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Risk-Neutral Density Extraction from Option Prices: Improved Pricing with Mixture Density Networks
- Source :
- IEEE Transactions on Neural Networks. July, 2001, Vol. 12 Issue 4, 716
- Publication Year :
- 2001
-
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. Index Terms--Hedging, mixture density networks, options, pricing, risk-neutral densities.
Details
- ISSN :
- 10459227
- Volume :
- 12
- Issue :
- 4
- Database :
- Gale General OneFile
- Journal :
- IEEE Transactions on Neural Networks
- Publication Type :
- Academic Journal
- Accession number :
- edsgcl.77103192