Fast Valuation of Options under Parameter Uncertainty

Abstract: Option valuation is typically done under the unrealistic assumption of perfect knowledge about model parameters. This thesis shows that risk-neutral valuation, while still adressing the parameter uncertainty, can be computed for a variety of models within the Fourier framework. This results in a computationally inexpensive method for valuating options. A study of S&P500 index option data shows that the method improves the predictive performances of the Black&Scholes, Merton and Heston models.