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Monte Carlo Simulation of SDEs

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

Abstract

This chapter introduces what is commonly known as Monte Carlo simulation for stochastic differential equations. We explain that Monte Carlo simulation is a much simpler task than scenario simulation, discussed in the previous chapters. A weak convergence criterion will be introduced that allows us to classify various discrete-time approximations and numerical schemes for the purpose of Monte Carlo simulation. For simplicity, we focus on the case without jumps in this introductory chapter. The case with jumps is more complicated and will be described in Chaps. 12 and 13.

Keywords

Monte Carlo Simulation Weak Convergence Time Step Size Euler Scheme Weak Order 
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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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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