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Estimating dynamic copula dependence using intraday data

  • Lidan Grossmass EMAIL logo and Ser-Huang Poon
Published/Copyright: December 2, 2014
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Studies in Nonlinear Dynamics & Econometrics
From the journal Studies in Nonlinear Dynamics & Econometrics Volume 19 Issue 4

Abstract

We estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data. Using tick data of three stock returns of the period before and during the credit crisis, we find that our dependence estimator better captures the steep increase in dependence during the onset of the crisis as compared to other commonly used time-varying copula methods. Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model. We show that for out-of-sample forecasts, our dependence estimator performs better than the constant estimator and other commonly used time-varying copula dependence estimators.

Keywords: copula; high frequency data; intraday dependence; time-varying dependence; value-at-risk
JEL classification: C14; C18; C58; G17
Corresponding author: Lidan Grossmass, Department of Economics, University of Konstanz, Box 124, 78457 Konstanz, Germany, e-mail:

Acknowledgments

We would like to thank two anonymous referees, Bruce Mizrach (the Editor), Winfried Pohlmeier, Joachim Grammig and Yongwoong Lee for their insightful comments and suggestions, as well as Heikki Seppala for checking the mathematical proofs in the Web Appendix Section B. The research leading to these results has received funding from the European Community’s Seventh Framework Programme FP7-PEOPLE-ITN-2008 under grant agreement number PITN-GA-2009-237984. The funding is gratefully acknowledged.

Appendices

Figure 10 Constant and time varying dependence estimators Co (black solid lines), RW (black dashed-dotted lines), AR (blue solid lines) and ID (red dotted lines) for c-ibm between 2006 and 2008. Top row: Gaussian and Plackett copulas. Left middle and bottom rows: Student’s-t copula (rho and v); right middle and bottom rows: SJC copula (upper and lower tail parameters).
Figure 10

Constant and time varying dependence estimators Co (black solid lines), RW (black dashed-dotted lines), AR (blue solid lines) and ID (red dotted lines) for c-ibm between 2006 and 2008. Top row: Gaussian and Plackett copulas. Left middle and bottom rows: Student’s-t copula (rho and v); right middle and bottom rows: SJC copula (upper and lower tail parameters).

Figure 11 Constant and time varying dependence estimators Co (black solid lines), RW (black dashed-dotted lines), AR (blue solid lines) and ID (red dotted lines) for jpm-ibm between 2006 and 2008. Top row: Gaussian and s. Gumbel copulas. Left middle: Plackett copula; right middle and bottom rows: SJC copula (upper and lower tail parameters).
Figure 11

Constant and time varying dependence estimators Co (black solid lines), RW (black dashed-dotted lines), AR (blue solid lines) and ID (red dotted lines) for jpm-ibm between 2006 and 2008. Top row: Gaussian and s. Gumbel copulas. Left middle: Plackett copula; right middle and bottom rows: SJC copula (upper and lower tail parameters).

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Supplemental Material:

The online version of this article (DOI: 10.1515/snde-2013-0123) offers supplementary material, available to authorized users.

Published Online: 2014-12-2
Published in Print: 2015-9-1

©2015 by De Gruyter

Articles in the same Issue

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  2. A video interview of James Stock
  3. More powerful cointegration tests with non-normal errors
  4. Asset pricing with flexible beliefs
  5. Improving model performance with the integrated wavelet denoising method
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https://doi.org/10.1515/snde-2013-0123
Keywords for this article
copula; high frequency data; intraday dependence; time-varying dependence; value-at-risk

Articles in the same Issue

  1. Frontmatter
  2. A video interview of James Stock
  3. More powerful cointegration tests with non-normal errors
  4. Asset pricing with flexible beliefs
  5. Improving model performance with the integrated wavelet denoising method
  6. Noncausality and inflation persistence
  7. A triple-threshold leverage stochastic volatility model
  8. Estimating dynamic copula dependence using intraday data
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