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Can VAR models capture regime shifts in asset returns? A long-horizon strategic asset allocation perspective

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  • Guidolin, Massimo
  • Hyde, Stuart

Abstract

It is often suggested that through a judicious choice of predictors that track business cycles and market sentiment, simple vector autoregressive (VAR) models could produce optimal strategic portfolio allocations that hedge against the bull and bear dynamics typical of financial markets. However, a distinct literature exists that shows that nonlinear econometric frameworks, such as Markov switching (MS), are also natural tools to compute optimal portfolios in the presence of stochastic good and bad market states. In this paper we examine whether simple VARs can produce portfolio rules similar to those obtained under MS, by studying the effects of expanding both the order of the VAR and the number/selection of predictor variables included. In a typical stock–bond strategic asset allocation problem, we compute the out-of-sample certainty equivalent returns for a wide range of VARs and compare these measures of performance with those typical of nonlinear models for a long-horizon investor with constant relative risk aversion. We conclude that most VARs cannot produce portfolio rules, hedging demands, or (net of transaction costs) out-of-sample performances that approximate those obtained from equally simple nonlinear frameworks. We also compute the improvement in realized performance that may be achieved adopting more complex MS models and report this may be substantial in the case of regime switching ARCH.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Banking & Finance.

Volume (Year): 36 (2012)
Issue (Month): 3 ()
Pages: 695-716

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Handle: RePEc:eee:jbfina:v:36:y:2012:i:3:p:695-716

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Keywords: Predictability; Strategic asset allocation; Markov switching; Vector autoregressive models; Out-of-sample performance;

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Cited by:
  1. Massimo Guidolin & Stuart Hyde, 2012. "Optimal Portfolios for Occupational Funds under Time-Varying Correlations in Bull and Bear Markets? Assessing the Ex-Post Economic Value," Working Papers 455, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  2. Carolina Fugazza & Massimo Guidolin & Giovanna Nicodano, 2010. "1/N and Long Run Optimal Portfolios: Results for Mixed Asset Menus," Carlo Alberto Notebooks 190, Collegio Carlo Alberto.
  3. Dewandaru, Ginanjar & Masih, Rumi & Bacha, Obiyathulla & Masih, A. Mansur M., 2014. "Combining Momentum, Value, and Quality for the Islamic Equity Portfolio: Multi-style Rotation Strategies using Augmented Black Litterman Factor Model," MPRA Paper 56965, University Library of Munich, Germany.
  4. Nyberg, Henri, 2013. "Predicting bear and bull stock markets with dynamic binary time series models," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3351-3363.
  5. 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.

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