Maximum likelihood estimation for Wishart processes

Aurélien Alfonsi, Ahmed Kebaier and Clément Rey

Stochastic Processes and their Applications, 2016, vol. 126, issue 11, 3243-3282

Abstract: In the last decade, there has been a growing interest to use Wishart processes for modeling, especially for financial applications. However, there are still few studies on the estimation of its parameters. Here, we study the Maximum Likelihood Estimator (MLE) in order to estimate the drift parameters of a Wishart process. We obtain precise convergence rates and limits for this estimator in the ergodic case and in some nonergodic cases. We check that the MLE achieves the optimal convergence rate in each case. Motivated by this study, we also present new results on the Laplace transform that extend the recent findings of Gnoatto and Grasselli (2014) and are of independent interest.

Keywords: Wishart processes; Laplace transform; Parameter inference; Maximum likelihood; Limit theorems; Local asymptotic properties (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:spapps:v:126:y:2016:i:11:p:3243-3282

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DOI: 10.1016/j.spa.2016.04.026

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