Multivariate Student versus Multivariate Gaussian Regression Models with Application to Finance

Nguyen, T.H.A, Ruiz-Gazen, Anne, Thomas-Agnan, Christine and Laurent, Thibault (2019) Multivariate Student versus Multivariate Gaussian Regression Models with Application to Finance. Journal of Risk and Financial Management, 12 (1).

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Official URL: http://www.mdpi.com/1911-8074/12/1/28

Abstract

To model multivariate, possibly heavy-tailed data, we compare the multivariate normal model (N) with two versions of the multivariate Student model: the independent multivariate Student (IT) and the uncorrelated multivariate Student (UT). After recalling some facts about these distributions and models, known but scattered in the literature, we prove that the maximum likelihood estimator of the covariance matrix in the UT model is asymptotically biased and propose an unbiased version. We provide implementation details for an iterative reweighted algorithm to compute the maximum likelihood estimators of the parameters of the IT model. We present a simulation study to compare the bias and root mean squared error of the ensuing estimators of the regression coefficients and covariance matrix under several scenarios of the potential data-generating process, misspecified or not. We propose a graphical tool and a test based on the Mahalanobis distance to guide the choice between the competing models. We also present an application to model vectors of financial assets returns.

Item Type: Article
Language: English
Date: 2019
Refereed: Yes
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 28 Feb 2019 13:18
Last Modified: 08 Mar 2019 10:41
OAI ID: oai:tse-fr.eu:122839
URI: http://publications.ut-capitole.fr/id/eprint/31774

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