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Time Inhomogeneous Multiple Volatility Modelling

Author

Listed:
  • Wolfgang Haerdle

    (Humboldt Universitaet zu Berlin)

  • Helmut Herwartz

    (Humboldt Universitaet zu Berlin)

  • Volodia Spokoiny

    (Humboldt Universitaet zu Berlin)

Abstract

Price variations observed at speculative markets exhibit positive autocorrelation and cross correlation among a set of assets, stock market indices, exchange rates etc. A particular problem in investigating multivariate volatility processes arises from the high dimensionality implied by a simultaneous analysis of variances and covariances. Parametric volatility models as e.g. the multivariate version of the prominent GARCH model become easily intractable for empirical work. We propose an adaptive procedure that aims to identify periods of second order homogeneity for each moment in time. Similar to principal component analysis the dimensionality problem is solved by transforming a multivariate series into a set of univariate processes. We discuss thoroughly implementation issues which naturally arise in the framework of adaptive modelling. Theoretical and Monte Carlo results are given. The empirical performance of the new method is illustrated by an application to a bivariate exchange rate series. Empirical results are compared to a parametric approach, namely the multivariate GARCH model.

Suggested Citation

  • Wolfgang Haerdle & Helmut Herwartz & Volodia Spokoiny, 2000. "Time Inhomogeneous Multiple Volatility Modelling," Econometric Society World Congress 2000 Contributed Papers 1429, Econometric Society.
    • Handle: RePEc:ecm:wc2000:1429
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    References listed on IDEAS

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    Cited by:

    1. Cizek, P. & Haerdle, W. & Spokoiny, V., 2007. "Adaptive Pointwise Estimation in Time-Inhomogeneous Time-Series Models," Discussion Paper 2007-35, Tilburg University, Center for Economic Research.

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