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Regime shifts in mean-variance efficient frontiers: some international evidence

Author

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  • Massimo Guidolin
  • Federica Ria
Abstract
Regime switching models have been assuming an increasingly central role in financial applications because of their well-known ability to capture the presence of rich non-linear patterns in the joint distribution of asset returns. After reviewing key concepts and technical issues related to specifying, estimating, and using multivariate Markov switching models in financial applications, in this paper we examine how the presence of regimes in means, variances, and covariances of asset returns translates into explicit dynamics of the Markowitz mean-variance frontier. In particular, we show both theoretically and through an application to international equity portfolio diversification that substantial differences exist between bull and bear regime-specific frontiers, both in statistical and in economic terms. Using Morgan Stanley Capital International (MSCI) investable indices for five countries/macro-regions, we characterize mean-variance frontiers and optimal portfolio strategies in bull periods, in bear periods, and in periods where high uncertainty exists on the nature of the current regime.

Suggested Citation

  • Massimo Guidolin & Federica Ria, 2010. "Regime shifts in mean-variance efficient frontiers: some international evidence," Working Papers 2010-040, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:2010-040
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    Cited by:

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    2. Angelidis, Timotheos & Tessaromatis, Nikolaos, 2014. "Global Style Portfolios Based on Country Indices," MPRA Paper 53094, University Library of Munich, Germany.
    3. Apostolos Thomadakis, 2012. "Contagion or Flight-to-Quality Phenomena in Stock and Bond Returns," School of Economics Discussion Papers 0612, School of Economics, University of Surrey.
    4. Balcılar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat, 2015. "Regional and global spillovers and diversification opportunities in the GCC equity sectors," Emerging Markets Review, Elsevier, vol. 24(C), pages 160-187.
    5. Kamesh Korangi & Christophe Mues & Cristi'an Bravo, 2024. "Large-scale Time-Varying Portfolio Optimisation using Graph Attention Networks," Papers 2407.15532, arXiv.org.
    6. Platanakis, Emmanouil & Sakkas, Athanasios & Sutcliffe, Charles, 2019. "Harmful diversification: Evidence from alternative investments," The British Accounting Review, Elsevier, vol. 51(1), pages 1-23.
    7. Emmanouil Platanakis & Athanasios Sakkas & Charles Sutcliffe, 2017. "Should Portfolio Model Inputs Be Estimated Using One or Two Economic Regimes?," ICMA Centre Discussion Papers in Finance icma-dp2017-07, Henley Business School, University of Reading.
    8. Giulia Dal Pra & Massimo Guidolin & Manuela Pedio & Fabiola Vasile, 2016. "Do Regimes in Excess Stock Return Predictability Create Economic Value? An Out-of-Sample Portfolio Analysis," BAFFI CAREFIN Working Papers 1637, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    9. Wasim Ahmad & N. Bhanumurthy & Sanjay Sehgal, 2015. "Regime dependent dynamics and European stock markets: Is asset allocation really possible?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(1), pages 77-107, February.
    10. Alexander Berglund & Massimo Guidolin & Manuela Pedio, 2020. "Monetary policy after the crisis: A threat to hedge funds' alphas?," Journal of Asset Management, Palgrave Macmillan, vol. 21(3), pages 219-238, May.

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