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

Author

Listed:
  • Nikolaus Hautsch
  • Julia Schuamburg
  • Melanie Schienle

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 Rd + 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 reestimate 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.

Suggested Citation

  • Nikolaus Hautsch & Julia Schuamburg & Melanie Schienle, 2012. "Modeling Time-Varying Dependencies between Positive-Valued High-Frequency Time Series," SFB 649 Discussion Papers SFB649DP2012-054, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2012-054
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    References listed on IDEAS

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    More about this item

    Keywords

    vector multiplicative error model; copula; time-varying copula; highfrequency data;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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