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On mutual funds-of-ETFs asset allocation with rebalancing: sample covariance versus EWMA and GARCH

Author

Listed:
  • Panos Xidonas

    (ESSCA - Ecole Supérieure des Sciences Commerciales d'Angers)

  • Mike Tsionas

    (Lancaster University)

  • Constantin Zopounidis

    (Audencia Business School)

Abstract

Our purpose in this article is to investigate the benefits of introducing quantitative strategies for the estimation of portfolio variance–covariance matrices, expecting that the stylized facts of asset returns and their economic impact will be effectively captured. More specifically, we are dealing with the process of portfolio optimization with rebalancing for ETFs portfolios, in a time-varying volatility environment. The aim of the analysis is to construct optimal portfolios, based on the econometric modelling and calculation of return covariances. Also, our target is to infer critical comparative insights, as far as the application of three popular quantitative frames: (a) the sample covariance or equal weighting model, (b) the EWMA model, and (c) the GARCH (1,1) model. The validity of the attempt is verified through an illustrative empirical testing procedure on an actively traded low-volatility momentum mutual fund-of-ETFs, consisting of a well-diversified investment universe of 150 ETFs. Additionally, we co-assess a set of non-convex investment policy restrictions, such as buy-in thresholds and compliance norms, modelling the corresponding portfolio selection process as a mixed-integer optimization problem. The qualitative and technical conclusions obtained, document superior out-of-sample returns for the portfolios constructed by means of the EWMA and GARCH (1,1) models. Moreover, other findings that confirm and expand the existing underlying research, are also reported.
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Suggested Citation

  • Panos Xidonas & Mike Tsionas & Constantin Zopounidis, 2018. "On mutual funds-of-ETFs asset allocation with rebalancing: sample covariance versus EWMA and GARCH," Post-Print hal-02880066, HAL.
    • Handle: RePEc:hal:journl:hal-02880066
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    References listed on IDEAS

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    1. Adcock, C. J. & Meade, N., 1994. "A simple algorithm to incorporate transactions costs in quadratic optimisation," European Journal of Operational Research, Elsevier, vol. 79(1), pages 85-94, November.
    2. Gita Persand & Chris Brooks & Simon P. Burke, 2003. "Multivariate GARCH models: software choice and estimation issues," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 725-734.
    3. 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.
    4. Panos Xidonas & George Mavrotas, 2014. "Comparative issues between linear and non-linear risk measures for non-convex portfolio optimization: evidence from the S&P 500," Quantitative Finance, Taylor & Francis Journals, vol. 14(7), pages 1229-1242, July.
    5. Frank Fabozzi & Dashan Huang & Guofu Zhou, 2010. "Robust portfolios: contributions from operations research and finance," Annals of Operations Research, Springer, vol. 176(1), pages 191-220, April.
    6. Moallemi, Ciamac C. & Sağlam, Mehmet, 2017. "Dynamic Portfolio Choice with Linear Rebalancing Rules," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(3), pages 1247-1278, June.
    7. Panos Xidonas & George Mavrotas, 2014. "Multiobjective portfolio optimization with non-convex policy constraints: Evidence from the Eurostoxx 50," The European Journal of Finance, Taylor & Francis Journals, vol. 20(11), pages 957-977, November.
    8. Frank J. Fabozzi & Sergio Focardi & Caroline Jonas, 2007. "Trends in quantitative equity management: survey results," Quantitative Finance, Taylor & Francis Journals, vol. 7(2), pages 115-122.
    9. David B. Brown & James E. Smith, 2011. "Dynamic Portfolio Optimization with Transaction Costs: Heuristics and Dual Bounds," Management Science, INFORMS, vol. 57(10), pages 1752-1770, October.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
    12. 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.
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    Cited by:

    1. Maciej Wysocki & Paweł Sakowski, 2022. "Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models," Working Papers 2022-12, Faculty of Economic Sciences, University of Warsaw.
    2. Xidonas, Panos & Doukas, Haris & Hassapis, Christis, 2021. "Grouped data, investment committees & multicriteria portfolio selection," Journal of Business Research, Elsevier, vol. 129(C), pages 205-222.

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