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Copula Parameter Estimation - Numerical Considerations And Implications For Risk Management
Year Of Publication: 2009
Month Of Publication: October
Pages: 46
Download Count: 12
View Count: 1437
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 8-15-2010
Publisher: Administrator
Summary
The purpose of this paper is to present a comprehensive simulation study on the finite sample properties of minimum-distance and maximum-likelihood estimators for bivariate and multivariate parametric copulas. For five popular parametric copulas, classical maximum-likelihood is compared to a total of nine different minimum-distance estimators. The results presented in this paper show that in most settings canonical maximum-likelihood yields smaller estimation biases at less computational effort than any of the MD-estimators. MD-estimators based on Kendall's transform on the other hand yield only suboptimal results in all configurations of the simulation study. The results of the simulation study are con firmed by empirical examples where the Value-at-Risk as well as the Expected Shortfall of 100 bivariate portfolios are computed. Interestingly, the estimates for these risk measures di ered considerably depending on the choice of parameter estimator.
Author(s)
Weiss, Gregor N. Sign in to follow this author
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