A dynamic conditional score model for the log correlation matrix

Christian Hafner and Linqi Wang

No 2019031, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: This paper proposes a new model for the dynamics of correlation matrices, where the dynamics are driven by the likelihood score with respect to the matrix logarithm of the correlation matrix. In analogy to the exponential GARCH model for volatility, this transformation ensures that the correlation matrices remain positive defi nite, even in high dimensions. For the conditional distribution of returns we assume a student-t copula to explain the dependence structure and univariate student-t for the marginals with potentially diff erent degrees of freedom. The separation into volatility and correlation parts allows two-step estimation, which facilitates estimation in high dimensions. We derive estimation theory for one-step and two-step estimation. In an application to a set of six asset indices including nancial and alternative assets we show that the model performs well in terms of various diagnostics and speci cation tests.

Keywords: score; correlation; matrix logarithm; identification (search for similar items in EconPapers)
JEL-codes: C14 C43 Z11 (search for similar items in EconPapers)
Date: 2019-12-17
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Related works:
Working Paper: A dynamic conditional score model for the log correlation matrix (2022)
Working Paper: A dynamic conditional score model for the log correlation matrix (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2019031

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