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Unbiased Monte Carlo Simulation of Diffusion Processes

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  • Louis Paulot

Abstract

Monte Carlo simulations of diffusion processes often introduce bias in the final result, due to time discretization. Using an auxiliary Poisson process, it is possible to run simulations which are unbiased. In this article, we propose such a Monte Carlo scheme which converges to the exact value. We manage to keep the simulation variance finite in all cases, so that the strong law of large numbers guarantees the convergence. Moreover, the simulation noise is a decreasing function of the Poisson process intensity. Our method handles multidimensional processes with nonconstant drifts and nonconstant variance-covariance matrices. It also encompasses stochastic interest rates.

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  • Louis Paulot, 2016. "Unbiased Monte Carlo Simulation of Diffusion Processes," Papers 1605.01998, arXiv.org.
    • Handle: RePEc:arx:papers:1605.01998
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    File URL: http://arxiv.org/pdf/1605.01998
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    Cited by:

    1. Andersson, Patrik & Kohatsu-Higa, Arturo & Yuasa, Tomooki, 2020. "Second order probabilistic parametrix method for unbiased simulation of stochastic differential equations," Stochastic Processes and their Applications, Elsevier, vol. 130(9), pages 5543-5574.

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