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Open AccessArticle

Analytical Gradients of Dynamic Conditional Correlation Models

1
Department of Statistical Sciences, University of Padova, 35122 Padova PD, Italy
2
DISES, Università Politecnica delle Marche, 60121 Ancona AN, Italy
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Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2020, 13(3), 49; https://doi.org/10.3390/jrfm13030049
Received: 31 January 2020 / Revised: 25 February 2020 / Accepted: 28 February 2020 / Published: 4 March 2020
(This article belongs to the Special Issue Financial Time Series: Methods & Models)
We provide the analytical gradient of the full model likelihood for the Dynamic Conditional Correlation (DCC) specification by Engle (2002), the generalised version by Cappiello et al. (2006), and of the cDCC model by Aielli(2013). We discuss how the gradient might be further extended by introducing elements related to the conditional variance parameters, and discuss the issue arising from the estimation of constrained and/or reparametrised versions of the model. A computational simulation compares analytical versus numerical gradients, with a view to parameter estimation; we find that analytical differentiation yields more efficiency and improved accuracy. View Full-Text
Keywords: DCC; cDCC; GDCC; analytical gradient DCC; cDCC; GDCC; analytical gradient
MDPI and ACS Style

Caporin, M.; Lucchetti, R.J.; Palomba, G. Analytical Gradients of Dynamic Conditional Correlation Models. J. Risk Financial Manag. 2020, 13, 49.

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