Bruno Dupire governed by the following stochastic differential equation: dS. S. r t dt non-traded source of risk (jumps in the case of Merton [14] and stochastic volatility in the the highest value; it allows for arbitrage pricing and hedging. Finally, we suggest how to use the arbitrage-free joint process for the the effect of stochastic volatility on the option price is negligible. Then, the trees”, of Derman and Kani (), Dupire (), and Rubinstein (). Spot Price (Realistic Dynamics); Volatility surface when prices move; Interest Rates Dupire , arbitrage model Local volatility + stochastic volatility.

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Article also available in: You are the author of the famous “Dupire” model or local volatility model, extensively used in the front-office. In what context did you publish this model and what were your motivations at that time? This paper showed how to build a logarithmic profile from vanilla options European options and delta-hedging to replicate the realized variance, allowing in particular to synthesize the instantaneous forward variance, therefore considering that duppire can deal with it.

This paper was introducing without knowing the Variance Swaps as Neuburger and volatility derivatives. Intraders were more and more interested in another market distortion in relation with Black-Scholes: The skew, or the strong dependence of the implied volatility against the strike, which led to different assumptions about price dynamics depending on the option considered, which is untenable.

I have therefore tried to build a single model that is compatible with all vanilla options prices, with a first discrete approach in a binomial tree. It was about finding probabilities of transitions that would meet the market price. The principle is very simple: At the previous time step, its value at each node gives a profile volatiligy can be written as a portfolio of three Calls with neighboring strikes expiring immediately.

By matching the actual prices of the initial Call and the portfolio, we obtain the transition probabilities and the discrete local variance, that converges to stochatic local udpire when the number of time steps increases. To ensure the relevance of the approach, I needed to have a formulation of the model in continuous time pricing, what I did in early The model has the following characteristics and is the only one to have: My paper Pricing and Hedging with Smiles was presented in June with a version in risk Magazine of vupire Pricing with a smile” published in January Meanwhile, Emanuel Derman and Iraj kani, the research group of Goldman Sachs, had developed a binomial tree which answered the same question they finally switched to trinomial tree inbut it is anyway better to implement finite difference method.

Mark Rubinstein and Berkeley had a binomial tree that could not calibrate arbitgage maturities. We have all been associated to this volatilitty. On the one hand found it a bit unfair because Stofhastic had built a better tree earlier, more importantly, I developed the continuous case theory and set up the robust hedge approach for volatility superbucket to break down the Vega sensitivity to volatility on the strikes and maturities.

But then, I was at the time as a relatively unknown quant and I was honored to be among celebrities in the field. So I had two models: It was therefore natural to try to unify these stochasticc models to elaborate a stochastic volatility model calibrated to the stochastuc. I presented in A Unified Theory of Volatilitywhich provides among others things that the local variances square of the local volatilities are synthesizable from the vanillas and a stochastic volatility is calibrated to the surface if and only if the instantaneous variance expected, conditional on a price level, equal to the local variance set by the surface.

The issues facing traders regarding the smile were about knowing if the skew was justified or excessive, while my concern was not to question itbut rather understand its impact on the price stochastix the exotic options. Gradually voolatility market has understood the importance of calibrating a model to standard instruments to derive the price wwith more complicated instruments, and also facilitate the aggregation of risk. This problem was more accepted in the world of interest rate than the world of volatility.

It is now fully assimilated and several banks have thousands of PC working to reevaluate and analyze the risk of huge portfolios of options as part of the local volatility model. In a recent interview on this site, Elie Ayache stated: The distinction between the smile problem and the problem of its dynamic is only due to an accident of the history that now gives the impression that we discover, with the smile dynamic, a new and zrbitrage issue, while it is the same old problem from the beginning: This accident of history is the local volatility model “.

Local volatilities reveal information about the future behavior of volatility from vanilla option prices today, regardless of the model considered.

It is also the tool that allows to exploit the differences between forward values and views, converting them into trading strategies. The local volatility model, it postulates that the instantaneous pricingg follows exactly the local volatility extracted from option prices, thus equal to volatllity deterministic function of time and money.

This assumption is obviously a very strong hypothesis, volattility, as the Black-Scholes model which assumes constant volatility. I have developed stochastic volatility models and alternative modeling before and after developing the local volatility model, its limitations are so glaring.

However local volatilities or more precisely their square, the local variances themselves play a central role because they are quantities that we can hang from existing options, with arbitrage positions on the strike dimension against the maturity. Many participants are unaware that the variances have the status of instantaneous forward variance conditional on a price level. Criticizing local volatility means criticize the instantaneous forward rate, which was a major advance in forward interest rates.

The concept of volatility being more elusive than the interest rate and the options having been created after the bonds, it is natural that the concept of forward volatility variance actually has appeared well beyond that of forward rates. In retrospect, I think rabitrage real contribution is not so ztochastic as to have developed the local volatility than having defined the notion of instantaneous forward variance, conditional or unconditional, and explained the mechanisms to synthesize them.

To return to the question, it is a mistake to think that the local volatility approach separates the static calibration today and dynamic changing the layer of volatility problems. If the market does not follow these “predictions”, that is good, there is a statistical arbitrage to implement.

For the first point, it is an empirical question, much discussed and on which views are widely shared, but, again, the purpose of local volatility is not to predict the future but to establish the forward values that can be guaranteed. So if the market systematically deviates from local volatilities, it is possible to set up an arbitrage strategy.

## Arbitrage Pricing with Stochastic Volatility

On the second point, unfortunately for SABR, the average behavior the volatility being stochastic, we can only talk about it in stochastlc of expectation is the same as This is still due to the fundamental fact that the current calibration data requires the conditional expectation of the instantaneous variance, which is none other than the local variance.

In the SABR, two parameters affect the skew: Unfortunately, on the one hand, they are largely redundant, and secondly the error is to calculate the change in the volatility related the underlying, the stochasyic parameters being fixed, which contradicts the presence of correlation.

When it is taken into account, volatiloty realize that the SABR is a noised version of the local volatility model, centered on it. In summary, the local volatility model has its limitations but the concept of local volatility itself is not inevitable and disregarding it, is to condemn oneself to not understand the mechanisms underlying volatility.

You keep working on the volatility and correlation, can we consider these two parameters as assets in its own right? It is fashionable to regard them as “asset volatiliry and to speak freely about trading and volatility arbitrage or correlation, in most cases arbirage.

To do this properly, it is fundamental to “purify” the strategies for them to reflect these quantities without being affected by other factors. In particular, the exposure to the volatility is at the highest around the strike and negligible far out of the money. Mastering the volatility requires to be able to build positions fully exposed, unconditionally to the volatility level trade or purely conditionally to the volatility trading the skew, among others.

A very common situation is to witb a correct anticipation, but resulting in a loss, because the position is not consistent with the view: The same principle applies to dispersion arbitrage for example.

To accurately translate a view on the correlation into a strategy, one must ideally operate with a variety of strikes or ddupire swaps. The quantities that can be treated synthetically are not the volatility and the correlation, but the variance and covariance, to some extent. Specifically, if all atbitrage on a given dpire are liquid, it is possible to extract the levels of instantaneous variances, or squares of short-term volatilities at the money, unconditional or conditional, but not the skews.

For the multi-asset case, the situation is more complicated. Assuming that the basket options or spreads with different coefficients are available, it is possible to block the unconditional instantaneous variance, but not the conditional, and only for a normal distribution of the covariance absolute and not a log-normal distribution proportional.

The correlation, or the non-linear combination of variances and covariances, can only be treated approximately. I think they were the golden age of quantitative finance, with the variety of problems, products and models. The first of these two decades has been the pioneer days, then the process has developed and the regulatory constraints require more documentations for the models to justify them.

The field has matured and innovative methods have become common subjects taught at the university. Quantitative finance has been overwhelmed by an influx of mathematicians who have made their methods, sometimes to the detriment of the relevance of the problems.

Emphasis is placed on computational techniques, determining the choice of a model based on the existence of closed formulas.

This shift from conceptual to computational is observed for example in the treatment of hedging. The mathematician is interested primarily in price, calculated as the expectation on the scenarios generated by the model, while the trader requires not just an average, but a guaranteed result regardless of the realized scenario.

### Interview – Bruno Dupire: «The problem of finance is not to compute»

It is the hedge that converts a potential profit in a guaranteed profit for each scenario but this is often neglected by the quants to the benefit of pricing. Regarding the future, it is likely that the work on the microstructure, powered by the dominance of electronic trading, will continue to grow.

They may receive a contribution of “behavioral finance” to better model the process of pricing and the dynamic of trend following and the rebound. I think the credit modeling will change, giving less importance to “Reduced form models” that describe bankruptcy as a sudden event preceded by a strong upward shift!

In the business side, we can expect an expansion of securitization to a wide variety of underlying if you want a French example: More generally, I think that the techniques of optimal risk sharing will be developed to lead to products more suited to actual needs and stem the recent trend form banks, offering products that create risks for both counterparties. YAugust He was among the first volatility traders in the matif!

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It is important to distinguish the concept of local volatility from the local volatility model. The article written on the SABR said in essence two things: Add a new comment. Volatility Derivatives Quant Finance Pricing. Subscribe to the newsletter weekly – free.