Nonlinearities and regimes in conditional correlations with different dynamics

Luc Bauwens and Edoardo Otranto

No 3128, LIDAM Reprints CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: New parameterizations of the dynamic conditional correlation (DCC) model and of the regime-switching dynamic correlation (RSDC) model are introduced, such that these models provide a specific dynamics for each correlation. They imply a nonlinear autoregressive form of dependence on lagged correlations and are based on properties of the Hadamard exponential matrix. The new models are applied to a data set of twenty stock market indices and a data set of the thirty Dow Jones components, comparing them to the classical DCC and RSDC models. The empirical results show that the new models improve their classical versions in terms of several criteria.

Keywords: Dynamic conditional correlations; Regime-switching dynamic correlations; Hadamard exponential matrix (search for similar items in EconPapers)
JEL-codes: C32 C58 (search for similar items in EconPapers)
Date: 2020-01-01
Note: In : Journal of Econometrics - Vol. 217, no.2, p. 496-522 (2020)
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Nonlinearities and regimes in conditional correlations with different dynamics (2020)
Working Paper: Nonlinearities and Regimes in Conditional Correlations with Different Dynamics (2018)
Working Paper: Nonlinearities and regimes in conditional correlations with different dynamics (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvrp:3128

DOI: 10.1016/j.jeconom.2019.12.014

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