Nonparametric estimation of copulas for time series

Jean‐David Fermanian · Olivier Scaillet B

199 citations0/0 files storedThe Journal of Risk

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Probability and Risk Models

Abstract

ABSTRACT We consider a nonparametric method to estimate copulas, ie, functions linking joint distributions to their univariate margins. We derive the asymptotic properties of kernel estimators of copulas and their derivatives in the context of a multivariate stationary process satisfying strong mixing conditions. Monte Carlo results are reported for a stationary vector autoregressive process of order one with Gaussian innovations. An empirical illustration containing a comparison with the independent, comotonic and Gaussian copulas is given for European and US stock index returns.

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References

1999An Introduction to Copulas 2002Correlation and Dependence in Risk Management: Properties and Pitfalls 1998Understanding Relationships Using Copulas 2000On Default Correlation 1993Statistical Inference Procedures for Bivariate Archimedean Copulas 1995A Semiparametric Estimation Procedure of Dependence Parameters in Multivariate Families of Distributions 2007Copulas for Finance - A Reading Guide and Some Applications 2000Sensitivity analysis of Values at Risk

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