Nonparametric Estimation of Copulas for Time Series

Jean-David Fermanian and Olivier Scaillet
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Jean-David Fermanian: CDC Ixis Capital Markets and CREST

FAME Research Paper Series from International Center for Financial Asset Management and Engineering

Abstract: We consider a nonparametric method to estimate copulas, i.e. 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 satisfactory 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.

Keywords: Nonparametric, Kernel; Time Series; Copulas; Dependence Measures; Risk Management; Loss Severity Distribution (search for similar items in EconPapers)
JEL-codes: C14 D81 G10 G21 G22 (search for similar items in EconPapers)
Date: 2003-02
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Citations: View citations in EconPapers (80)

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Related works:
Working Paper: Nonparametric estimation of copulas for time series (2003)
Journal Article: Nonparametric estimation of copulas for time series
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Persistent link: https://EconPapers.repec.org/RePEc:fam:rpseri:rp57

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