Break Even Volatilities Dr Bruno Dupire Dr Arun Verma Quantitative Research, Bloomberg LP

1. Break Even Volatilities Dr Bruno Dupire Dr Arun Verma Quantitative Research, Bloomberg LP King’s College, London

2. 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. • Discounted average of the historical Intrinsic Values. • Bad : depends on bull/bear, no call/put parity. • Generate paths by sampling 1 day return re-centered histogram. • Problem : CLT => converges quickly to same volatility for all strike/maturity; breaks auto-correlation and vol/spot dependency. King’s College, London

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

4. S K t Theoretical Skewfrom historical prices (3) How to get a theoretical Skew just from spot price history? Example: 3 month daily data 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” King’s College, London

5. Zero-finding of P&L King’s College, London

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

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11. 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) King’s College, London

12. 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. King’s College, London

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

14. 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 are 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. King’s College, London

15. Strike dependency for multiple paths King’s College, London

16. SPX Index BEVL <GO> King’s College, London

17. 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! King’s College, London

18. Discrete Local Volatility Or Regional Volatility King’s College, London

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

20. Market facts King’s College, London

21. S&P Strikes and Maturities K Aug 07 Oct 07 Dec 07 Sept 07 Mar 08 Jun 08 Jun 09 Dec 08 Mar 09 T King’s College, London

22. Price at T1 of : Can be replicated by a PF of T1 options: of known price Discrete Local Volatilities King’s College, London

23. Discrete local vol: that retrieves market price Discrete Local Volatilities King’s College, London

24. Taking a position • Local vol = 5% • User thinks it should be 10% King’s College, London

25. P&L at T1 • Buy , Sell King’s College, London

26. P&L at T2 • Buy , Sell King’s College, London

27. Assume real model is: is a weighted average of with the restriction of the Brownian Bridge density between T1 and T2  Market prices tell us about some averages of local volatilities - Regional Vols Link Discrete Local Vol / Local Vol King’s College, London

28. Numerical example King’s College, London

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

30. Cumulative Variance • Naïve idea: • Better approximation: King’s College, London

31. Vol stripping • The approximation leads to • Better: following geodesics: where where Anyway, still first order equation King’s College, London

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

33. Numerical examples BS prices (S0=100; s=20%, T=1Y) stripped with Bachelier formula  sth=s.K Price Stripping Vol Stripping K King’s College, London

34. Accuracy comparison 1 3 2 T K 1 (linearization of ) 2 3 3 King’s College, London

35. (where ) Local Vol Surface construction Finite difference of Vol PDE gives averages of s2, which we use to build a full surface by interpolation. Interpolate from with King’s College, London

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

37. 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. King’s College, London