IDEAS home Printed from https://ideas.repec.org/a/eac/articl/11-07.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.unagaliciamoderna.com/eawp/coldata/upload/multivariate_risk-return_decision_univ_split.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Y.K. Tse & Albert K.C. Tsui, 2000. "A Multivariate GARCH Model with Time-Varying Correlations," Econometrics 0004007, University Library of Munich, Germany.
    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. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tsuji, Chikashi, 2020. "Correlation and spillover effects between the US and international banking sectors: New evidence and implications for risk management," International Review of Financial Analysis, Elsevier, vol. 70(C).
    2. Bernardi, Mauro & Catania, Leopoldo, 2018. "Portfolio optimisation under flexible dynamic dependence modelling," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 1-18.
    3. Boudt, Kris & Cornilly, Dries & Verdonck, Tim, 2020. "Nearest comoment estimation with unobserved factors," Journal of Econometrics, Elsevier, vol. 217(2), pages 381-397.
    4. Conrad, Christian & Karanasos, Menelaos & Zeng, Ning, 2011. "Multivariate fractionally integrated APARCH modeling of stock market volatility: A multi-country study," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 147-159, January.
    5. Dahiru A. Balaa & Taro Takimotob, 2017. "Stock markets volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 17(1), pages 25-48, March.
    6. Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
    7. Ruey S. Tsay, 2007. "Multivariate volatility models," Papers math/0702815, arXiv.org.
    8. Hafner, Christian M. & Wang, Linqi, 2023. "A dynamic conditional score model for the log correlation matrix," Journal of Econometrics, Elsevier, vol. 237(2).
    9. Pesaran, M. Hashem & Schleicher, Christoph & Zaffaroni, Paolo, 2009. "Model averaging in risk management with an application to futures markets," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 280-305, March.
    10. Maria Kasch & Massimiliano Caporin, 2013. "Volatility Threshold Dynamic Conditional Correlations: An International Analysis," Journal of Financial Econometrics, Oxford University Press, vol. 11(4), pages 706-742, September.
    11. repec:cte:wsrepe:es142416 is not listed on IDEAS
    12. Storti, Giuseppe & Wang, Chao, 2022. "A multivariate semi-parametric portfolio risk optimization and forecasting framework," MPRA Paper 115266, University Library of Munich, Germany.
    13. Drew Creal & Siem Jan Koopman & André Lucas, 2011. "A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(4), pages 552-563, October.
    14. Zouheir Mighri & Faysal Mansouri, 2014. "Modeling international stock market contagion using multivariate fractionally integrated APARCH approach," Cogent Economics & Finance, Taylor & Francis Journals, vol. 2(1), pages 1-25, December.
    15. repec:awi:wpaper:0472 is not listed on IDEAS
    16. Donghang Luo & Ke Zhu & Huan Gong & Dong Li, 2020. "Testing error distribution by kernelized Stein discrepancy in multivariate time series models," Papers 2008.00747, arXiv.org.
    17. Zouheir Mighri, 2018. "On the Dynamic Linkages Among International Emerging Currencies," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(2), pages 427-473, June.
    18. Jensen, Mark J. & Maheu, John M., 2013. "Bayesian semiparametric multivariate GARCH modeling," Journal of Econometrics, Elsevier, vol. 176(1), pages 3-17.
    19. Luc, BAUWENS & Walid, BEN OMRANE & Erick, Rengifo, 2006. "Intra-Daily FX Optimal Portfolio Allocation," Discussion Papers (ECON - Département des Sciences Economiques) 2006005, Université catholique de Louvain, Département des Sciences Economiques.
    20. 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.
    21. M. Hashem Pesaran & Bahram Pesaran, 2007. "Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution," CESifo Working Paper Series 2056, CESifo.
    22. Wei Kuang, 2024. "High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-35, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eac:articl:11/07. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jose González Seoane (email available below). General contact details of provider: https://edirc.repec.org/data/cecorea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.