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Modelling the Dynamic Dependence Structure in Multivariate Financial Time Series

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  • Mihaela Şerban
  • Anthony Brockwell
  • John Lehoczky
  • Sanjay Srivastava

Abstract

The dependence structure in multivariate financial time series is of great importance in portfolio management. By studying daily return histories of 17 exchange-traded index funds, we identify important features of the data, and we propose two new models to capture these features. The first is an extension of the multivariate BEKK (Baba, Engle, Kraft, Kroner) model, which includes a multivariate t-type error distribution with different degrees of freedom. We demonstrate that this error distribution is able to accommodate different levels of heavy-tailed behaviour and thus provides a better fit than models based on a multivariate t-with a common degree of freedom. The second model is copula based, and can be regarded as an extension of the standard and the generalized dynamic conditional correlation model [Engle, Journal of Business and Economics Statistics (2002) Vol. 17, 425-446; Cappiello et al. (2003) Working paper, UCSD] to a Student copula. Model comparison is carried out using criteria including the Akaike information criteria and Bayesian information criteria. We also evaluate the two models from an asset-allocation perspective using a three-asset portfolio as an example, constructing optimal portfolios based on the Markowitz theory. Our results indicate that, for our data, the proposed models both outperform the standard BEKK model, with the copula model performing better than the extension of the BEKK model. Copyright 2007 The Authors Journal compilation 2007 Blackwell Publishing Ltd.

Suggested Citation

  • Mihaela Şerban & Anthony Brockwell & John Lehoczky & Sanjay Srivastava, 2007. "Modelling the Dynamic Dependence Structure in Multivariate Financial Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 763-782, September.
  • Handle: RePEc:bla:jtsera:v:28:y:2007:i:5:p:763-782
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    Cited by:

    1. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.
    2. Stanisław Wanat & Sławomir Śmiech & Monika Papież, 2016. "In Search Of Hedges And Safe Havens In Global Financial Markets," Statistics in Transition. New Series, Polish Statistical Association, vol. 17(3), pages 557-574, September.
    3. Nicholas Taylor, 2014. "The Economic Value of Volatility Forecasts: A Conditional Approach," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(3), pages 433-478.
    4. Miralles-Marcelo, José Luis & Miralles-Quirós, María del Mar & Miralles-Quirós, José Luis, 2015. "Improving international diversification benefits for US investors," The North American Journal of Economics and Finance, Elsevier, vol. 32(C), pages 64-76.
    5. Aloui, Riadh & Aïssa, Mohamed Safouane Ben & Nguyen, Duc Khuong, 2011. "Global financial crisis, extreme interdependences, and contagion effects: The role of economic structure?," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 130-141, January.
    6. Rossi, E. & Spazzini, F., 2010. "Model and distribution uncertainty in multivariate GARCH estimation: A Monte Carlo analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2786-2800, November.
    7. Eli Bouri & Andre Eid & Imad Kachacha, 2014. "The Dynamic Behaviour and Determinants of Linkages among Middle Eastern and North African Stock Exchanges," Economic Issues Journal Articles, Economic Issues, vol. 19(1), pages 1-22, March.
    8. Varneskov, Rasmus & Voev, Valeri, 2013. "The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 83-95.
    9. Roxana Chiriac & Valeri Voev, 2011. "Modelling and forecasting multivariate realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 922-947, September.
    10. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    11. Wanat, Stanisław & Papież, Monika & Śmiech, Sławomir, 2014. "The conditional dependence structure between precious metals: a copula-GARCH approach," MPRA Paper 56664, University Library of Munich, Germany.
    12. Sunil S. Poshakwale & Anandadeep Mandal, 2017. "Sources of time varying return comovements during different economic regimes: evidence from the emerging Indian equity market," Review of Quantitative Finance and Accounting, Springer, vol. 48(4), pages 859-892, May.
    13. Wang, Chou-Wen & Yang, Sharon S. & Huang, Hong-Chih, 2015. "Modeling multi-country mortality dependence and its application in pricing survivor index swaps—A dynamic copula approach," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 30-39.

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