Time varying vine copulas for multivariate returns (in Russian)

We analyze the multivariate distribution of financial returns using time varying conditional vine copulas. We present the d-Stage Maximum Likelihood (dSML) estimator which is shown to be not only consistent and asymptotically normal, but also more computationally attractive than the standard ML or Patton's 2SML. Using dSML, we fit vine copulas to returns of a portfolio on emerging market currencies.

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Year of publication:	2014
Authors:	Groshev, Oleg
Published in:	Quantile. - Quantile. - 2014, 12, p. 53-67
Publisher:	Quantile
Subject:	multidimensional time series \| vine copulas \| dSML estimator \| computational efficiency

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Extent:	application/pdf
Type of publication:	Article
Classification:	C13 - Estimation ; c58 ; G15 - International Financial Markets
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010744672