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A Simulation and Empirical Study of the Maximum Likelihood Estimator for Stochastic Volatility Jump-Diffusion Models

  • Jean-François Bégin ORCID logo EMAIL logo und Mathieu Boudreault
Veröffentlicht/Copyright: 29. März 2024
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Abstract

We investigate the behaviour of the maximum likelihood estimator (MLE) for stochastic volatility jump-diffusion models commonly used in financial risk management. A simulation study shows the practical conditions under which the MLE behaves according to theory. In an extensive empirical study based on nine indices and more than 6000 individual stocks, we nonetheless find that the MLE is unable to replicate key higher moments. We then introduce a moment-targeted MLE – robust to model misspecification – and revisit both simulation and empirical studies. We find it performs better than the MLE, improving the management of financial risk.

JEL Classification: C13; C51; C58

Corresponding author: Jean-François Bégin, Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, British Columbia, Canada, E-mail:

Funding source: Simon Fraser University

Funding source: Natural Science and Engineering Research Council of Canada

Acknowledgment

The authors would like to thank Louis Arsenault-Mahjoubi and Geneviève Gauthier for their helpful suggestions and comments. Bégin wishes to acknowledge the financial support of the Natural Science and Engineering Research Council of Canada (NSERC) and Simon Fraser University. Boudreault also thanks the financial support of NSERC. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

  1. Research funding: This work was supported by Simon Fraser University and Natural Science and Engineering Research Council of Canada.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/snde-2023-0028).


Received: 2023-04-03
Accepted: 2024-03-06
Published Online: 2024-03-29

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