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

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

Abstract

We systematically examine the comparative predictive performance of a number of 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 models, we also estimate univariate models in which conditional heteroskedasticity is captured by GARCH and in which predicted volatilities appear in the conditional mean function. We find that capturing non-linear effects may be key to improving forecasting. In contrast to other G7 countries, US and UK asset return data are "special," requiring that non-linear dynamics be modeled, especially when using a Markov switching framework. The results appear to be remarkably stable over time, robust to changes in the loss function used in statistical evaluations as well as to the methodology employed to perform pair-wise comparisons.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 25 (2009)
Issue (Month): 2 ()
Pages: 373-399

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Handle: RePEc:eee:intfor:v:25:y:2009:i:2:p:373-399

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Non-linearities Regime switching Threshold predictive regressions Forecasting;

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References

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Citations

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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. De Gooijer, J. & Diks, C.G.H. & Gatarek, L., 2009. "Information Flows Around the Globe: Predicting Opening Gaps from Overnight Foreign Stock Price Patterns," CeNDEF Working Papers 09-13, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  7. 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.
  8. Thomas Q. Pedersen, 2010. "Predictable return distributions," CREATES Research Papers 2010-38, School of Economics and Management, University of Aarhus.
  9. 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.
  10. Jan G. de Gooijer & Cees G.H. Diks & Lukasz T. Gatarek, 2009. "Information Flows around the Globe: Predicting Opening Gaps from Overnight Foreign Stock Price Patterns," Tinbergen Institute Discussion Papers 09-107/4, Tinbergen Institute.
  11. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  12. 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.
  13. 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.

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