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Analytical Gradients of Dynamic Conditional Correlation Models

Author

Listed:
  • Massimiliano Caporin

    (Department of Statistical Sciences, University of Padova, 35122 Padova PD, Italy)

  • Riccardo (Jack) Lucchetti

    (DISES, Università Politecnica delle Marche, 60121 Ancona AN, Italy)

  • Giulio Palomba

    (DISES, Università Politecnica delle Marche, 60121 Ancona AN, Italy)

Abstract

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.

Suggested Citation

  • Massimiliano Caporin & Riccardo (Jack) Lucchetti & Giulio Palomba, 2020. "Analytical Gradients of Dynamic Conditional Correlation Models," JRFM, MDPI, vol. 13(3), pages 1-21, March.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:3:p:49-:d:328491
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    References listed on IDEAS

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    Cited by:

    1. Massimiliano Caporin & Giuseppe Storti, 2020. "Financial Time Series: Methods and Models," JRFM, MDPI, vol. 13(5), pages 1-3, April.

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    Keywords

    DCC; cDCC; GDCC; analytical gradient;
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