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Nonparametric Estimation of Copulas for Time Series

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Author Info
Jean-David FERMANIAN (CDC Ixis Capital Markets and CREST)
Olivier SCAILLET (HEC Genève and FAME, Université de Genève)

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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.

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Publisher Info
Paper provided by International Center for Financial Asset Management and Engineering in its series FAME Research Paper Series with number rp57.

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Date of creation: Feb 2003
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Handle: RePEc:fam:rpseri:rp57

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Related research
Keywords: Nonparametric; Kernel; Time Series; Copulas; Dependence Measures; Risk Management; Loss Severity Distribution;

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Mortgages
G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies

Cited by:
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  1. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173. [Downloadable!]
  2. Rob van den Goorbergh, 2004. "A Copula-Based Autoregressive Conditional Dependence Model of International Stock Markets," DNB Working Papers 022, Netherlands Central Bank, Research Department. [Downloadable!]
  3. Joshua V. Rosenberg, 2003. "Nonparametric pricing of multivariate contingent claims," Staff Reports 162, Federal Reserve Bank of New York. [Downloadable!]
  4. Paul Doukhan & Jean-David Fermanian & Gabriel Lang, 2009. "An empirical central limit theorem with applications to copulas under weak dependence," Statistical Inference for Stochastic Processes, Springer, vol. 12(1), pages 65-87, February. [Downloadable!] (restricted)
Statistics
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