MLEMVD: A R Package for Maximum Likelihood Estimation of Multivariate Diffusion Models

15 Pages Posted: 3 Apr 2017 Last revised: 20 May 2017

See all articles by Matthew Francis Dixon

Matthew Francis Dixon

Illinois Institute of Technology

Tao L. Wu

Illinois Institute of Technology

Date Written: May 18, 2017

Abstract

Continuous-time Markov processes are typically defined by stochastic differential equations, describing the evolution of one or more state variables. Maximum likelihood estimation of the model parameters to historical observations is only possible when at least one of the state variables is observable. In these cases, the form of the transition function corresponding to the stochastic differential equations must be known to assess the efficacy of fitting a continuous model to discrete samples. This paper makes two contributions: (i) we describe a new R package MLEMVD for calibrating general multi-variate diffusions models using maximum likelihood estimates; and (ii) we present an algorithm for calibrating the Heston model to option prices using maximum likelihood estimation and assess the robustness of the approach using Monte Carlo simulation.

Keywords: Maximum likelihood estimation, Heston, R

Suggested Citation

Dixon, Matthew Francis and Wu, Tao L., MLEMVD: A R Package for Maximum Likelihood Estimation of Multivariate Diffusion Models (May 18, 2017). Available at SSRN: https://ssrn.com/abstract=2944341 or http://dx.doi.org/10.2139/ssrn.2944341

Matthew Francis Dixon (Contact Author)

Illinois Institute of Technology ( email )

Department of Math
W 32nd St., E1 room 208, 10 S Wabash Ave, Chicago,
Chicago, IL 60616
United States

Tao L. Wu

Illinois Institute of Technology ( email )

Stuart Graduate School of Business
565 W. Adams St.
Chicago, IL 60661
United States

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