Title: Bayesian Calibration of Option Pricing Models
Dr. Luca Gonzato, University of Vienna, Department of Statistics and Operations Research

Abstract: Calibration of option pricing models to the implied volatility surface is a complicated, yet fundamental task in the quantitative finance community. By exploiting Sequential Monte Carlo (SMC) methods we turn the standard calibration problem into a Bayesian estimation task. In this way we can construct a sequence of distributions from the prior to the posterior which allows to compute any statistic of the estimated parameters, to overcome the strong dependence on the starting point and to avoid troublesome local minima; all of which are typical plagues of the standard calibration. To highlight the strength of our approach we consider the calibration of the double jump stochastic volatility model of Duffie et. al (2000) both on simulated and real option data. From the results on both single dates and time series of implied volatilities we find that our Bayesian approach largely outperforms the benchmark in terms of run time-accuracy, option pricing errors and statistical fit. Finally, we show how to further speed up computations by leveraging delayed-acceptance MCMC methods and deep learning. This is a joint work with R. Brignone, S. Knaust and E. Lutkebohmert, University of Friburg.