EconPapers    
Economics at your fingertips  
 

Analytical Gradients of Dynamic Conditional Correlation Models

Massimiliano Caporin, Riccardo (Jack) Lucchetti and Giulio Palomba ()

JRFM, 2020, vol. 13, issue 3, 1-21

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.

Keywords: DCC; cDCC; GDCC; analytical gradient (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1911-8074/13/3/49/pdf (application/pdf)
https://www.mdpi.com/1911-8074/13/3/49/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:13:y:2020:i:3:p:49-:d:328491

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-04-26
Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:3:p:49-:d:328491