Nonparametric Estimation of Copulas for Time Series | Zendy

Olivier Scaillet | Zendy; JeanDavid Fermanian | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Nonparametric Estimation of Copulas for Time Series

Author(s) -

Olivier Scaillet,

JeanDavid Fermanian

Publication year - 2003

Publication title -

ssrn electronic journal

Language(s) - English

Resource type - Journals

ISSN - 1556-5068

DOI - 10.2139/ssrn.372142

Subject(s) - nonparametric statistics , series (stratigraphy) , econometrics , estimation , time series , statistics , copula (linguistics) , mathematics , computer science , economics , management , paleontology , biology

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 es- timators 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 illus- tration containing a comparison with the independent, comotonic and Gaussian copulas is given for European and US stock index returns. Key words: Nonparametric, Kernel, Time Series, Copulas, Dependence Measures, Risk Man-

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research