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.

Additional Metadata
Keywords entropy, relative entropy measure of joint dependence, copula, most entropic copula, canonical, kullback-Leibler cross entropy
Persistent URL dx.doi.org/10.3390/econometrics4020020
Journal Econometrics
Citation
Chu, B, & Satchell, Stephen. (2016). Recovering the Most Entropic Copulas from Preliminary Knowledge of Dependence. Econometrics, 4(2). doi:10.3390/econometrics4020020