IDEAS home Printed from https://ideas.repec.org/a/taf/ufajxx/v60y2004i2p86-99.html
   My bibliography  Save this article

How Regimes Affect Asset Allocation

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.2469/faj.v60.n2.2612
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.2469/faj.v60.n2.2612?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    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. "Short rate nonlinearities and regime switches," Journal of Economic Dynamics and Control, Elsevier, vol. 26(7-8), pages 1243-1274, July.
    3. Gray, Stephen F., 1996. "Modeling the conditional distribution of interest rates as a regime-switching process," Journal of Financial Economics, Elsevier, vol. 42(1), pages 27-62, September.
    4. Fama, Eugene F. & Schwert, G. William, 1977. "Asset returns and inflation," Journal of Financial Economics, Elsevier, vol. 5(2), pages 115-146, November.
    5. 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.
    6. Bekaert, Geert & Hodrick, Robert J. & Marshall, David A., 2001. "Peso problem explanations for term structure anomalies," Journal of Monetary Economics, Elsevier, vol. 48(2), pages 241-270, October.
    7. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    8. Green, Richard C & Hollifield, Burton, 1992. "When Will Mean-Variance Efficient Portfolios Be Well Diversified?," Journal of Finance, American Finance Association, vol. 47(5), pages 1785-1809, December.
    9. Massimo Guidolin & Allan Timmerman, 2005. "Optimal portfolio choice under regime switching, skew and kurtosis preferences," Working Papers 2005-006, Federal Reserve Bank of St. Louis.
    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. 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.
    2. 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.
    3. Andrew Ang & Allan Timmermann, 2012. "Regime Changes and Financial Markets," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 313-337, October.
    4. 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.
    5. Jonathan Tuck & Shane Barratt & Stephen Boyd, 2021. "Portfolio Construction Using Stratified Models," Papers 2101.04113, arXiv.org, revised Feb 2021.
    6. 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".
    7. 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.
    8. Massimo Guidolin & Giovanna Nicodano, 2010. "Ex Post Portfolio Performance with Predictable Skewness and Kurtosis," Carlo Alberto Notebooks 191, Collegio Carlo Alberto.
    9. 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.
    10. Yuanrong Wang & Tomaso Aste, 2021. "Dynamic Portfolio Optimization with Inverse Covariance Clustering," Papers 2112.15499, arXiv.org, revised Jan 2022.
    11. 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.
    12. 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).
    13. Chollete, Loran & Jaffee, Dwight, 2009. "Economic Implications of Extreme and Rare Events," UiS Working Papers in Economics and Finance 2009/32, University of Stavanger.
    14. 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.).
    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. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.

    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. 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. He, Hui & Yang, Jiawen, 2011. "Regime-switching analysis of ADR home market pass-through," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 204-214, January.
    3. Guidolin, Massimo & Ono, Sadayuki, 2006. "Are the dynamic linkages between the macroeconomy and asset prices time-varying?," Journal of Economics and Business, Elsevier, vol. 58(5-6), pages 480-518.
    4. Andrew Ang & Geert Bekaert & Min Wei, 2008. "The Term Structure of Real Rates and Expected Inflation," Journal of Finance, American Finance Association, vol. 63(2), pages 797-849, April.
    5. Massimo Guidolin & Carrie Fangzhou Na, 2007. "The economic and statistical value of forecast combinations under regime switching: an application to predictable U.S. returns," Working Papers 2006-059, Federal Reserve Bank of St. Louis.
    6. Henkel, Sam James & Martin, J. Spencer & Nardari, Federico, 2011. "Time-varying short-horizon predictability," Journal of Financial Economics, Elsevier, vol. 99(3), pages 560-580, March.
    7. Levy, Moshe & Kaplanski, Guy, 2015. "Portfolio selection in a two-regime world," European Journal of Operational Research, Elsevier, vol. 242(2), pages 514-524.
    8. Huseyin Gulen & Yuhang Xing & Lu Zhang, 2011. "Value versus Growth: Time‐Varying Expected Stock Returns," Financial Management, Financial Management Association International, vol. 40(2), pages 381-407, June.
    9. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    10. Guidolin, Massimo & Timmermann, Allan, 2007. "Asset allocation under multivariate regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 31(11), pages 3503-3544, November.
    11. Guidolin, Massimo & Timmermann, Allan, 2009. "Forecasts of US short-term interest rates: A flexible forecast combination approach," Journal of Econometrics, Elsevier, vol. 150(2), pages 297-311, June.
    12. Oscar V. De la Torre-Torres & Evaristo Galeana-Figueroa & José Álvarez-García, 2020. "Markov-Switching Stochastic Processes in an Active Trading Algorithm in the Main Latin-American Stock Markets," Mathematics, MDPI, vol. 8(6), pages 1-22, June.
    13. Pesaran, M. Hashem & Timmermann, Allan, 2004. "How costly is it to ignore breaks when forecasting the direction of a time series?," International Journal of Forecasting, Elsevier, vol. 20(3), pages 411-425.
    14. Massimo Guidolin & Allan Timmermann, 2006. "An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 1-22, January.
    15. José Luis Fernández Serrano & Mª Dolores Robles Fernández, 2002. "Política Monetaria y Cambios de Régimen en los tipos de Interés del Mercado Interbancario," Documentos de Trabajo del ICAE 0209, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    16. Lange, Ronald H., 2017. "The expected real yield and inflation components of the nominal yield curve," The North American Journal of Economics and Finance, Elsevier, vol. 39(C), pages 1-18.
    17. Ilias Lekkos & Costas Milas & Theodore Panagiotidis, 2005. "On the predictability of common risk factors in the US and UK interest rate swap markets: Evidence from non-linear and linear models," Discussion Paper Series 2005_9, Department of Economics, Loughborough University, revised Sep 2005.
    18. Morana, Claudio & Beltratti, Andrea, 2002. "The effects of the introduction of the euro on the volatility of European stock markets," Journal of Banking & Finance, Elsevier, vol. 26(10), pages 2047-2064, October.
    19. Ono, Sadayuki, 2019. "Term structure dynamics in a monetary economy with learning," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 730-745.
    20. 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.

    More about this item

    JEL classification:

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

    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:taf:ufajxx:v:60:y:2004:i:2:p:86-99. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/ufaj20 .

    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.