title: Risk-neutral density extraction from option prices: improved pricing with mixture density networks.
abstract: 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 new 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, nonlinear 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 accuracy in comparison to the basic and an extended Black-Scholes model. It is also significantly better than a more elaborate GARCH option pricing model which includes a time-dependent volatility process. From the perspective of risk management, the extracted risk-neutral densities provide valuable information for value-at-risk estimations.
authors.affiliation: Austrian Research Institute for Artificial Intelligence, 1010 Vienna, Austria.
authors.forename: C
authors.initials: C
authors.lastname: Schittenkopf
authors.forename: G
authors.initials: G
authors.lastname: Dorffner