| Sumario: | Model calibration is a challenging yet fundamental task in financial engineering. Using sequential Monte Carlo methods, we reformulate the nonconvex optimization problem as a Bayesian estimation task. This allows to compute any statistic of the estimated parameters, mitigating the strong dependence on starting points and avoiding the troublesome local minima, that plague standard calibration methods. To accelerate computation, we incorporate Markov chain Monte Carlo methods with delayed acceptance and a neural network-based option pricing approach. When applied to S&P 500 index options, our Bayesian algorithms significantly outperform the standard approach in terms of runtime, accuracy, and statistical fit.
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