Inhomogeneous Dependence Modeling with Time-Varying Copulae

Enzo Giacomini · Wolfgang Karl Härdle · Vladimir Grigor'evich Spokoiny

97 citations0/0 files storedJournal of Business and Economic Statistics

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Risk and Portfolio Optimization

Abstract

Measuring dependence in multivariate time series is tantamount to modeling its dynamic structure in space and time. In risk management, the nonnormal behavior of most financial time series calls for non-Gaussian dependences. The correct modeling of non-Gaussian dependences is, therefore, a key issue in the analysis of multivariate time series. In this article we use copula functions with adaptively estimated time-varying parameters for modeling the distribution of returns. Furthermore, we apply copulae to the estimation of Value-at-Risk of portfolios and show their better performance over the RiskMetrics approach.

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References

1999An Introduction to Copulas 1989The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis 2002Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis 1993Tests for Parameter Instability and Structural Change With Unknown Change Point 2001Empirical properties of asset returns: stylized facts and statistical issues 2001Extreme Correlation of International Equity Markets 2005Quantitative Risk Management: Concepts, Techniques, and Tools 2006MODELLING ASYMMETRIC EXCHANGE RATE DEPENDENCE*

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