Multilevel Monte Carlo Methods for Simulated Maximum Likelihood Inference in Multivariate Diffusions

Research output: Contribution to conferencePaper, not in proceedingpeer-review

Abstract

Multilevel Monte Carlo is a novel method for reducing the computational cost when computing conditional expectations of stochastic processes. This paper considers the transition density for diffusion processes. It is known that the transition density can be written as an expectation by utilizing the law of total probability combined with the Markov property. This idea is combined with the multilevel Monte Carlo framework to derive a new estimator. Both the theoretical derivation and the simulations show that the proposed method is able to reduce the variance of the estimates substantially, when keeping the bias and computational cost fixed, relative to the standard approximations.









Original languageEnglish
Publication statusPublished - 2016
EventWORLD CONGRESS OF THE BACHELIER FINANCE SOCIETY - New York, United States
Duration: 2016 Jul 152016 Jul 19
Conference number: 9th
http://www.bacheliercongress.com/2016/conference.html

Conference

ConferenceWORLD CONGRESS OF THE BACHELIER FINANCE SOCIETY
Country/TerritoryUnited States
CityNew York
Period2016/07/152016/07/19
Internet address

Subject classification (UKÄ)

  • Probability Theory and Statistics

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