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Non-linear predictability in stock and bond returns: when and where is it exploitable?

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  • Massimo Guidolin
  • Stuart Hyde
  • David McMillan
  • Sadayuki Ono

Abstract

We systematically examine the comparative predictive performance of a number of alternative linear and non-linear models for stock and bond returns in the G7 countries. Besides Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STAR) regime switching (predictive) regression models, we also estimate univariate models in which conditional heteroskedasticity is captured through GARCH, TARCH and EGARCH models and ARCH-in mean effects appear in the conditional mean. Although we fail to find a consistent winner/out-performer across all countries and asset markets, it turns out that capturing non-linear effects is of extreme importance to improve forecasting performance. U.S. and U.K. asset return data are “special” in the sense that good predictive performance seems to loudly ask for models that capture non linear dynamics, especially of the Markov switching type. Although occasionally also stock and bond return forecasts for other G7 countries appear to benefit from non-linear modeling (especially of TAR and STAR type), data from France, Germany, and Italy express interesting predictive results on the basis of simpler benchmarks. U.S. and U.K. data are also the only two data sets in which we find statistically significant differences between forecasting models. Results appear to be remarkably stable over time, and robust to the specification of the loss function used in statistical evaluations as well as to the methodology employed to perform pairwise comparisons.

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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2008-010.

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Date of creation: 2009
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Handle: RePEc:fip:fedlwp:2008-010

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Keywords: Group of Seven countries ; Financial markets;

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Cited by:
  1. Jan G. De Gooijer & Cees G. H. Diks & Łukasz T. Gątarek, 2012. "Information Flows Around the Globe: Predicting Opening Gaps from Overnight Foreign Stock Price Patterns," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 4(1), pages 23-44, March.
  2. George Kapetanios & James Mitchell & Yongcheol Shin, 2010. "A Nonlinear Panel Model of Cross-sectional Dependence," Working Papers 673, Queen Mary, University of London, School of Economics and Finance.
  3. Massacci, Daniele, 2013. "A switching model with flexible threshold variable: With an application to nonlinear dynamics in stock returns," Economics Letters, Elsevier, vol. 119(2), pages 199-203.
  4. Rangan Gupta & Shawkat Hammoudeh & Beatrice D. Simo-Kengne & Soodabeh Sarafrazi, 2013. "Can the Sharia-Based Islamic Stock Market Returns be Forecasted Using Large Number of Predictors and Models?," Working Papers 201381, University of Pretoria, Department of Economics.
  5. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  6. Michael Scholz & Stefan Sperlich & Jens Perch Nielsen, 2012. "Nonparametric prediction of stock returns with generated bond yields," Graz Economics Papers 2012-10, University of Graz, Department of Economics.
  7. repec:dgr:uvatin:2009107 is not listed on IDEAS
  8. Massimo Guidolin & Stuart Hyde, 2011. "Can VAR Models Capture Regime Shifts in Asset Returns? A Long-Horizon Strategic Asset Allocation Perspective," Working Papers 414, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  9. James Mitchell & George Kapetanios & Yongcheol Shin, 2012. "A Nonlinear Panel Data Model of Cross-Sectional Dependence," Discussion Papers in Economics 12/01, Department of Economics, University of Leicester.
  10. Thomas Q. Pedersen, 2010. "Predictable return distributions," CREATES Research Papers 2010-38, School of Economics and Management, University of Aarhus.
  11. David G McMillan, 2012. "Long-run stock price-house price relation: evidence from an ESTR model," Economics Bulletin, AccessEcon, vol. 32(2), pages 1737-1746.
  12. Guidolin, Massimo & Hyde, Stuart, 2012. "Simple VARs cannot approximate Markov switching asset allocation decisions: An out-of-sample assessment," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3546-3566.
  13. Marcos Álvarez-Díaz & Shawkat Hammoudeh & Rangan Gupta, 2013. "Detecting Predictable Non-linear Dynamics in Dow Jones Industrial Average and Dow Jones Islamic Market Indices using Nonparametric Regressions," Working Papers 201385, University of Pretoria, Department of Economics.

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