Nonparametric Estimation of Copulas for Time Series
University of Geneva - HEC; Swiss Finance Institute
FAME Research Paper No. 57
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 is given for European and US stock index returns. Another empirical illustration deals with Danish data on fire insurance losses.
Number of Pages in PDF File: 37
JEL Classification: C14, D81, G10, G21, G22working papers series
Date posted: March 12, 2003
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