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How Regimes Affect Asset Allocation

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  • Andrew Ang
  • Geert Bekaert

Abstract

International equity returns are characterized by episodes of high volatility and unusually high correlations coinciding with bear markets. This article provides models of asset returns that match these patterns and illustrates their use in asset allocation. The presence of regimes with different correlations and expected returns is difficult to exploit within a framework focused on global equities. Nevertheless, for global all-equity portfolios, the regime-switching strategy dominated static strategies in an out-of-sample test. In addition, substantial value was added when an investor switched between domestic cash, bonds, and equity investments. In a persistent high-volatility market, the model told the investor to switch primarily to cash. Large market-timing benefits are possible because high-volatility regimes tend to coincide with periods of relatively high interest rates. International equity returns are more highly correlated with each other in high-volatility bear markets than in normal times. Regime-switching (RS) models perform well at replicating the degree of asymmetric correlations observed in the data because they draw data from a normal regime most of the time but transition to a bear market regime when asset returns are, on average, lower and much more volatile than in normal times. The regimes are persistent, and in the bear markets, asset correlations are higher than in the normal regime.We show that the presence of regimes in international returns is exploitable in active asset allocation programs. We illustrate how the presence of regimes can be incorporated into two asset allocation programs—a global equity allocation setting (with six equity markets) and a market-timing setting for U.S. cash, bonds, and equity. For global portfolios, the optimal equity portfolio in the high-volatility bear market is very different from the optimal portfolio in the normal regime; for example, it is more home biased in bear markets. For a domestic U.S. portfolio, optimally exploiting regime switches implies portfolio shifts into bonds or cash when a high-volatility bear market regime is expected.To build a quantitative model for the international asset classes, we incorporated two regimes in the basic capital asset pricing model. Conditional means, volatilities, and correlations in this model depend on which regime prevails at each time. The RS model can produce rich patterns of stochastic volatility and time-varying correlations. The regimes are identified endogenously through the estimation procedure, which provides an easy way for an investor to determine which regime is prevailing at a given time.The regime-dependent strategies have the potential to outperform static investment strategies because they set up a defensive portfolio in the bear market regime that hedges against high correlations and low returns. Theoretically, the presence of two regimes implies two mean–variance frontiers, one for each regime. The presence of two regimes and two frontiers means that the regime-switching investment opportunity set dominates the investment opportunity set offered by one unconditional frontier. For example, in the global asset allocation setting in the normal regime, the unconditional tangency portfolio yielded a Sharpe ratio of 0.619. The investor could improve this trade-off to 0.871 by holding the risk-free asset and the optimal tangency portfolio. Similarly, in the bear market regime, the unconditional tangency portfolio had a Sharpe ratio of only 0.129, but it could be improved to 0.268 by holding the optimal regime-dependent tangency portfolio.To illustrate the practical implementation of the regime-dependent strategies, we used an out-of-sample analysis starting in 1985 and ex post Sharpe ratios as a performance criterion. For the global asset allocation example, the regime-switching strategy's Sharpe ratio was more than double the world market portfolio's Sharpe ratio.In an out-of-sample test of the market-timing model for U.S. equities, bonds, and cash, we found that substantial value could be added when an investor moved assets among cash, bonds, and equity investments. When a persistent high-volatility market hit, the investor switched primarily to cash. Market-timing benefits were large because high-volatility markets tend to coincide with periods of relatively high interest rates.The results reported here provide a clear demonstration of how active managers can incorporate regime-switching strategies to enhance returns in market-timing models. Our results lead to two robust conclusions. First, one can add value by considering regime switches in global all-equity portfolios; the presence of a bear market regime does not negate the benefits of international diversification. Although portfolios in the high-correlation regime are more home biased, they still involve significant international exposure. Second, an even more valuable situation in which to consider regime-switching models is tactical asset allocation programs that allow switching to a risk-free asset.

Suggested Citation

  • Andrew Ang & Geert Bekaert, 2004. "How Regimes Affect Asset Allocation," Financial Analysts Journal, Taylor & Francis Journals, vol. 60(2), pages 86-99, March.
  • Handle: RePEc:taf:ufajxx:v:60:y:2004:i:2:p:86-99
    DOI: 10.2469/faj.v60.n2.2612
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    1. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," The Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    2. Ang, Andrew & Bekaert, Geert, 2002. "Regime Switches in Interest Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 163-182, April.
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    7. Ang, Andrew & Bekaert, Geert, 2002. "Short rate nonlinearities and regime switches," Journal of Economic Dynamics and Control, Elsevier, vol. 26(7-8), pages 1243-1274, July.
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    1. Andrew Ang & Allan Timmermann, 2012. "Regime Changes and Financial Markets," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 313-337, October.
    2. Nicholas Chan & Mila Getmansky & Shane M. Haas & Andrew W. Lo, 2007. "Systemic Risk and Hedge Funds," NBER Chapters, in: The Risks of Financial Institutions, pages 235-330, National Bureau of Economic Research, Inc.
    3. Garcia, René & Tsafack, Georges, 2011. "Dependence structure and extreme comovements in international equity and bond markets," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 1954-1970, August.
    4. Chae, Joon & Lee, Eun Jung, 2018. "Distribution uncertainty and expected stock returns," Finance Research Letters, Elsevier, vol. 25(C), pages 55-61.
    5. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.
    6. Patrick Roger & Marie-Hélène Broihanne & Maxime Merli, 2012. "In search of positive skewness: the case of individual investors," Working Papers of LaRGE Research Center 2012-04, Laboratoire de Recherche en Gestion et Economie (LaRGE), Université de Strasbourg.
    7. Massimo Guidolin & Giovanna Nicodano, 2010. "Ex Post Portfolio Performance with Predictable Skewness and Kurtosis," Carlo Alberto Notebooks 191, Collegio Carlo Alberto.
    8. Yuanrong Wang & Tomaso Aste, 2021. "Dynamic Portfolio Optimization with Inverse Covariance Clustering," Papers 2112.15499, arXiv.org, revised Jan 2022.
    9. Aymen Ben Rejeb & Adel Boughrara, 2014. "The relationship between financial liberalization and stock market volatility: the mediating role of financial crises," Journal of Economic Policy Reform, Taylor & Francis Journals, vol. 17(1), pages 46-70, March.
    10. Peter Nystrup & Henrik Madsen & Erik Lindström, 2018. "Dynamic portfolio optimization across hidden market regimes," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 83-95, January.
    11. Wang, Yuanrong & Aste, Tomaso, 2023. "Dynamic portfolio optimization with inverse covariance clustering," LSE Research Online Documents on Economics 117701, London School of Economics and Political Science, LSE Library.
    12. Guidolin, Massimo & Hyde, Stuart, 2012. "Can VAR models capture regime shifts in asset returns? A long-horizon strategic asset allocation perspective," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 695-716.
    13. Mili, Mehdi, 2012. "Fixed-income portfolio management in crisis period: Expected tail loss (ETL) approach," Economics Discussion Papers 2012-33, Kiel Institute for the World Economy (IfW Kiel).
    14. Chollete, Loran & Jaffee, Dwight, 2009. "Economic Implications of Extreme and Rare Events," UiS Working Papers in Economics and Finance 2009/32, University of Stavanger.
    15. Manuel Ammann & Michael Verhofen, 2006. "The Effect of Market Regimes on Style Allocation," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(3), pages 309-337, September.
    16. Ravi Kashyap, 2024. "The Concentration Risk Indicator: Raising the Bar for Financial Stability and Portfolio Performance Measurement," Papers 2408.07271, arXiv.org.
    17. Elizabeth Fons & Paula Dawson & Jeffrey Yau & Xiao-jun Zeng & John Keane, 2019. "A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing," Papers 1902.10849, arXiv.org.
    18. Deborah Miori & Mihai Cucuringu, 2022. "Returns-Driven Macro Regimes and Characteristic Lead-Lag Behaviour between Asset Classes," Papers 2209.00268, arXiv.org, revised Sep 2022.
    19. Jonathan Tuck & Shane Barratt & Stephen Boyd, 2021. "Portfolio Construction Using Stratified Models," Papers 2101.04113, arXiv.org, revised Feb 2021.
    20. Monica Billio & Mila Getmansky & Loriana Pelizzon, 2006. "Phase-Locking and Switching Volatility in Hedge Funds," Working Papers 2006_54, Department of Economics, University of Venice "Ca' Foscari".
    21. Stefania D'Amico, 2005. "Density selection and combination under model ambiguity: an application to stock returns," Finance and Economics Discussion Series 2005-09, Board of Governors of the Federal Reserve System (U.S.).

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    JEL classification:

    • F30 - International Economics - - International Finance - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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