One of the central goals in finance is to find better models for pricing and hedging financial derivatives such as call and put options. We present a semi-nonparametric approach to risk-neutral density extraction from option prices which is based on an extension of the concept of mixture density networks. The central idea is to model the shape of the risk-neutral density in a flexible, non-linear way as a function of the time horizon. Thereby, stylized facts such as negative skewness and excess kurtosis are captured. The approach is applied to a very large set of intraday options data on the FTSE 100 recorded at LIFFE. It is shown to yield significantly better results in terms of out-of-sample pricing in comparison to the basic Black-Scholes model and to an extended model adjusting the skewness and kurtosis terms. From the perspective of risk management, the extracted risk-neutral densities provide valuable information about market expectations. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"