IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/119910.html
   My bibliography  Save this paper

Navigating extreme market fluctuations: asset allocation strategies in developed vs. emerging economies

Author

Listed:
  • Bonga-Bonga, Lumengo
  • Montshioa, Keitumetse

Abstract

This paper contributes to the literature on portfolio allocation by assessing how assets from emerging and developed stock markets can be allocated efficiently during crisis periods. Towards this end, the paper proposes an approach to portfolio allocation that combines traditional portfolio theory with extreme value theory (EVT) based on Generalised Pareto Distributions (GPDs) and Generalised Extreme Values (GEVs). The results of the empirical analysis show that for the mean-variance portfolio constructed from GPD, the emerging market portfolio outperforms both the international portfolio, the combination of emerging and developed market assets, and the developed market portfolio. However, the developed market portfolio outperforms the emerging market portfolio for the mean-variance portfolio constructed from GEV distribution. The paper attributes these different outcomes to the intended objectives of these extreme-value approaches in the context of portfolio selection. These results offer essential guidance for investors and asset managers during the construction of portfolios in times of crisis. They highlight that the effectiveness of a portfolio is significantly influenced by its predefined objectives. Ultimately, these objectives are crucial in deciding the most suitable approach for portfolio construction.

Suggested Citation

  • Bonga-Bonga, Lumengo & Montshioa, Keitumetse, 2024. "Navigating extreme market fluctuations: asset allocation strategies in developed vs. emerging economies," MPRA Paper 119910, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:119910
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/119910/1/MPRA_paper_119910.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Walter Briec & Kristiaan Kerstens & Octave Jokung, 2007. "Mean-Variance-Skewness Portfolio Performance Gauging: A General Shortage Function and Dual Approach," Management Science, INFORMS, vol. 53(1), pages 135-149, January.
    2. Cohen, Benjamin H. & Remolona, Eli M., 2008. "Information flows during the Asian crisis: Evidence from closed-end funds," Journal of International Money and Finance, Elsevier, vol. 27(4), pages 636-653, June.
    3. Harvey, Campbell R, 1995. "Predictable Risk and Returns in Emerging Markets," The Review of Financial Studies, Society for Financial Studies, vol. 8(3), pages 773-816.
    4. R. Glenn Hubbard, 1991. "Introduction to "Financial Markets and Financial Crises"," NBER Chapters, in: Financial Markets and Financial Crises, pages 1-10, National Bureau of Economic Research, Inc.
    5. Hong-Ghi Min & Judith A. McDonald & Sang-Ook Shin, 2016. "What Makes a Safe Haven? Equity and Currency Returns for Six OECD Countries during the Financial Crisis," Annals of Economics and Finance, Society for AEF, vol. 17(2), pages 365-402, November.
    6. Alain Kabundi & John Mwamba Muteba, 2011. "Extreme Value At Risk: A Scenario For Risk Management," South African Journal of Economics, Economic Society of South Africa, vol. 79(2), pages 173-183, June.
    7. Boubaker, Sabri & Jouini, Jamel & Lahiani, Amine, 2016. "Financial contagion between the US and selected developed and emerging countries: The case of the subprime crisis," The Quarterly Review of Economics and Finance, Elsevier, vol. 61(C), pages 14-28.
    8. Jansen, Dennis W & de Vries, Casper G, 1991. "On the Frequency of Large Stock Returns: Putting Booms and Busts into Perspective," The Review of Economics and Statistics, MIT Press, vol. 73(1), pages 18-24, February.
    9. Manuel Tarrazo & Ricardo Úbeda, 2012. "Minimum-variance versus tangent portfolios – A note," Journal of Asset Management, Palgrave Macmillan, vol. 13(3), pages 186-195, June.
    10. Corsetti, Giancarlo & Pesenti, Paolo & Roubini, Nouriel, 1999. "Paper tigers?: A model of the Asian crisis," European Economic Review, Elsevier, vol. 43(7), pages 1211-1236, June.
    11. Manfred Gilli & Evis këllezi, 2006. "An Application of Extreme Value Theory for Measuring Financial Risk," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 207-228, May.
    12. Nelson, Daniel B., 1992. "Filtering and forecasting with misspecified ARCH models I : Getting the right variance with the wrong model," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 61-90.
    13. Hyung, Namwon & de Vries, Casper G., 2007. "Portfolio selection with heavy tails," Journal of Empirical Finance, Elsevier, vol. 14(3), pages 383-400, June.
    14. Pellegrini, Santiago & Ruiz, Esther & Espasa, Antoni, 2010. "Conditionally heteroscedastic unobserved component models and their reduced form," Economics Letters, Elsevier, vol. 107(2), pages 88-90, May.
    15. Jakša Cvitanić & Vassilis Polimenis & Fernando Zapatero, 2008. "Optimal portfolio allocation with higher moments," Annals of Finance, Springer, vol. 4(1), pages 1-28, January.
    16. Campbell Harvey & John Liechty & Merrill Liechty & Peter Muller, 2010. "Portfolio selection with higher moments," Quantitative Finance, Taylor & Francis Journals, vol. 10(5), pages 469-485.
    17. François‐Serge Lhabitant, 2001. "Assessing Market Risk for Hedge Funds and Hedge Fund Portfolios," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 2(4), pages 16-32, March.
    18. Juha Uotila & Markku Maula & Thomas Keil & Shaker A. Zahra, 2009. "Exploration, exploitation, and financial performance: analysis of S&P 500 corporations," Strategic Management Journal, Wiley Blackwell, vol. 30(2), pages 221-231, February.
    19. Ibragimov, Marat & Ibragimov, Rustam & Kattuman, Paul, 2013. "Emerging markets and heavy tails," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2546-2559.
    20. Montfort Mlachila & Sarah Sanya, 2016. "Post-crisis bank behavior: lessons from Mercosur," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 11(4), pages 584-606, September.
    21. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    22. Marco Tronzano, 2020. "Safe-Haven Assets, Financial Crises, and Macroeconomic Variables: Evidence from the Last Two Decades (2000–2018)," JRFM, MDPI, vol. 13(3), pages 1-21, February.
    23. Bonga-Bonga, Lumengo, 2018. "Uncovering equity market contagion among BRICS countries: An application of the multivariate GARCH model," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 36-44.
    24. Muteba Mwamba, John W. & Hammoudeh, Shawkat & Gupta, Rangan, 2017. "Financial tail risks in conventional and Islamic stock markets: A comparative analysis," Pacific-Basin Finance Journal, Elsevier, vol. 42(C), pages 60-82.
    25. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    26. Susmel, Raul, 2001. "Extreme observations and diversification in Latin American emerging equity markets," Journal of International Money and Finance, Elsevier, vol. 20(7), pages 971-986, December.
    27. Yannick Malevergne & Vladilen Pisarenko & Didier Sornette, 2006. "On the Power of Generalized Extreme Value (GEV) and Generalized Pareto Distribution (GPD) Estimators for Empirical Distributions of Stock Returns," Post-Print hal-02311834, HAL.
    28. Arzac, Enrique R. & Bawa, Vijay S., 1977. "Portfolio choice and equilibrium in capital markets with safety-first investors," Journal of Financial Economics, Elsevier, vol. 4(3), pages 277-288, May.
    29. Muteba Mwamba, John, 2012. "On the optimality of hedge fund investment strategies: a Bayesian skew t distribution model," MPRA Paper 50323, University Library of Munich, Germany.
    30. Pownall, Rachel A. J. & Koedijk, Kees G., 1999. "Capturing downside risk in financial markets: the case of the Asian Crisis," Journal of International Money and Finance, Elsevier, vol. 18(6), pages 853-870, December.
    31. Bartram, Söhnke M. & Bodnar, Gordon M., 2012. "Crossing the lines: The conditional relation between exchange rate exposure and stock returns in emerging and developed markets," Journal of International Money and Finance, Elsevier, vol. 31(4), pages 766-792.
    32. Baur, Dirk G., 2012. "Financial contagion and the real economy," Journal of Banking & Finance, Elsevier, vol. 36(10), pages 2680-2692.
    33. Thomas C. Chiang & Yuanqing Zhang, 2018. "An Empirical Investigation of Risk-Return Relations in Chinese Equity Markets: Evidence from Aggregate and Sectoral Data," IJFS, MDPI, vol. 6(2), pages 1-22, March.
    34. Cepoi, Cosmin-Octavian, 2020. "Asymmetric dependence between stock market returns and news during COVID-19 financial turmoil," Finance Research Letters, Elsevier, vol. 36(C).
    35. K. Saranya & P. Prasanna, 2014. "Portfolio Selection and Optimization with Higher Moments: Evidence from the Indian Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 21(2), pages 133-149, May.
    36. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    Full references (including those not matched with items on IDEAS)

    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. Montshioa, Keitumetse & Muteba Mwamba, John Weirstrass & Bonga-Bonga, Lumengo, 2021. "Asset allocation in extreme market conditions: a comparative analysis between developed and emerging economies," MPRA Paper 106248, University Library of Munich, Germany.
    2. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    3. DiTraglia, Francis J. & Gerlach, Jeffrey R., 2013. "Portfolio selection: An extreme value approach," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 305-323.
    4. Moore, Kyle & Sun, Pengfei & de Vries, Casper G. & Zhou, Chen, 2013. "The cross-section of tail risks in stock returns," MPRA Paper 45592, University Library of Munich, Germany.
    5. Salhi, Khaled & Deaconu, Madalina & Lejay, Antoine & Champagnat, Nicolas & Navet, Nicolas, 2016. "Regime switching model for financial data: Empirical risk analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 148-157.
    6. Claudeci Da Silva & Hugo Agudelo Murillo & Joaquim Miguel Couto, 2014. "Early Warning Systems: Análise De Ummodelo Probit De Contágio De Crise Dos Estados Unidos Para O Brasil(2000-2010)," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 110, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    7. Dias, Alexandra, 2014. "Semiparametric estimation of multi-asset portfolio tail risk," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 398-408.
    8. Moore, Kyle & Sun, Pengei & de Vries, Casper G. & Zhou, Chen, 2013. "The drivers of downside equity tail risk," MPRA Paper 45591, University Library of Munich, Germany.
    9. Chebbi, Ali & Hedhli, Amel, 2022. "Revisiting the accuracy of standard VaR methods for risk assessment: Using the Copula–EVT multidimensional approach for stock markets in the MENA region," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 430-445.
    10. Haque, Mahfuzul & Kabir Hassan, M. & Varela, Oscar, 2004. "Safety-first portfolio optimization for US investors in emerging global, Asian and Latin American markets," Pacific-Basin Finance Journal, Elsevier, vol. 12(1), pages 91-116, January.
    11. Zhou, Chen, 2010. "Dependence structure of risk factors and diversification effects," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 531-540, June.
    12. Gagnon, Louis & Karolyi, G. Andrew, 2006. "Price and Volatility Transmission across Borders," Working Paper Series 2006-5, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    13. Susmel, Raul, 2001. "Extreme observations and diversification in Latin American emerging equity markets," Journal of International Money and Finance, Elsevier, vol. 20(7), pages 971-986, December.
    14. Gu, Zhiye & Ibragimov, Rustam, 2018. "The “Cubic Law of the Stock Returns” in emerging markets," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 182-190.
    15. Chen Zou, 2009. "Dependence structure of risk factors and diversification effects," DNB Working Papers 219, Netherlands Central Bank, Research Department.
    16. Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.
    17. Chiang, Thomas C., 2019. "Empirical analysis of intertemporal relations between downside risks and expected returns—Evidence from Asian markets," Research in International Business and Finance, Elsevier, vol. 47(C), pages 264-278.
    18. Chen, Zhimin & Ibragimov, Rustam, 2019. "One country, two systems? The heavy-tailedness of Chinese A- and H- share markets," Emerging Markets Review, Elsevier, vol. 38(C), pages 115-141.
    19. Fong, Wai Mun, 1997. "Robust beta estimation: Some empirical evidence," Review of Financial Economics, Elsevier, vol. 6(2), pages 167-186.
    20. Danielsson, Jon & Jorgensen, Bjorn N. & Sarma, Mandira & de Vries, Casper G., 2006. "Comparing downside risk measures for heavy tailed distributions," Economics Letters, Elsevier, vol. 92(2), pages 202-208, August.

    More about this item

    Keywords

    Extreme Value Theory; General Pareto Distribution; Emerging and developed markets; portfolio optimisation; mean-variance.;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:119910. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.