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Econometrics 2016, 4(2), 20; doi:10.3390/econometrics4020020

Recovering the Most Entropic Copulas from Preliminary Knowledge of Dependence

1
Department of Economics, Carleton University, B-857 Loeb Building, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
2
Trinity College, University of Cambridge, Cambridge CB2 1TQ, UK
3
The University of Sydney Business School, New South Wales NSW 2006, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Fredj Jawadi, Tony S. Wirjanto, Marc S. Paolella and Nuttanan Wichitaksorn
Received: 7 June 2015 / Revised: 24 February 2016 / Accepted: 1 March 2016 / Published: 29 March 2016
(This article belongs to the Special Issue Recent Developments of Financial Econometrics)
View Full-Text   |   Download PDF [336 KB, uploaded 29 March 2016]

Abstract

This paper provides a new approach to recover relative entropy measures of contemporaneous dependence from limited information by constructing the most entropic copula (MEC) and its canonical form, namely the most entropic canonical copula (MECC). The MECC can effectively be obtained by maximizing Shannon entropy to yield a proper copula such that known dependence structures of data (e.g., measures of association) are matched to their empirical counterparts. In fact the problem of maximizing the entropy of copulas is the dual to the problem of minimizing the Kullback-Leibler cross entropy (KLCE) of joint probability densities when the marginal probability densities are fixed. Our simulation study shows that the proposed MEC estimator can potentially outperform many other copula estimators in finite samples. View Full-Text
Keywords: entropy; relative entropy measure of joint dependence; copula; most entropic copula; canonical; kullback-Leibler cross entropy entropy; relative entropy measure of joint dependence; copula; most entropic copula; canonical; kullback-Leibler cross entropy
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chu, B.; Satchell, S. Recovering the Most Entropic Copulas from Preliminary Knowledge of Dependence. Econometrics 2016, 4, 20.

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