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Multivariate Risk-Return Decision Making Within Dynamic Estimation

Author

Listed:
  • Josip Arneric

    (University of Split, Faculty of Economics, Croatia.)

  • Elza Jurun

    (University of Split, Faculty of Economics, Croatia.)

  • Snježana Pivac

    (University of Split, Faculty of Economics, Croatia)

Abstract

Risk management in this paper is focused on multivariate risk-return decision making assuming time-varying estimation. Empirical research in risk management showed that the static "mean-variance" methodology in portfolio optimization is very restrictive with unrealistic assumptions. The objective of this paper is estimation of time-varying portfolio stocks weights by constraints on risk measure. Hence, risk measure dynamic estimation is used in risk controlling. By risk control manager makes free supplementary capital for new investments. Univariate modeling approach is not appropriate, even when portfolio returns are treated as one variable. Portfolio weights are time-varying, and therefore it is necessary to reestimate whole model over time. Using assumption of bivariate Student´s t-distribution, in multivariate GARCH(p,q) models, it becomes possible to forecast time-varying portfolio risk much more precisely. The complete procedure of analysis is established from Zagreb Stock Exchange using daily observations of Pliva and Podravka stocks.

Suggested Citation

  • Josip Arneric & Elza Jurun & Snježana Pivac, 2008. "Multivariate Risk-Return Decision Making Within Dynamic Estimation," Economic Analysis Working Papers (2002-2010). Atlantic Review of Economics (2011-2016), Colexio de Economistas de A Coruña, Spain and Fundación Una Galicia Moderna, vol. 7, pages 1-11, October.
    • Handle: RePEc:eac:articl:11/07
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    References listed on IDEAS

    as
    1. Y.K. Tse & Albert K.C. Tsui, 2000. "A Multivariate GARCH Model with Time-Varying Correlations," Econometrics 0004007, University Library of Munich, Germany.
    2. Bauwens, Luc & Laurent, Sebastien, 2005. "A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 346-354, July.
    3. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    4. RENGIFO, Erick & ROMBOUTS, Jeroen, 2004. "Dynamic optimal portfolio selection in a VaR framework," LIDAM Discussion Papers CORE 2004057, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Maria Kasch-Haroutounian & Simon Price, 2001. "Volatility in the transition markets of Central Europe," Applied Financial Economics, Taylor & Francis Journals, vol. 11(1), pages 93-105.
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    Cited by:

    1. Elza Jurun & Snježana Pivac, 2011. "Comparative regional GDP analysis: case study of Croatia," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 19(3), pages 319-335, September.

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