fail” since their failure would have systemic consequences and knock-on effects that were simply not an option. Institutions such as AIG, Bear Stearns, Dexia and Royal Bank of Scotland were all given some form of a bailout by their central banks in order to avert such events.
The problem of the too big to fail mentality is illustrated by AIG (American International Group Inc.) which had written16 insurance (credit derivatives) on a notional amount of around half a trillion dollars. AIG did not have to set aside capital or reserves and had limited collateral requirements. Counterparties were presumably happy to transact with AIG on the basis of their strong credit quality and the fact that collateral terms could be contractually tightened in the event of AIG experiencing credit quality deterioration. However, AIG suffered a $99.3 billion loss in 2008 and failed in September 2008 due to liquidity problems,17 causing the US Department of Treasury and Federal Reserve Bank of New York to arrange loans as support for a “too big to fail” institution. AIG required over $100 billion of US taxpayers' money to cover losses due to the excessive risk-taking. Monoline insurers collectively had a comparable exposure to AIG but, since this exposure was spread across a number of financial institutions, their failure was more palatable.
A stable derivatives market is not one heavily dominated by a few large institutions but rather a market with smaller institutions that can and will fail, but with less dramatic consequences. A financial system that is “safe to fail” is more readily achievable than one that is “failsafe”. Having too big to fail institutions such as large dealers, insurance companies and central counterparties is not ideal.18 However, policymakers and regulators seem to have accepted that this is an unavoidable consequence of global financial markets and such entities must then be regulated with extreme caution and given special status due to their “SIFI” (systemically important financial institution) status, which is a less crude way of saying too big to fail. A key reaction to the global financial crisis has been to mandate central clearing via central counterparties (CCPs). Yet, by its very nature, a CCP will be a SIFI.
SIFIs create moral hazard problems since they and their counterparties may behave less cautiously due to the implicit or explicit promise that they will always be supported in financial distress by their central bank. A key question for counterparty risk assessment is whether certain counterparties can be regarded to all intents and purposes as “risk-free”. It seems in the case of SIFIs that the answer is yes. However, one of the key lessons of the global financial crisis was that it was precisely these types of institutions that can be the most dangerous counterparties. Furthermore, it should not be assumed that a government-sponsored bailout would protect any counterparty in full. No counterparty should ever be regarded as risk-free.
2.4.5 Credit Derivatives
The credit derivatives market, whilst relatively young, has grown swiftly due to the need to transfer credit risk efficiently. The core credit derivative instrument, the credit default swap (CDS), is simple and has transformed the trading of credit risk. However, CDSs themselves can prove highly toxic since, whilst they can be used to hedge counterparty risk in other products, there is counterparty risk embedded within the CDS itself. The market has recently become all too aware of the dangers of CDSs and their usage has partly declined in line with this realisation. It is generally agreed that CDS counterparty risk poses a significant threat to global financial markets. Credit derivatives can, on the one hand, be very efficient at transferring credit risk but, if not used correctly, can be counterproductive and highly toxic.
One of the main drivers of the move towards central clearing of standard OTC derivatives is the wrong-way counterparty risk represented by the CDS market. Furthermore, as hedges for counterparty risk, CDSs seem to require the default remoteness that central clearing apparently gives them. However, the ability of central counterparties to deal with the CDS product, which is much more complex, illiquid and risky than other cleared products, is crucial and not yet tested.
2.5 Counterparty Risk in Context
2.5.1 The Rise of Counterparty Risk
Counterparty risk is traditionally thought of as credit risk between OTC derivatives counterparties. Since the global financial crisis, the importance of OTC derivatives in defining crises has made counterparty risk the key financial risk. Historically, many financial institutions limited their counterparty risk by trading only with the most sound counterparties. The size and scale of counterparty risk has always been important but has for many years been obscured by the myth of the creditworthiness of the “too big to fail” institutions such as those mentioned in Chapter 1. For many years, institutions ignored counterparty risk with high-quality (e.g., Triple-A) rated institutions, sovereigns, supranational or collateral posting entities. However, the financial crisis showed that these are often the entities that represent the most counterparty risk. The need to consider counterparty risk in all counterparty relationships and the decline in credit quality generally has caused a meteoric rise in interest in and around counterparty risk. Regulatory pressure has continued to fuel this interest. Whereas in the past, only a few large dealers invested heavily in assessed counterparty risk, it has rapidly become the problem of all financial institutions, big or small.
2.5.2 Counterparty Risk and CVA
Counterparty risk represents a combination of market risk, which defines the exposure, and credit risk, which defines the counterparty credit quality. A counterparty with a large default probability and a small exposure may be considered preferable to one with a larger exposure and smaller underlying default probability – but this is not clear. CVA puts a precise value on counterparty risk and can distinguish numerically between the aforementioned cases. CVA values the counterparty risk that an institution takes and potentially allows it to be traded (hedged).
Many banks essentially accounted for CVA many years prior to the global financial crisis in line with the common practice in taking “reserves” against potential future losses. Such reserves tend to be estimated based on historical data and by their nature did not change much from day to day. A CVA calculated in this way is to be interpreted as a statistical estimate of the expected future losses from counterparty risk. This treats CVA as a banking book item since it is not marked-to-market but rather estimated actuarially. CVA is analogous to a loan loss reserve, which aims to absorb the future potential credit risk losses on a loan book.
