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Etienne Koehler Barclays Capital CVA - VaR January 2012 Shahram Alavian Royal Bank of Scotland

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Presentation on theme: "Etienne Koehler Barclays Capital CVA - VaR January 2012 Shahram Alavian Royal Bank of Scotland"— Presentation transcript:

1 Etienne Koehler Barclays Capital CVA - VaR January 2012 Shahram Alavian Royal Bank of Scotland

2 Preliminaries Credit Value Adjustment (CVA ) Value at Risk (VaR) VaR for CVA The Proposed Approach VaR properties VaR and Monte Carlo simulation VaR for CVA Examples A non-linear payoff Application to Regulatory VaR Concluding Remarks Limits of the approach Challenges In Nutshell Statement Calculate VaR on CVA using a single batch run instead of multiple runs; once for each sensitivity. Benefit Faster execution of VaR Approach Insert a 1-day time horizon in the simulation timeline. Model the distribution of the change in value, at 1-day horizon, as a function of the underlying factors. Use this function to reprice the CVA changes in the same way a trades pricing formula is used to calculate its VaR. What we say …How we go about it …. 2

3 Credit Value Adjustment (CVA ) Value at Risk (VaR) VaR for CVA Preliminaries 3

4 Definition It is a Fair Value Adjustment to an OTC Trade. It is equivalent to the risk that a dealer has taken on its counterpartys credit by entering into an OTC trade Objective Accounting, risk mitigation, and regulatory Unilateral versus bilateral Management Accounting – Managing its volatility in the balance sheet. Risk Mitigation – Selling protection to other Proposed business functions (desks) Regulatory Capital – Minimizing the cost of regulatory capital Preliminaries - CVA Dealer Counterparty = Dealer Counterparty + Dealer Counterparty Defaultable Cash flow Default Free Cash flow Option to Default 4

5 Main Components of CVA Market component Credit component (including default) Cross gamma component Hedging CVA Inconsistencies in hedging from different objectives Hedging for Default? Hedging for Capital Hedging for volatility The hedged CVA book, П is Preliminaries – CVA Continued … 5

6 Value at Risk (VaR) Is A measure of unexpected loss due to market move. tool for calculating capital qualitative representation of the volatility of the balance-sheet. risk-limit measure for trading desks Sensitivity Based Sensitivities are sent to market risk department. VaR and its relevant break-down numbers are then returned. Not all sensitivities are included Historical Based Recent (3 year) daily time series stressed period Preliminaries – VaR 6

7 CVA Volatility leads to: CVA Capital CVA as a fair value adjustment brings volatility to the balance sheet For firms using unilateral CVA (no DVA), the unexpected loss due to a rise in credit spread of the market can be very large. Newly introduced regulatory CVA capital (BASEL III) Limit on CVA-VaR Level - how much? Bucket – Region, industry, currency, … Preliminaries – VaR for CVA 7

8 VaR properties VaR and Monte Carlo simulation VaR for CVA Proposed Approach 8

9 1.Instantaneous: It excludes the duration of the portfolio, omits any cash flow during the time horizon and limits the change of value to the instantaneous change in the underlying risk factors, only. 2.Conditional: It is conditional on its initial value at current time. 3.Functional: Similar to a pricing function, it is a function of the risk factors driving its value. This feature provides the means for generating VaR from any arbitrary distribution. Proposed Approach – VaR Properties 9

10 Monte Carlo Approach N risk factors are simulated under a joint process. For each path j and time t, we have (dependence on t is implied) Proposed Approach – VaR and Monte Carlo Valuation of an asset π conditional on each time t and path j will result Therefore, we have a distribution of π, as a function of its underlying risk factors, for each time t. Defining a 1-day Implied VaR. Calculate the conditional values of the asset at δ = 1-day time horizon for each path j. 10 In future slides, we replace π with expected exposure (EE) and CVA itself.

11 1. Create an Instantaneous Change Proposed Approach – VaR and Monte Carlo This is not an instantaneous change. Therefore, we pick up another path j. and create an instantaneous change by Calculate the change in values of the asset at 1-day time horizon for each path j. 2. Create a Conditional Change So we have a conditional change from j which given the above assumption, we a have conditional on spot. Having simulated a large number of paths, one can find a path for which <= This change is from any j to any j 11

12 3. Create a Functional Change Proposed Approach – VaR and Monte Carlo Assuming there exists a model, M, representing the conditional changes as a function of the changes in the risk factors with Using a linear regression model, for example, 12 Putting It All Together with

13 Proposed Approach – VaR for CVA Change of Value of a Hedged CVA Book Re-writing the above in the same notation as previous section Following our prescription, we need to calculate which we can do using a rollback method like Least Squared MC, under two different approaches. 13

14 Proposed Approach – Method I Joint Simulation of the Credit and Market Step-by-Step 14

15 Proposed Approach – Method II Simulation of the market, only CVA (bilateral case) 15

16 Proposed Approach – Method II Simulation of the market, only Change in CVA (bilateral case) 16

17 Proposed Approach – Method II Step-by-Step 17

18 A Power Option An Interest Rate Swap and Its Application to Regulatory VaR Examples 18

19 Examples – A Power Option Motivation For This Example To show the effectiveness of linear regression when modelling a non-linear trade Motivation For This Trade The power option is a highly non-linear trade with an analytical pricing formula. Setup Assumption Rates and Vols are not stochastic. Counterparty has sold us a put on its stock 19

20 Examples – A Power Option [ ρ = 0 ] For every strike, with ρ = 0, both S and H were simulated. First, the VaR using benchmark(BM) was produced by pricing the CVA with current market data and then pricing the CVA with each of the 750 scenarios. The VaR of this distribution, for this strike, produces one point under BM label. Separately, Method-I, and Method-II were used to calculate their corresponding VaR numbers. Each VaR makes a data point under the Method-I and Method-II labels, respectively. This process was then repeated for strikes ranging from 4,000 to 40,000. The longest part of the exercise was the generation of the VaR using BM, since it had to reprice the CVA 751 times for each strike value. 20

21 Examples – A Power Option [ ρ = -0.95 ] The same set of simulations was repeated, for the same range of strikes, using ρ =-0.95, incorporating a large WWR. In this case, however, only Method-I was calculated. This figure illustrates the effect of WWR to VaR using Method-I. 21

22 Examples – An Interest Rate Swap Motivation For This Example Sensitivity of the approach to various volatility values To compare the BASEL –III CVA regulatory capital using method-I. Motivation For This Trade Trades value can be negative creating the exposure non-linear and making the rollback process challenging. Setup Assumption Current period is also the stress Period There are no hedging instruments. 22

23 Examples – Effect of Volatility on Approach Since the regulatory VaR does not take any contribution from the un-hedged exposure variations, the first test would be to compare the credit component of the VaR for various historical spread volatilities. This is done by generating a time series for H while keeping the historical volatility of the rates to zero. The results are then compared with the regulatory VaR. Using the regulatory VaR as the benchmark, one can also observe that the VaR methodology proposed here can generate, from a single implied volatility of 20%, the correct VaR for different historical volatilities generated from various time series. 23

24 Examples – Regulatory VaR and WWR The next objective is to compare the regulatory and the proposed VaR for a given historical volatility of market (both, hazard rate and the rates) and for various correlation. This is done by performing a Monte Carlo simulation for each level of ρ, generating the time series matching the corresponding correlation and calculating the VaR as prescribed in method-I. Since the regulatory VaR depends only on the historical volatility of the hazard spread, it produces a flat line. Note that for ρ=0, method-I includes variations in the market component and regulatory VaR does not. 24

25 Concluding Remarks Limits of the Approach Implied Volatility – In cases where rollback methods were used to obtain the conditional prices of option, there will be no volatility to regress against. Challenges A Robust rollback algorithm – Obviously, there is no analytical formula to calculate the conditional CVA or exposures. This means we need a robust rollback method in order to obtain convergent conditional values. Nonlinearity of the exposures – Even when using a rollback one still needs a substantial number of paths for the exposures and CVA values to converge as they are highly nonlinear. A different VaR platform – Almost in all cases, all desks use the same VaR platform. This method now requires a different one. 25

26 Questions and Comments Thank you 26

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