Bayesian option pricing using mixed normalheteroskedasticity models

While stochastic volatility models improve on the option pricing error when compared to theBlack-Scholes-Merton model, mispricings remain. This paper uses mixed normalheteroskedasticity models to price options. Our model allows for significant negative skewnessand time varying higher order moments of the risk neutral distribution. Parameter inference usingGibbs sampling is explained and we detail how to compute risk neutral predictive densities takinginto account parameter uncertainty. When forecasting out-of-sample options on the S&P 500index, substantial improvements are found compared to a benchmark model in terms of dollarlosses and the ability to explain the smirk in implied volatilities....