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Recovering the Most Entropic Copulas from Preliminary Knowledge of Dependence

Author

Listed:
  • Ba Chu

    (Department of Economics, Carleton University, B-857 Loeb Building, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada)

  • Stephen Satchell

    (Trinity College, University of Cambridge, Cambridge CB2 1TQ, UK
    The University of Sydney Business School, New South Wales NSW 2006, Australia)

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.

Suggested Citation

  • Ba Chu & Stephen Satchell, 2016. "Recovering the Most Entropic Copulas from Preliminary Knowledge of Dependence," Econometrics, MDPI, vol. 4(2), pages 1-21, March.
    • Handle: RePEc:gam:jecnmx:v:4:y:2016:i:2:p:20-:d:66662
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    References listed on IDEAS

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