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Break Even Volatilities Dr Bruno Dupire Dr Arun Verma Quantitative Research, Bloomberg LP 13 Break Even Volatilities Dr Bruno Dupire Dr Arun Verma Quantitative Research, Bloomberg LP 13 th Nov, 2007 King’s College, London

Theoretical Skew from Prices ? => Problem : How to compute option prices on Theoretical Skew from Prices ? => Problem : How to compute option prices on an underlying without options? For instance : compute 3 month 5% OTM Call from price history only. 1) Discounted average of the historical Intrinsic Values. 2) Bad : depends on bull/bear, no call/put parity. 2) Generate paths by sampling 1 day return re-centered histogram. 3) Problem : CLT => converges quickly to same volatility for all strike/maturity; breaks auto-correlation and vol/spot dependency. 13 th Nov, 2007 King’s College, London

Theoretical Skew from Prices (2) 3) Discounted average of the Intrinsic Value from re-centered Theoretical Skew from Prices (2) 3) Discounted average of the Intrinsic Value from re-centered 3 month histogram. 4) Δ-Hedging : compute the implied volatility which makes the Δhedging a fair game. 13 th Nov, 2007 King’s College, London

Theoretical Skew from historical prices (3) How to get a theoretical Skew just from Theoretical Skew from historical prices (3) How to get a theoretical Skew just from spot price history? S Example: K 3 month daily data t 1 strike – a) price and delta hedge for a given within Black-Scholes model – b) compute the associated final Profit & Loss: – c) solve for – d) repeat a) b) c) for general time period and average – e) repeat a) b) c) and d) to get the “theoretical Skew” 13 th Nov, 2007 King’s College, London

Zero-finding of P&L 13 th Nov, 2007 King’s College, London Zero-finding of P&L 13 th Nov, 2007 King’s College, London

Strike dependency • Fair or Break-Even volatility is an average of returns, weighted by Strike dependency • Fair or Break-Even volatility is an average of returns, weighted by the Gammas, which depend on the strike 13 th Nov, 2007 King’s College, London

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Alternative approaches Shifting the returns A simple way to ensure the forward is properly Alternative approaches Shifting the returns A simple way to ensure the forward is properly priced is to shift all the returns, . In this case, all returns are equally affected but the probability of each one is unchanged. (The probabilities can be uniform or weighed to give more importance to the recent past) 13 th Nov, 2007 King’s College, London

Alternative approaches Entropy method • For those who have developed or acquired a taste Alternative approaches Entropy method • For those who have developed or acquired a taste for equivalent measure aesthetics, it is more pleasant to change the probabilities and not the support of the measure, i. e. the collection of returns. This can be achieved by an elegant and powerful method: entropy minimization. It consists in twisting a price distribution in a minimal way to satisfy some constraints. The initial histogram has returns weighted with uniform probabilities. The new one has the same support but different probabilities. • However, this is still a global method, which applies to the maturity returns and does not pay attention to the sub period behavior. Remember, option pricing is made possible thanks to dynamic replication that grinds a global risk into a sequence of pulverized ones. 13 th Nov, 2007 King’s College, London

Alternate approaches: Fit the best log-normal 13 th Nov, 2007 King’s College, London Alternate approaches: Fit the best log-normal 13 th Nov, 2007 King’s College, London

Implementation details Time windows aggregation • The most natural way to aggregate the results Implementation details Time windows aggregation • The most natural way to aggregate the results is to simply average for each strike over the time windows. An alternative is to solve for each strike the volatility that would have zeroed the average of the P&Ls over the different time windows. In other words, in the first approach, we average the volatilities that cancel each P&L whilst in the second approach, we seek the volatility that cancel the average P&L. The second approach seems to yield smoother results. Break-Even Volatility Computation • The natural way to compute Break-Even volatilities is to seek the root of the P&L as a function of. This is an iterative process that involves for each value of the unfolding of the delta-hedging algorithm for each timestep of each window. • There alternative routes to compute the Break-Even volatilities. To get a feel for them, let us say that an approximation of the Break -Even volatility for one strike is linked to the quadratic average of the returns (vertical peaks) weighted by the gamma of the option (surface with the grid) corresponding to that strike. 13 th Nov, 2007 King’s College, London

Strike dependency for multiple paths 13 th Nov, 2007 King’s College, London Strike dependency for multiple paths 13 th Nov, 2007 King’s College, London

SPX Index BEVL <GO> 13 th Nov, 2007 King’s College, London SPX Index BEVL 13 th Nov, 2007 King’s College, London

New Approach: Parametric BEVL • Find break-even vols for the power payoffs • This New Approach: Parametric BEVL • Find break-even vols for the power payoffs • This gives us the different moments of the distribution instead of strike dependent vol which can be noisy • Use the moment based distribution to get Break even “implied volatility”. • Much smoother! 13 th Nov, 2007 King’s College, London

Discrete Local Volatility Or Regional Volatility 13 th Nov, 2007 King’s College, London Discrete Local Volatility Or Regional Volatility 13 th Nov, 2007 King’s College, London

Local Volatility Model GOOD Given smooth, arbitrage free , there is a unique (r=0) Local Volatility Model GOOD Given smooth, arbitrage free , there is a unique (r=0) Given by BAD • Requires a continuum of strikes and maturities • Very sensitive to interpolation scheme • May be compute intensive 13 th Nov, 2007 King’s College, London :

Market facts 13 th Nov, 2007 King’s College, London Market facts 13 th Nov, 2007 King’s College, London

S&P Strikes and Maturities 13 th Nov, 2007 King’s College, London Jun 09 Mar S&P Strikes and Maturities 13 th Nov, 2007 King’s College, London Jun 09 Mar 09 Dec 08 Jun 08 Mar 08 Dec 07 Aug 07 Sept 07 Oct 07 K T

Discrete Local Volatilities Price at T 1 of : Can be replicated by a Discrete Local Volatilities Price at T 1 of : Can be replicated by a PF of T 1 options: 13 th Nov, 2007 King’s College, London of known price

Discrete Local Volatilities Discrete local vol: 13 th Nov, 2007 that retrieves market price Discrete Local Volatilities Discrete local vol: 13 th Nov, 2007 that retrieves market price King’s College, London

Taking a position • Local vol = 5% • User thinks it should be Taking a position • Local vol = 5% • User thinks it should be 10% 13 th Nov, 2007 King’s College, London

P&L at T 1 • Buy 13 th Nov, 2007 , Sell King’s College, P&L at T 1 • Buy 13 th Nov, 2007 , Sell King’s College, London

P&L at T 2 • Buy 13 th Nov, 2007 , Sell King’s College, P&L at T 2 • Buy 13 th Nov, 2007 , Sell King’s College, London

Link Discrete Local Vol / Local Vol Assume real model is: is a weighted Link Discrete Local Vol / Local Vol Assume real model is: is a weighted average of with the restriction of the Brownian Bridge density between T 1 and T 2 Market prices tell us about some averages of local volatilities Regional Vols 13 th Nov, 2007 King’s College, London

Numerical example 13 th Nov, 2007 King’s College, London Numerical example 13 th Nov, 2007 King’s College, London

Price stripping Finite difference approximation: Crude approximation: for instance constant volatility (Bachelier model) does Price stripping Finite difference approximation: Crude approximation: for instance constant volatility (Bachelier model) does not give constant discrete local K volatilities: 13 th Nov, 2007 King’s College, London T

Cumulative Variance • Naïve idea: • Better approximation: 13 th Nov, 2007 King’s College, Cumulative Variance • Naïve idea: • Better approximation: 13 th Nov, 2007 King’s College, London

Vol stripping • The approximation leads to where • Better: following geodesics: where Anyway, Vol stripping • The approximation leads to where • Better: following geodesics: where Anyway, still first order equation 13 th Nov, 2007 King’s College, London

Vol stripping The exact relation is a non linear PDE : • Finite difference Vol stripping The exact relation is a non linear PDE : • Finite difference approximation: • Perfect if K 13 th Nov, 2007 King’s College, London T

Numerical examples BS prices (S 0=100; s=20%, T=1 Y) stripped with Bachelier formula sth=s. Numerical examples BS prices (S 0=100; s=20%, T=1 Y) stripped with Bachelier formula sth=s. K Price Stripping Vol Stripping K 13 th Nov, 2007 King’s College, London

Accuracy comparison 1 3 2 T K 1 2 (linearization of 3 ) 3 Accuracy comparison 1 3 2 T K 1 2 (linearization of 3 ) 3 13 th Nov, 2007 King’s College, London

Local Vol Surface construction Finite difference of Vol PDE gives averages of s 2, Local Vol Surface construction Finite difference of Vol PDE gives averages of s 2, which we use to build a full surface by interpolation. Interpolate from with (where 13 th Nov, 2007 King’s College, London )

Reconstruction accuracy • Use FWD PDE option prices to recompute • Compare with initial Reconstruction accuracy • Use FWD PDE option prices to recompute • Compare with initial market price • Use a fixed point algorithm to correct for convexity bias 13 th Nov, 2007 King’s College, London

Conclusion • Local volatilities describe the vol information and correspond to forward values that Conclusion • Local volatilities describe the vol information and correspond to forward values that can be enforced. • Direct approaches lead to unstable values. • We present a scheme based on arbitrage principle to obtain a robust surface. 13 th Nov, 2007 King’s College, London