Kenyon_December2013_Slides
Transcription
Kenyon_December2013_Slides
Regulatory Challenges to Mathematical Finance Chris Kenyon 12.12.2013 London Quantitative Finance Seminar Contact: [email protected] Acknowledgments & Disclaimers This presentation is based on joint work with Andrew Green. The views expressed in this presentation are the personal views of the speaker and do not necessarily reflect the views or policies of current or previous employers. Not guaranteed fit for any purpose. Use at your own risk. , 2/62 Contents Introduction No Risk-Neutral Measure Risky Markets and Derivative Holding Costs Step 1. Risky Markets are Incomplete Step 2. Desks are Inherently Risky Step 3. Desks’ Risk Quantification Step 4. Funding is a Separate Risk from Capital Step 5. Costs Set by Desk Risk Summary Regulatory-Optimal Funding Regulations Funding Optimization Results Summary Conclusions Bibliography Appendix Introduction No Risk-Neutral Measure Risky Markets and Derivative Holding Costs Regulatory-Optimal Funding Conclusions Bibliography Appendix Introduction I Regulators clearly believe that derivatives can never be risk free I I I Burst of regulation after the 2008 financial crisis slowing? I I I I , Already: Basel III officially starting Jan 1, 2014 (EU, US) Soon: Leverage Ratio; Liquidity Coverage Ratio Prudent Valuation (EBA 2013), Non-IMM proposal (BCBS-254 2013) Potential replacement of VaR by Expected Shortfall (BCBS-219 2012) now uncertain because not elicitable. Direct effect on bottom line, what effect on theory? 5/62 Steps I Desks always risky ⇒ Strictly positive Capital cost I I I I I I Holding costs for long positions 6= holding costs for short positions Incomplete-markets (Cerný 2009; Kaido and White 2009) Holding costs (Tuckman 1992), not transaction costs (Kabanov and Safarian 2010) Idiosyncratic effects ⇒ no Risk-Neutral Measures I I , Have to recover Institutional Costs (facilities, personnel) Regulations ⇒ Holding Costs ⇒ Incomplete Market Trading difficulties, potentially empty markets (seen before (Jouini, Koehl, and Touzi 1997)) Also implied by arbitrage (Shreve 2004) 6/62 Introduction No Risk-Neutral Measure Risky Markets and Derivative Holding Costs Regulatory-Optimal Funding Conclusions Bibliography Appendix Risk-Neutral Measure Definition Let P be the physical measure, then a probability measure Q is said to be risk-neutral if: (i) Q and P are equivalent; (ii) under Q discounted stock prices are martingales. , I (Shreve 2004) shows that if the market price of risk equations cannot be solved then there is arbitrage assuming that the cost of 1 unit of stock is exactly the negative of -1 units of stock. I Not true here because holding costs are present whether the position is short or long. 8/62 Theorem 1: No Risk-Neutral Measure Theorem If each market participant has different idiosyncratic continuous dividends when holding the same stock then there is no risk-neutral measure. Proof. Obvious. Let the stock price, from the point of view of market participant i, be: P dSi (t) = (µi + ai )Si (t)dt + σSi (t)dW i (t) where ai is the objective dividend received by market participant i, and µi is the P drift believed by market participant i. This implies that in the idiosyncratic risk-neutral measure of i, the evolution of the stock price is: Q dSi (t) = (r + ai )Si (t)dt + σSi (t)dW i (t) where r is the riskless rate. The P drifts of the market participants have been replaced by the riskless rate, but dividends are unchanged because they are objective although idiosyncratic. Hence there is no risk neutral measure because the rates of return are different for each participant (under each participants’ risk neutral measures). , 9/62 Idiosyncratic Effects I If dividends were not idiosyncratic then all participants would see the same risk-neutral measure I Usually the Girsanov transformations are idiosyncratic but the final measure is common Capital requirements on derivative desks are different for different banks, thus Theorem 1 is applicable I I I I , Market Risk capital consists of a VaR part and a Stressed VaR part where the stressed period is determined at the bank level Expect different banks to have different stress periods, e.g. Greek banks versus US banks Systematically Important Banks have different capital requirements 10/62 Introduction No Risk-Neutral Measure Risky Markets and Derivative Holding Costs Step 1. Risky Markets are Incomplete Step 2. Desks are Inherently Risky Step 3. Desks’ Risk Quantification Step 4. Funding is a Separate Risk from Capital Step 5. Costs Set by Desk Risk Summary Regulatory-Optimal Funding Conclusions Bibliography Appendix Risk Transfers DVA FVA (c/p) Capital (c/p) Desk Bank Risk Desk Risk Counterparty Capital (desk) FVA (desk) CVA Bank Systematic Risks Figure: Risk transfers between a trading desk and its parent bank, and between the desk and its counterparties. , 12/62 Step 1. Risky Markets are Incomplete Base case: back-to-back collateralized interest rate swaps (IRS). Suppose IRS identical IRS except for opposite directions. The desk will leak PnL due to the following. I Counterparty Credit Risk (CCR) capital on margin periods of risk I Institutional Costs (IC), i.e. facilities and personnel, etc. I Leverage Ratio (LR) capital I Initial Margins (IM) require funding (plus possible Default Fund contributions) Desk needs (small) arbitrage to cover costs, or close. I Desk requires competitive advantage, its business model. I With sufficient flow, swap terms may remain closely matched (within 15bps) so no Market Risk capital requirement, (BCBS-128 2006) paragraph 718(xiv). Desk risk is from business model robustness for ongoing trading. Market incomplete and capital costs strictly positive. , 13/62 Swap Edge Case I I I , Desk enters collateralized swap + uncollateralized swap Enters collateralized CCDS on swap c/p at zero cost Term repos uncollateralized swap+CCDS as package; puts extra into riskless account Effects I Repo provides collateral for collateralized swap and vice versa I Uncollateralized swap+extra pay CCDS premia and repo rate I If CCDS spread >15bps, Market Risk capital on both swaps I Initial Margin(s) requires funding I Place extra funding from repo in riskless account (assume pays repo) and pass back I Collateralized CCDS, economic risk on CCDS jump-to-default I Counterparty Credit Risk capital for margin period of risk I Economic risk from uncollateralized swap default time I Institutional Costs; Leverage Ratio capital Outcome I Desk leaks PnL 14/62 Edge Case Details Assumptions I no bond CDS, or swap CCDS basis I no capital relief priced into CDS, CCDS (Kenyon and Green 2013a) I no repo haircuts Notes , I Default times creates PnL volatility because different numbers of protection coupons paid, and replacement swap not ATM I CDS/CCDS hedges not cashflow-replicating I Economic capital is required by the bank covering PnL volatility 15/62 Edge Cases: Consequences I I I Market is incomplete from holding costs (non-zero cost capital, IM, IC) Holding costs idiosyncratic from Market Risk Stressed VaR period, and capital portfolio effects (at least) Market has no risk-neutral measure because holding costs idiosyncratic No funding yet → introduce haircuts on repos. , 16/62 Generalizing: Repo Haircuts , I Regulations (BCBS-189 2011; BCBS-193 2011) have requirements before a repo has zero haircut I Double-default risk, and margin period of risk, mean non-zero haircuts rational on packages I Funding buffers are costs. Basel III has the Liquidity Coverage Ratio (30 days funding) and UK regulations have Individual Liquidity Guidance (at least two weeks). I Funding buffer costs give a floor to the funding charge from the bank to desks (if not seen as funding risk-reducing — possibly repo desks). 17/62 Repo Haircut Effects Bond I Known finite limit on funding quantity I Bank charges funding to desk at the funding risk of the desk’s business model, floored by the price of funding risk Swap: receive fixed on haircut fraction I Known finite limit on funding quantity I Bank charges funding to desk at the funding risk of the desk’s business model, floored by the price of funding risk Swap: receive floating, e.g. 3M EUR, on haircut fraction , I Potentially unlimited funding requirements I No limit on potential funding costs I Bank charges funding to desk at the funding risk of the desk’s business model, including market impact of different funding levels, floored by the price of funding risk 18/62 Generalizing: Limited CDS and CCDS I Counterparty Credit Risk capital increases from the margin period of risk to the maturity of the trades (regulatory details important for caps) I Desk must now warehouse credit risk, hence require Economic capital for increased PnL volatility I Increased repo haircuts (with usual credit risk on haircut amount) Major qualitative difference is point on PnL volatility: “market can remain irrational longer than you can remain solvent”. , 19/62 Edge Case Desk PnL PROFIT LOSS Capital – known Institutional Costs – known Funding – sometimes unknown I When there are no bond-CDS, or swap-CCDS bases, PnL is negative I Default timing produces unfinessable PnL volatility requiring Economic capital I Market Risk, CCR, IM, unavoidably costly I Non-zero haircuts produce unfinessable losses conditional on survival1 1 Absent controllable recovery rates on own-issued bonds, which are problematic even in theory. , 20/62 Theorem 2: NMPwHC Theorem (No Market Positions with Holding Costs) If a market has assets with zero holding costs, and assets with strictly positive holding costs, then there are no positions in assets with positive holding costs. Proof. Trivial. Assuming economic agents are value maximizing, they will never hold positions that lose money if there is an alternative. I Markets with holding costs have familiar examples in commodity markets with storage costs (e.g. gas). It is no accident that usual storage-cost-positive commodity strategies involve not holding physical commodity positions, i.e. futures contracts. I Here markets are different: participants can only hold physical positions. , 21/62 Corollaries Corollary (No Complete Market with Holding Costs) A complete market with holding costs is empty. Corollary (Capital Market Empty) Any market with mandatory capital requirements and positive capital costs is empty. Corollary (Institutional Market Empty) Any market with institutional costs is empty. Corollary (Funding Market Empty) Any market with funding requirements is empty. Corollary (Risky Market Empty) Any risky market with positive capital costs is empty. These corollaries derive from Theorem 1 [NMPwHC] and our analysis of edge cases. , 22/62 Desks are Inherently Risky I Risky markets with capital and institutional markets are incomplete, and theoretically empty I Therefore a desk can be a riskless portfolio only if it is empty I Hence all existing desks are risky This last statement will appear blindingly obvious to all practitioners2 . However, it is necessary to provide the theoretical background in order to place FVA on a sound academic footing. 2 Check , Dilbert. 23/62 Trading Desks are Businesses I I If value-maximizing agents never trade how do desks exist? Need additional assumptions about competitive advantages I I I I , Skills Barriers to entry: reputation; networks; systems; legal and regulatory approvals Rents, e.g. from monopoly position (only dealer in x) Synergy with existing organization I These competitive advantages form a business model I The business model will be more, or less, risky depending on the robustness of the business model 24/62 Counterparties do not Face Desk Risks I I Executives set up new desks, or close existing ones, with changes in economic conditions Many examples 2000-6 and 2008-onwards I I I I I Structured credit desks post-2008; structured credit desks 2013 Fixed Income: “planned to eliminate most of its fixed-income businesses because they had become unprofitable.” Commodities: “The bank is in the process of selling its physical commodity arm in the face of rising regulatory pressure” Wealth: “will stop offering wealth management services in about 130 countries by 2016” Usually a desk can be closed without the bank failing Desk risk 6= Bank risk I , Desks must nevertheless pay the bank for the risks the desks take on 25/62 Aside: FVA vs DVA I When a desk is the whole bank then these have incomplete overlap I I I , Incomplete because funding is a separate risk to credit (aka capital) within business model — see later Usual to analyze risky projects as stand-alone entities within firm Paradoxical implication is that interaction effects dominate stand-alone-within-firm calculation 26/62 Desks’ Risk Quantification DVA FVA (c/p) Capital (c/p) Desk Bank Risk Desk Risk Counterparty Capital (desk) FVA (desk) CVA Bank Systematic Risks I I Desk risk comes from the robustness of their business models Observable risk components (not comprehensive): I I I , Institutional costs Funding requirements (create PnL leaks, and may be unlimited) Capital requirements 27/62 Funding is a Separate Risk to Capital: PRA Liquidity Coverage Ratio coming soon. , 28/62 Contingent Forward Funding is a Tesseract Dimensions I Expiry — (up to) when the funding is required I Tenor — for (up to) how long I Rate — at what price I Volume — (up to) how much Data Sources , I Basis swaps give forward Libor funding for up to 12M I Longer forward maturities: issued callable bonds; forward CDS spreads 29/62 Minimum funding costs I I Current work (Burgard and Kjaer 2011; Burgard and Kjaer 2012; Morini and Prampolini 2011; Hull and White 2012) typically takes funding as a curve, as an input, and constant. (Piterbarg 2012) is one exception w.r.t. collateral and (Pallavicini, Perini, and Brigo 2012) another w.r.t liquidity. Theoretical approaches unworkable because: I I I , market incompleteness do not include tenor-specific funding optimization (PIDE) do not include capital costs of funding I Practically set by an optimal funding strategy (Kenyon and Green 2013b) for the bank I If the desk is the same as the bank then this will set minimum on desk funding costs, because counterparty trade creates at least this cost (and hence risk) to the desk. 30/62 Step 5. Costs Set by Desk Risk I I As a risky project a desk must pay for its risks Because the desk counterparties create the desk risks, they must pay for those risks — despite symmetry, this is a condition for trading I , Like a CDS — protection buyer pays I A desk need not pay for non-desk risks I Like a desk, a bank must have competitive advantages to be viable. The first of which is a banking license. I In as much as the bank and its counterparty face the same systematic (correlation=1) risks these will not be priced in as there is no risk transfer 31/62 Marking to Market “Uncollateralized Market Price” is an Oxymoron , I An uncollateralized trade builds in the risks of both counterparties therefore it is not fungible I “uncollateralized market price” of a trade is an oxymoron, because this market can only ever involve the two original counterparties I There is no market price for such transactions because there is no market 32/62 Risky Markets Summary I Theoretically Capital is a real cost to desks I I I Theoretically Funding is a real cost to desks I I I I , Capital is legally required Desks are inherently risky because they depend on their business model to recover PnL leaks IM legally required (now or soon depending on setup) Repos are usually required to have non-zero haircuts Funding is a separate risk to those covered by capital, and this is enforced via liquidity buffers Funding risk of a desk >> default risk to counterparty; i.e. interaction effects dominate stand-alone analysis 33/62 Introduction No Risk-Neutral Measure Risky Markets and Derivative Holding Costs Regulatory-Optimal Funding Regulations Funding Optimization Results Summary Conclusions Bibliography Appendix Regulatory-Optimal Funding I Gaps in funding theory for pricing — and between theory and practice I I I Regulatory limits on funding strategies Practical funding to address data and market issues I I Methodology evaluation using standard machine learning statistical machinery Optimizing funding parameters using P and Q I I , Current work (Burgard and Kjaer 2011; Burgard and Kjaer 2012; Morini and Prampolini 2011; Hull and White 2012) typically takes funding as a curve, as an input, and constant. (Piterbarg 2012) is one exception w.r.t. collateral. Q is risk neutral measure (i.e. market implied) P is physical measure 35/62 Should you hedge funding costs? Recall discussion on input costs in (Hull 2011): 1. Assume that different banks have different (funding) costs 2. Bank A hedges its (funding) costs 3. General market climate improves and the systematic part of funding costs decreases for all banks 4. Treasurer of Bank A must now explain to the CEO and the Board why Bank A is losing money relative to its competitors when offering similar prices for similar products Thus (funding) hedging decisions should always be at Board level , 36/62 Funding: Theory vs Practice I Cost of funding? Ask Treasury. I Where does Treasury get the cost from? Let’s assume that Treasury can borrow at Libor flat I I I I , May be O/N + credit + liquidity, but this is sufficient for now as a model for a ”typical major bank” Some cash may be at repo, e.g. Gilts, but not for derivatives ... unless these are repackaged into a repo’able security via internal trades Is this the cost of funding? 37/62 PRA Liquidity Stress , 38/62 Model Setup I , Funding volume is always the same whatever the funding roll 39/62 I In units of ∆ the number of times funding must be rolled to horizon hn with roll length αn , nrolls (hn , αn ) is: ( 0 nrolls (hn , αn ) = l hn −αn m αn −1 αn ≥ hn otherwise (1) where: αn = α/∆; hn = h/∆. I Thus the gross excess funding Gef will be as a percentage: Gef = 100 × , nrolls hn (2) 40/62 Average Expected Undiscounted Cost Use the expected undiscounted funding cost C av as our primary metric because this is often used in practice C av t (α, h) = 1 ‡ E min(α, h)Ft (t, t + min(α, h)) h t + nrolls X − ϕ∆Ft (t + i(α − ∆), t + ∆ + i(α − ∆)) i=1 + min(α, h − i(α − ∆))Ft (t + i(α − ∆), t + min(i(α − ∆) + α, h)) (3) Where: Ft (t1 , t2 ) is the forward rate as seen from t between t1 and t2 ; if nrolls < 1 then there are no terms in the summation; ‡ measure used in the expectaion (P, Q, or some combination). , 41/62 Optimization Problem I myopic version of our optimization problem based on Equation 3 is: C av t,opt (h) = min C av t (α, h) (4) I myopic as we are only permitted to chose α once, at the start. I Equation 4 is a non-linear, non-convex, optimization problem because of the minimum terms in Equation 3. I To get some idea of solution character we assume a linear (continuously compounding) yield curve y (T ) with constants a, b: α y (T ) = a + bT , 42/62 Optimal Q Funding Proposition Given a linear input yield curve (a > 0, b) the regulatory-optimal Q-funding strategies with horizon h are: , I a > 0, ϕ = 1: all funding strategies are equivalent; I a > 0, a + bh > 0, ϕ < 1: Term funding; I a > 0, a + bh < 0, ϕ < 1: Shortest possible. 43/62 Optimal P Funding Proposition Given a linear input yield curve (a > 0, b) the regulatory-optimal P-funding strategies where P=CONSTANT yield curve, with horizon h are: , I a > 0, b > 0, ϕ = 1: Shortest possible; I a > 0, b > 0, ϕ < 1: Neither the shortest nor Term funding are always optimal; I a > 0, b < 0: Term funding; 44/62 P-optimal vs Q-optimal , I Strikingly, the Q-optimal and P-CONSTANT-optimal strategies are almost opposite. I Most realistic case is perhaps where there is a bid-ask spread and then the P-CONSTANT-optimal strategy is intermediate. I P-CONSTANT-optimal strategy is not even present within the Q-optimal strategies. I CONSTANT case is not as restrictive as it may appear. Really comparing baseline change with the differential of the yield curve. 45/62 1Y Spot Deposit Curves BP EU 0.10 0.05 0.08 0.04 0.06 0.03 0.04 0.02 0.02 0.01 1995 2000 2005 2010 0.00 1995 2000 JP 2010 2005 2010 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.05 0.04 0.03 0.02 0.01 0.00 2005 US 1995 2000 2005 2010 1995 2000 . , 46/62 Optimal Funding Costs? Theory and Practice I I Benchmark = expected value of perfect information (EVPI) If you know the future what do you do? I I I Compare with I I , Still constrained by PRA liquidity rules Can only trade on spot funding curve Hedging Assumptions (models) of future funding curve behavior 47/62 Machine Learning Statistical Machinery Figure: Standard setup. We will identify the best funding curve predictor from standard choices. Basically calibrate g() on next slide. , 48/62 Funding Curves I Q : Risk Neutral yRN (t0 , tf , τ ) = yHistorical (t0 , τ ) dtRN (t0 , tf , τ ) = τ I EVPI yEVPI (t0 , tf , τ ) = yHistorical (tf , τ − (tf − t0 )) dtEVPI (t0 , tf , τ ) = τ − (tf − t0 ) I P : Constant yConstant (t0 , tf , τ ) = yHistorical (t0 , τ − (tf − t0 )) dtConstant (t0 , tf , τ ) = τ − (tf − t0 ) I P : Projected Base Rate , yPBR (t0 , tf , τ ) = yHistorical (t0 , τ − (tf − t0 )) + g(t0 )(tf − t0 ) dtPBR (t0 , tf , τ ) = τ − (tf − t0 ) 49/62 Base Rate Predictors I I First model (almost) always Exponentially Weighted Moving Average (EWMA): choose decay parameter λ Refinements: I I I I , Limit gradient so that projected base rate does not go negative at horizon Apply threshold to calculated gradient: θ Weight gradient between 0% and 100% of value: ω Benchmark against EVPI 50/62 Setup , I Four major currencies analyzed together: BP, EU, JP, US I Minimum buffer ∆ = 1/12 I Horizon h = 1 I Bid-ask parameter ϕ = 0.75 51/62 Model Calibration & out-of-sample Performance I Best parameter set was: (λ, θ, ω) = (90days, 0.005, 0.3) I Average performance in bps funding cost over horizon h = 1Y (bps) Q vs EWMA T-Test EWMA vs EVPI %-efficient I , BP 13 E-25 16 44% EU 22 E-27 15 59% JP 10 E-45 4 71% US 19 E-37 15 56% Out-of-sample model test achieves significant gains versus hedging over test period (series lifetime minus 5 years = calibration/selection period). 52/62 Summary I Derivative funding requires forward funding surface, and forward funding volatility tesseract: these pose practical issues I PRA and Basel III liquidity requirements change funding costs Regulatory-optimal funding in can be run as a machine-learning-type exercise to avoid practical market data and completeness issues I I I , Out-of-sample EWMA-based funding optimization achieved 44% to 71% of possible gains to EVPI from Q-hedging T-Test levels of p < 0.00001 significance versus Q-hedging. 53/62 Introduction No Risk-Neutral Measure Risky Markets and Derivative Holding Costs Regulatory-Optimal Funding Conclusions Bibliography Appendix Conclusions , I Regulations have costly, asymmetric, and idiosyncratic effects: this mean that market participants see no common risk-neutral measure I Desks are risky and rely on competitive advantage to price in the costs of their risks which include funding (buffers + options) and capital I Funding, as an input cost, is a relatively unexplored area where there is great potential 55/62 Introduction No Risk-Neutral Measure Risky Markets and Derivative Holding Costs Regulatory-Optimal Funding Conclusions Bibliography Appendix BCBS-128 (2006, June). International Convergence of Capital Measurement and Capital Standards. Basel Committee for Bank Supervision. BCBS-189 (2011). Basel III: A global regulatory framework for more resilient banks and banking systems. Basel Committee for Bank Supervision. BCBS-193 (2011, July). Revisions to the Basel II market risk framework: updated as of 31 December 2010. Basel Committee for Bank Supervision. BCBS-219 (2012). Fundamental review of the trading book — consultative document. Basel Committee for Bank Supervision. BCBS-254 (2013). The non-internal model method for capitalising counterparty credit risk exposures - consultative document. Basel Committee for Bank Supervision. Burgard, C. and M. Kjaer (2011). Partial differential equation representations of derivatives with bilateral counterparty risk and funding costs. The Journal of Credit Risk 7, 75–93. Burgard, C. and M. Kjaer (2012). Generalised CVA with Funding and Collateral via Semi-Replication. SSRN. Cerný, A. (2009). Mathematical Techniques in Finance: Tools for Incomplete Markets, (2nd edition). New York: Princeton University Press. EBA (2013). On prudent valuation under Article 105(14) of Regulation (EU) 575/2013. Technical report, European Banking Authority. EBA-CP-2013-28. Hull, J. (2011). Options, Futures and Other Derivatives, 8th Edition. New Jersey: Prentice Hall. Hull, J. and A. White (2012). Is FVA a Cost for Derivatives Desks? Risk 25(9). Jouini, E., P.-F. Koehl, and N. Touzi (1997). Incomplete markets, transaction costs and liquidity effects. European Journal of Finance 3, 325–347. Kabanov, Y. and M. Safarian (2010). Markets with transaction costs. Springer. , 57/62 Kaido, H. and H. White (2009). Inference on Risk Neutral Measures for Incomplete Markets. Journal of Financial Econometrics 7(3), 199–246. Kenyon, C. and A. Green (2013a). CDS pricing under Basel III: capital relief and default protection. Risk 26(10). Kenyon, C. and A. Green (2013b). Regulatory-Optimal Funding. arXiv . http://arxiv.org/abs/1310.3386. Morini, M. and A. Prampolini (2011). Risky Funding: A Unified Framework for Counterparty and Liquidity Charges. Risk 24(3). Pallavicini, A., D. Perini, and D. Brigo (2012). Funding, Collateral and Hedging: uncovering the mechanics and subtleties of funding valuation adjustments. SSRN. http://ssrn.com/=2161528. Piterbarg, V. (2012). Cooking with collateral. Risk 25(8), 58–63. Shreve, S. (2004). Stochastic Calculus for Finance, Volume II: Continuous-Time Models. Springer-Verlag. Tuckman, B. (1992). Arbitrage with holding costs: a utility-based approach. Journal of Finance 47(4). , 58/62 Applied Quantitative Finance The credit and sovereign debt crises have fundamentally changed the way participants in the global financial markets perceive credit risk. In market practice this is most directly visible from significant bases throughout the interest rate world, especially tenor bases, crosscurrency bases, and bond-cds bases. This means that the curve used for discounting is no longer the curve used for Libor (aka Fixing Curve or Forwarding Curve). In the last two years a consensus has emerged that this multi-curve pricing is now standard. Discounting, Libor, CVA and Funding: Interest Rate and Credit Pricing is the first book to illustrate new ways of pricing interest rate and credit products in the post-crisis markets. Written by two seasoned practitioners, it will enable the readers to understand the many different versions of credit and basis spreads, and to build the appropriate discount curves that take the these spreads into account so that collateralized derivatives will be priced correctly. The authors guide the reader through the complexity added by OIS discounting and multi-curve pricing as well as CVA, DVA and FVA. Derivatives do not exist in a vacuum. Regulators world-wide have reacted strongly to the crises with the introduction of Basel III. Hitherto quants could ignore capital costs and charges, but as of January 2013 this world is gone. Discounting, Libor, CVA and Funding explains details of Basel III that are important for pricing, especially around the CVA VaR and default exposure capital charges. 90101 9 781137 268518 www.palgrave.com Printed in Great Britain ISBN 978-1-137-26851-8 DISCOUNTING, LIBOR, CVA AND FUNDING Interest Rate and Credit Pricing Chris Kenyon Roland Stamm This book will be required reading for quantitative practitioners who need to keep up-to-date with the latest developments in derivatives pricing, and will also be of interest to academic researchers and students interested in how instruments are priced in practice. DISCOUNTING, LIBOR, CVA AND FUNDING The crises have also altered the perception of banks and governments – they are no longer regarded as zero-risk counterparties. Now both sides of an uncollateralized trade need to consider, and price in, the risk that the other defaults: my CVA is your DVA. Even collateralization does not remove pricing problems: when you post collateral how much do you have to pay for it? This FVA is not symmetric in many ways: whatever it costs you to source it, your counterparty will only pay you OIS. Even worse is that your funding costs are unlikely to be the same as those of all your counterparties. Dr. Chris Kenyon (London, UK) is a Director at Lloyds Banking Group in the front office Quantitative Research ñ CVA / FVA group. Previously he was head quant for counterparty risk at Credit Suisse, and at DEPFA Bank PLC he was Head of Structured Credit Valuation (post crisis), working on pricing model development and validation, and market risk. He has also held positions at IBM Research, and Schlumberger where he applied real options pricing to everything from offshore rig lease extension options to variable volume outsourcing contracts. Chris holds a PhD in Applied Mathematics from Cambridge University where he was a Research Fellow (Computer Modeling), and an MSc in Operations Research from the University of Austin, Texas. He is a regular writer and conference speaker, his papers have appeared in Quantitative Finance, Risk Magazine, Operations Research, IEEE Computer amongst others, and presented at academic conferences and industry meetings including those organized by Bachelier Finance Society, WBS, Marcus Evans, Risk Magazine, and many more. Dr. Roland Stamm is Head of Risk Methods and Valuation at HRE Group (formerly DEPFA Bank), where he is responsible (among other things) for the development of new pricing models, model set up, validation and calibration, CVA adjustments and market risk methodology. He was previously Head of Valuation at HRE Group, and has also held positions as Head of Market Risk Products, Head of IT Development and Project Manager, all at DEPFA Bank. He holds a PhD in Mathematics (Algebraic Topology) from the Westfälische Wilhelms-Universität, Münster where he was awarded a magna cum laude for his thesis The K- and L- Theory of Certain Discrete Groups, and received a master’s degree in Mathematics from the Johannes-GutenbergUniversität, Mainz. Chris Kenyon Roland Stamm 20/06/2012 11:01 , 59/62 Thanks for your attention — questions? , 60/62 Introduction No Risk-Neutral Measure Risky Markets and Derivative Holding Costs Regulatory-Optimal Funding Conclusions Bibliography Appendix Appendix: Bond Edge Case Desk purchases single Z-rated floating-rate bond (rate includes CDS cost) I Enters collateralized CDS on bond at zero cost I Term repos bond plus CDS as package; pays for bond; puts extra into riskless account Effects I Bond coupons+extra pay CDS premia and repo rate I Bond+CDS repo has no Market Risk, no CVA Risk I Collateralized CDS (designated CVA hedge so no Market Risk) has economic risk from jump-to-default of CDS c/p I Counterparty Credit Risk capital for margin period of risk on collateralized trades and repo I Economic risk from bond default time I Institutional Costs; Leverage Ratio capital Outcome I With no bond CDS basis, desk leaks PnL I , 62/62