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Asset Allocation Using Flexible Dynamic Correlation Models with Regime Switching

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  • E. Otranto
Abstract
The asset allocation decision is often considered as a trade-off between maximizing the expected return of a portfolio and minimizing the portfolio risk. The riskiness is evaluated in terms of variance of the portfolio return, so that it is fundamental to consider correctly the variance of its components and their correlations. The evidence of the heteroskedastic behavior of the returns and the time-varying relationships among the portfolio components have recently shifted attention to the multivariate GARCH models with time varying correlation. In this work we insert a particular Markov Switching dynamics in some Dynamic Correlation models to consider the abrupt changes in correlations affecting the assets in different ways. This class of models is very general and provides several specifications, constraining some coefficients. The models are applied to solve a sectorial asset allocation problem and are compared with alternative models.

Suggested Citation

  • E. Otranto, 2008. "Asset Allocation Using Flexible Dynamic Correlation Models with Regime Switching," Working Paper CRENoS 200810, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    • Handle: RePEc:cns:cnscwp:200810
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    Cited by:

    1. Haas Markus & Liu Ji-Chun, 2018. "A multivariate regime-switching GARCH model with an application to global stock market and real estate equity returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(3), pages 1-27, June.
    2. Guobin Fan & Yong Zeng, 2012. "The Timing Of Portfolio Adjustments: A Regime-Switching Approach," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(05), pages 909-933.
    3. Cipollini, Fabrizio & Gallo, Giampiero M. & Otranto, Edoardo, 2021. "Realized volatility forecasting: Robustness to measurement errors," International Journal of Forecasting, Elsevier, vol. 37(1), pages 44-57.
    4. Fereydooni, Ali & Barak, Sasan & Asaad Sajadi, Seyed Mehrzad, 2024. "A novel online portfolio selection approach based on pattern matching and ESG factors," Omega, Elsevier, vol. 123(C).
    5. E. Otranto, 2015. "Adding Flexibility to Markov Switching Models," Working Paper CRENoS 201509, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    6. Abdul Aziz, Nor Syahilla & Vrontos, Spyridon & M. Hasim, Haslifah, 2019. "Evaluation of multivariate GARCH models in an optimal asset allocation framework," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 568-596.
    7. Gad, Samar & Andrikopoulos, Panagiotis, 2019. "Diversification benefits of Shari'ah compliant equity ETFs in emerging markets," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 133-144.
    8. Ha, Youngmin & Zhang, Hai, 2020. "Algorithmic trading for online portfolio selection under limited market liquidity," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1033-1051.
    9. Haas, Markus & Liu, Ji-Chun, 2015. "Theory for a Multivariate Markov--switching GARCH Model with an Application to Stock Markets," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112855, Verein für Socialpolitik / German Economic Association.

    More about this item

    Keywords

    markov chain; multivariate garch; portfolio performance; switching parameters;
    All these keywords.

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