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Parametric Estimation of Risk Neutral Density Functions

Part of the Springer Handbooks of Computational Statistics book series (SHCS)

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 Carr and Madan [J. Comput. Finance 2(4):61–73, 1999]. It is intended to compare the reviewed estimation methods empirically.

Keywords

  • Risk-neutral Density Function
  • Risk-neutral Valuation Principle
  • Fast Fourier Transform Method
  • Stock Price Process
  • Risk-neutral Probability Measure

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Maria Grith .

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Grith, M., Krätschmer, V. (2012). Parametric Estimation of Risk Neutral Density Functions. In: Duan, JC., Härdle, W., Gentle, J. (eds) Handbook of Computational Finance. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17254-0_10

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