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Variance Reduction Techniques

  • Eckhard PlatenEmail author
  • Nicola Bruti-Liberati
Chapter
Part of the Stochastic Modelling and Applied Probability book series (SMAP, volume 64)

Abstract

The evaluation of the expectation of a given function of a solution of an SDE with jumps provides via the Feynman-Kac formula, see Sect. 2.7, the solution of a partial integro differential equation. In many applications it is of major interest to obtain numerically these expectations, in particular in multi-dimensional settings. Monte Carlo simulation appears to be a method that may be able to provide answers to this question under rather general circumstances. However, raw Monte Carlo estimates of the expectation of a payoff structure, for instance for derivative security prices, can be very expensive in terms of computer resource usage. In this chapter we investigate the problem of constructing variance reduced estimators for the expectation of functionals of solutions of SDEs that can speed up the simulation enormously. We follow again closely Heath (1995). As we will see, variance reduction is more of an art and can be applied in many ways. This chapter shall enable the reader to design her or his own variance reduction method for a given problem at hand.

Keywords

Unbiased Estimator Variance Reduction Implied Volatility Valuation Function Stochastic Volatility Model 
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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References

  1. Heath, D. (1995). Valuation of derivative securities using stochastic analytic and numerical methods, PhD thesis, ANU, Canberra. Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.School of Finance and Economics, Department of Mathematical SciencesUniversity of Technology, SydneyBroadwayAustralia

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