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Modeling Time-Varying Dependencies Between Positive-Valued High-Frequency Time Series

Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 213)

Abstract

Multiplicative error models (MEM) became a standard tool for modeling conditional durations of intraday transactions, realized volatilities, and trading volumes. The parametric estimation of the corresponding multivariate model, the so-called vector MEM (VMEM), requires a specification of the joint error term distribution, which is due to the lack of multivariate distribution functions on \(\mathbb{R}_{+}^{d}\) defined via a copula. Maximum likelihood estimation is based on the assumption of constant copula parameters and therefore leads to invalid inference if the dependence exhibits time variations or structural breaks. Hence, we suggest to test for time-varying dependence by calibrating a time-varying copula model and to re-estimate the VMEM based on identified intervals of homogenous dependence. This paper summarizes the important aspects of (V)MEM, its estimation, and a sequential test for changes in the dependence structure. The techniques are applied in an empirical example.

Keywords

Change Point Tail Dependency Copula Model Likelihood Ratio Test Statistic Archimedean Copula 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The financial support from the Deutsche Forschungsgemeinschaft via SFB 649 Ökonomisches Risiko, Humboldt-Universität zu Berlin is gratefully acknowledged.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Business and EconomicsHumboldt-Universität zu BerlinBerlinGermany

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