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Long Memory Dynamics for Multivariate Dependence under Heavy Tails

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Author Info

  • Pawel Janus

    (VU University Amsterdam)

  • Siem Jan Koopman

    (VU University Amsterdam)

  • Andr� Lucas

    (VU University Amsterdam)

Abstract

We develop a new simultaneous time series model for volatility and dependence with long memory (fractionally integrated) dynamics and heavy-tailed densities. Our new multivariate model accounts for typical empirical features in financial time series while being robust to outliers or jumps in the data. In the empirical study for four Dow Jones equities, we find that the degree of memory in the volatilities of the equity return series is similar, while the degree of memory in correlations between the series varies significantly. The forecasts from our model are compared with high-frequency realised volatility and dependence measures. The forecast accuracy is overall higher compared to those from some well-known competing benchmark models.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 11-175/2/DSF28.

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Date of creation: 12 Dec 2011
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Handle: RePEc:dgr:uvatin:20110175

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Web page: http://www.tinbergen.nl

Related research

Keywords: fractional integration; correlation; Student's t copula; time-varying dependence; multivariate volatility;

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References

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Cited by:
  1. repec:dgr:uvatin:2012059 is not listed on IDEAS
  2. Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2012. "Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes," Tinbergen Institute Discussion Papers 12-059/4, Tinbergen Institute.

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