文章基本信息

标题：Analytical Gradients of Dynamic Conditional Correlation Models
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作者：Caporin, Massimiliano ; Lucchetti, Riccardo (Jack) ; Palomba, Giulio 等
期刊名称：Journal of Risk and Financial Management
印刷版ISSN：1911-8074
出版年度：2020
卷号：13
期号：3
页码：1-21
DOI：10.3390/jrfm13030049
语种：English
出版社：MDPI, Open Access Journal
摘要：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.
关键词：DCC; cDCC; GDCC; analytical gradient