Risk-Neural Density Extraction From Option Prices : Improved Pricing With Mixture Density Networks  

Oleh:

Dorffner, G. ; Schittenkopf, C.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 4 (2001), page 716-725.
Topik: CURRENT DENSITY; risk - neural; density extraction; option prices; pricing; mixture density; networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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 -non parametric 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.

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