IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v122y2023ics0264999323000470.html
   My bibliography  Save this article

Portfolio optimization in the presence of tail correlation

Author

Listed:
  • Ben Abdelaziz, Fouad
  • Chibane, Messaoud

Abstract

We investigate the relative performance of optimal versus naive portfolio strategies. The accepted status on this question is that naive diversification outperforms optimal strategies. We revisit this question using U.S. data for equity, Treasury bonds, Gold and Crude Oil between 2002 and 2022 by analyzing the portfolio of investors displaying constant relative risk aversion who also consider tail behavior in the dynamics of assets. We use moment generating functions applied to non-Gaussian processes to obtain accurate model estimation as well as an efficient control variate for the utility maximization problem. Our results show that risk-averse investors that are aware of tail dynamics consistently outperform the most standard portfolio strategies. In particular, highly risk-averse investors substantially outperform the so-called naive 1/N portfolio in both pre-COVID-19 and post-COVID-19 periods. Thus, true portfolio diversification requires considering both the complexity of asset dynamics and realistic risk aversion structures.

Suggested Citation

  • Ben Abdelaziz, Fouad & Chibane, Messaoud, 2023. "Portfolio optimization in the presence of tail correlation," Economic Modelling, Elsevier, vol. 122(C).
    • Handle: RePEc:eee:ecmode:v:122:y:2023:i:c:s0264999323000470
    DOI: 10.1016/j.econmod.2023.106235
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999323000470
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econmod.2023.106235?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Robert J. Barro & Tao Jin, 2011. "On the Size Distribution of Macroeconomic Disasters," Econometrica, Econometric Society, vol. 79(5), pages 1567-1589, September.
    2. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    3. Hwang, Inchang & Xu, Simon & In, Francis, 2018. "Naive versus optimal diversification: Tail risk and performance," European Journal of Operational Research, Elsevier, vol. 265(1), pages 372-388.
    4. Jun Liu & Francis A. Longstaff & Jun Pan, 2003. "Dynamic Asset Allocation with Event Risk," Journal of Finance, American Finance Association, vol. 58(1), pages 231-259, February.
    5. Eric Jondeau & Michael Rockinger, 2006. "Optimal Portfolio Allocation under Higher Moments," European Financial Management, European Financial Management Association, vol. 12(1), pages 29-55, January.
    6. Bryan Kelly & Hao Jiang, 2014. "Editor's Choice Tail Risk and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 27(10), pages 2841-2871.
    7. Bart Diris & Franz Palm & Peter Schotman, 2015. "Long-Term Strategic Asset Allocation: An Out-of-Sample Evaluation," Management Science, INFORMS, vol. 61(9), pages 2185-2202, September.
    8. Lionel Martellini & Volker Ziemann, 2010. "Improved Estimates of Higher-Order Comoments and Implications for Portfolio Selection," The Review of Financial Studies, Society for Financial Studies, vol. 23(4), pages 1467-1502, April.
    9. Heinlein, Reinhold & Legrenzi, Gabriella D. & Mahadeo, Scott M.R., 2021. "Crude oil and stock markets in the COVID-19 crisis: Evidence from oil exporters and importers," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 223-229.
    10. Jiang, Chonghui & Du, Jiangze & An, Yunbi, 2019. "Combining the minimum-variance and equally-weighted portfolios: Can portfolio performance be improved?," Economic Modelling, Elsevier, vol. 80(C), pages 260-274.
    11. Massimo Guidolin & Allan Timmermann, 2008. "International asset allocation under regime switching, skew, and kurtosis preferences," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 889-935, April.
    12. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    13. Tim Bollerslev & Viktor Todorov, 2011. "Estimation of Jump Tails," Econometrica, Econometric Society, vol. 79(6), pages 1727-1783, November.
    14. Merton, Robert C, 1969. "Lifetime Portfolio Selection under Uncertainty: The Continuous-Time Case," The Review of Economics and Statistics, MIT Press, vol. 51(3), pages 247-257, August.
    15. Aït-Sahalia, Yacine & Matthys, Felix, 2019. "Robust consumption and portfolio policies when asset prices can jump," Journal of Economic Theory, Elsevier, vol. 179(C), pages 1-56.
    16. Cui, Tianxiang & Ding, Shusheng & Jin, Huan & Zhang, Yongmin, 2023. "Portfolio constructions in cryptocurrency market: A CVaR-based deep reinforcement learning approach," Economic Modelling, Elsevier, vol. 119(C).
    17. Jakša Cvitanić & Vassilis Polimenis & Fernando Zapatero, 2008. "Optimal portfolio allocation with higher moments," Annals of Finance, Springer, vol. 4(1), pages 1-28, January.
    18. 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.
    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. Li, Guowen & Jing, Zhongbo & Li, Jingyu & Feng, Yuyao, 2023. "Drivers of risk correlation among financial institutions: A study based on a textual risk disclosure perspective," Economic Modelling, Elsevier, vol. 128(C).

    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. Lassance, Nathan & Vrins, Frédéric, 2021. "Portfolio selection with parsimonious higher comoments estimation," Journal of Banking & Finance, Elsevier, vol. 126(C).
    2. Le, Trung H., 2021. "International portfolio allocation: The role of conditional higher moments," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 33-57.
    3. Xuan Vinh Vo & Thi Tuan Anh Tran, 2021. "Higher-order comoments and asset returns: evidence from emerging equity markets," Annals of Operations Research, Springer, vol. 297(1), pages 323-340, February.
    4. Xing Jin & Dan Luo & Xudong Zeng, 2021. "Tail Risk and Robust Portfolio Decisions," Management Science, INFORMS, vol. 67(5), pages 3254-3275, May.
    5. Dias, Alexandra, 2016. "The economic value of controlling for large losses in portfolio selection," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 81-91.
    6. Brinkmann, Felix & Kempf, Alexander & Korn, Olaf, 2014. "Forward-looking measures of higher-order dependencies with an application to portfolio selection," CFR Working Papers 13-08 [rev.], University of Cologne, Centre for Financial Research (CFR).
    7. Penaranda, Francisco, 2007. "Portfolio choice beyond the traditional approach," LSE Research Online Documents on Economics 24481, London School of Economics and Political Science, LSE Library.
    8. DiTraglia, Francis J. & Gerlach, Jeffrey R., 2013. "Portfolio selection: An extreme value approach," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 305-323.
    9. Ryo Kinoshita, 2015. "Asset allocation under higher moments with the GARCH filter," Empirical Economics, Springer, vol. 49(1), pages 235-254, August.
    10. Lassance, Nathan & Vrins, Frédéric, 2019. "Robust portfolio selection using sparse estimation of comoment tensors," LIDAM Discussion Papers LFIN 2019007, Université catholique de Louvain, Louvain Finance (LFIN).
    11. Brinkmann, Felix & Kempf, Alexander & Korn, Olaf, 2013. "Forward-looking measures of higher-order dependencies with an application to portfolio selection," CFR Working Papers 13-08, University of Cologne, Centre for Financial Research (CFR).
    12. Konermann, Patrick & Meinerding, Christoph & Sedova, Olga, 2013. "Asset allocation in markets with contagion: The interplay between volatilities, jump intensities, and correlations," Review of Financial Economics, Elsevier, vol. 22(1), pages 36-46.
    13. Díaz, Antonio & Escribano, Ana & Esparcia, Carlos, 2024. "Sustainable risk preferences on asset allocation: a higher order optimal portfolio study," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    14. Winston Buckley & Sandun Perera, 2019. "Optimal demand in a mispriced asymmetric Carr–Geman–Madan–Yor (CGMY) economy," Annals of Finance, Springer, vol. 15(3), pages 337-368, September.
    15. Massimo Guidolin & Giovanna Nicodano, 2009. "Small caps in international equity portfolios: the effects of variance risk," Annals of Finance, Springer, vol. 5(1), pages 15-48, January.
    16. Dahlquist, Magnus & Tédongap, Roméo & Farago, Adam, 2015. "Asymmetries and Portfolio Choice," CEPR Discussion Papers 10706, C.E.P.R. Discussion Papers.
    17. Aït-Sahalia, Yacine & Matthys, Felix, 2019. "Robust consumption and portfolio policies when asset prices can jump," Journal of Economic Theory, Elsevier, vol. 179(C), pages 1-56.
    18. Trung H. Le & Apostolos Kourtis & Raphael Markellos, 2023. "Modeling skewness in portfolio choice," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(6), pages 734-770, June.
    19. Platanakis, Emmanouil & Sakkas, Athanasios & Sutcliffe, Charles, 2019. "Harmful diversification: Evidence from alternative investments," The British Accounting Review, Elsevier, vol. 51(1), pages 1-23.
    20. Lassance, Nathan, 2022. "Reconciling mean-variance portfolio theory with non-Gaussian returns," European Journal of Operational Research, Elsevier, vol. 297(2), pages 729-740.

    More about this item

    Keywords

    Tail correlation; Risk aversion; Optimal portfolio; Naive diversification;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:eee:ecmode:v:122:y:2023:i:c:s0264999323000470. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    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.