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Parametric estimation of risk neutral density functions

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  • Maria Grith
  • Volker Krätschmer

Abstract

This chapter deals with the estimation of risk neutral distributions for pricing index options resulting from the hypothesis of the risk neutral valuation principle. After justifying this hypothesis, we shall focus on parametric estimation methods for the risk neutral density functions determining the risk neutral distributions. We we shall differentiate between the direct and the indirect way. Following the direct way, parameter vectors are estimated which characterize the distributions from selected statistical families to model the risk neutral distributions. The idea of the indirect approach is to calibrate characteristic parameter vectors for stochastic models of the asset price processes, and then to extract the risk neutral density function via Fourier methods. For every of the reviewed methods the calculation of option prices under hypothetically true risk neutral distributions is a building block. We shall give explicit formula for call and put prices w.r.t. reviewed parametric statistical families used for direct estimation. Additionally, we shall introduce the Fast Fourier Transform method of call option pricing developed in [6]. It is intended to compare the reviewed estimation methods empirically.

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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2010-045.

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Length: 23 pages
Date of creation: Sep 2010
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2010-045

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Keywords: Risk neutral valuation principle; risk neutral distribution; logprice risk neutral distribution; risk neutral density function; Black Scholes formula; Fast Fourier Transform method; log-normal distributions; mixtures of log-normal distributions; generalized gamma distributions; model calibration; Merton’s jump diffusion model; Heston’s volatility model;

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References

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