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Exact Maximum Likelihood Estimation for Copula Models

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  • Jin Zhang
  • Wing Long Ng

Abstract

In recent years, copulas have become very popular in financial research and actuarial science as they are more flexible in modelling the co-movements and relationships of risk factors as compared to the conventional linear correlation coefficient by Pearson. However, a precise estimation of the copula parameters is vital in order to correctly capture the (possibly nonlinear) dependence structure and joint tail events. In this study, we employ two optimization heuristics, namely Differential Evolution and Threshold Ac- cepting to tackle the parameter estimation of multivariate t distribution models in the EML approach. Since the evolutionary optimizer does not rely on gradient search, the EML approach can be applied to estimation of more complicated copula models such as high-dimensional copulas. Our experimental study shows that the proposed method provides more robust and more accurate estimates as compared to the IFM approach.

Suggested Citation

  • Jin Zhang & Wing Long Ng, 2010. "Exact Maximum Likelihood Estimation for Copula Models," Working Papers 038, COMISEF.
    • Handle: RePEc:com:wpaper:038
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    References listed on IDEAS

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    1. Fantazzini, Dean, 2010. "Three-stage semi-parametric estimation of T-copulas: Asymptotics, finite-sample properties and computational aspects," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2562-2579, November.
    2. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
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    Cited by:

    1. Aldy, Joseph E., 2014. "The Labor Market Impacts of the 2010 Deepwater Horizon Oil Spill and Offshore Oil Drilling Moratorium," RFF Working Paper Series dp-14-27, Resources for the Future.

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    More about this item

    Keywords

    Copula Models; Parameter Inference; Exactly Maximum Likelihood; Differential Evolution; Threshold Accepting;
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