IDEAS home Printed from
MyIDEAS: Login to save this article or follow this journal

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

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

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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

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

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

in new window

Handle: RePEc:eee:intfor:v:25:y:2009:i:2:p:373-399
Contact details of provider: Web page:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. 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.
  2. David G. McMillan, 2003. "Non-linear Predictability of UK Stock Market Returns," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(5), pages 557-573, December.
  3. Angelos Kanas, 2003. "Non-linear forecasts of stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 299-315.
  4. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
  5. Lekkos, Ilias & Milas, Costas, 2004. "Time-varying excess returns on UK government bonds: A non-linear approach," Journal of Banking & Finance, Elsevier, vol. 28(1), pages 45-62, January.
  6. Campbell, John, 1987. "Stock Returns and the Term Structure," Scholarly Articles 3207699, Harvard University Department of Economics.
  7. Bradley, Michael D. & Jansen, Dennis W., 2004. "Forecasting with a nonlinear dynamic model of stock returns and industrial production," International Journal of Forecasting, Elsevier, vol. 20(2), pages 321-342.
  8. Sarantis, Nicholas, 2001. "Nonlinearities, cyclical behaviour and predictability in stock markets: international evidence," International Journal of Forecasting, Elsevier, vol. 17(3), pages 459-482.
  9. Don Bredin & Stuart Hyde, 2008. "Regime Change and the Role of International Markets on the Stock Returns of Small Open Economies," European Financial Management, European Financial Management Association, vol. 14(2), pages 315-346.
  10. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA.
  11. Martens, M. & Kofman, P. & Vorst, T.C.F., 1995. "A Threshold Error Correction Model for Intraday Futures and Index Returns," Monash Econometrics and Business Statistics Working Papers 14/95, Monash University, Department of Econometrics and Business Statistics.
  12. Taylor, James W., 2004. "Volatility forecasting with smooth transition exponential smoothing," International Journal of Forecasting, Elsevier, vol. 20(2), pages 273-286.
  13. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
  14. Massimo Guidolin & Allan Timmerman, 2006. "International asset allocation under regime switching, skew and kurtosis preferences," Working Papers 2005-034, Federal Reserve Bank of St. Louis.
  15. Hodrick, Robert J, 1992. "Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement," Review of Financial Studies, Society for Financial Studies, vol. 5(3), pages 357-86.
  16. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423.
  17. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, vol. 28(3-4), pages 315-332, May.
  18. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
  19. Schwert, G William, 1990. " Stock Returns and Real Activity: A Century of Evidence," Journal of Finance, American Finance Association, vol. 45(4), pages 1237-57, September.
  20. David McMillan, 2001. "Non-Linear Predictability of Stock Market Returns: Evidence from Non-Parametric and Threshold Models," Discussion Paper Series, Department of Economics 200102, Department of Economics, University of St. Andrews.
  21. Simon van Norden & Huntley Schaller & ), 1995. "Regime Switching in Stock Market Returns," Econometrics 9502002, EconWPA.
  22. Asprem, Mads, 1989. "Stock prices, asset portfolios and macroeconomic variables in ten European countries," Journal of Banking & Finance, Elsevier, vol. 13(4-5), pages 589-612, September.
  23. John H. Boyd & Ravi Jagannathan & Jian Hu, 2001. "The Stock Market's Reaction to Unemployment News: Why Bad News is Usually Good for Stocks," NBER Working Papers 8092, National Bureau of Economic Research, Inc.
  24. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
  25. Allan Timmermann & Gabriel Perez-Quiros, 1999. "Firm Size and Cyclical Variations in Stock Returns," FMG Discussion Papers dp335, Financial Markets Group.
  26. Massimo Guidolin & Allan Timmermann, 2003. "Recursive Modeling of Nonlinear Dynamics in UK Stock Returns," Manchester School, University of Manchester, vol. 71(4), pages 381-395, 07.
  27. Enders, Walter & Siklos, Pierre L, 2001. "Cointegration and Threshold Adjustment," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 166-76, April.
  28. van Dijk, D.J.C. & Franses, Ph.H.B.F., 2003. "Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy," Econometric Institute Research Papers EI 2003-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  29. James H. Stock & Mark W. Watson, 2001. "Forecasting output and inflation: the role of asset prices," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  30. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.
  31. Norman Swanson & Valentina Corradi, 2006. "Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes," Departmental Working Papers 200618, Rutgers University, Department of Economics.
  32. David H. Cutler & James M. Poterba & Lawrence H. Summers, 1988. "What Moves Stock Prices?," Working papers 487, Massachusetts Institute of Technology (MIT), Department of Economics.
  33. Allan Timmermann & Andrew Patton, 2004. "Properties of Optimal Forecasts under Asymmetric Loss and Nonlinearity," Working Papers wp04-05, Warwick Business School, Finance Group.
  34. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46 National Bureau of Economic Research, Inc.
  35. Pesaran, M.H. & Timmermann, A., 1990. "A Simple, Non-Parametric Test Of Predictive Performance," Cambridge Working Papers in Economics 9021, Faculty of Economics, University of Cambridge.
  36. Pesaran, M Hashem & Timmermann, Allan, 1995. " Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-28, September.
  37. 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 357-84, March.
  38. Mark J. Flannery & Aris A. Protopapadakis, 2002. "Macroeconomic Factors Do Influence Aggregate Stock Returns," Review of Financial Studies, Society for Financial Studies, vol. 15(3), pages 751-782.
  39. Enders, Walter & Granger, C. W. J., 1998. "Unit Root Tests and Asymmetric Adjustment with an Example Using the Term Structure of Interest Rates," Staff General Research Papers 1388, Iowa State University, Department of Economics.
  40. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
  41. Awartani, Basel M.A. & Corradi, Valentina, 2005. "Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries," International Journal of Forecasting, Elsevier, vol. 21(1), pages 167-183.
  42. Balvers, Ronald J & Cosimano, Thomas F & McDonald, Bill, 1990. " Predicting Stock Returns in an Efficient Market," Journal of Finance, American Finance Association, vol. 45(4), pages 1109-28, September.
  43. Massimo Guidolin & Allan Timmermann, 2005. "Economic Implications of Bull and Bear Regimes in UK Stock and Bond Returns," Economic Journal, Royal Economic Society, vol. 115(500), pages 111-143, 01.
  44. 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.
  45. Shiller, Robert J. & Beltratti, Andrea E., 1992. "Stock prices and bond yields : Can their comovements be explained in terms of present value models?," Journal of Monetary Economics, Elsevier, vol. 30(1), pages 25-46, October.
  46. Fama, Eugene F, 1990. " Stock Returns, Expected Returns, and Real Activity," Journal of Finance, American Finance Association, vol. 45(4), pages 1089-1108, September.
  47. Clements, Michael P & Smith, Jeremy, 1996. "A Monte Carlo Study of the Forecasting Performance of Empirical Setar Models," The Warwick Economics Research Paper Series (TWERPS) 464, University of Warwick, Department of Economics.
  48. Philip Shively, 2003. "International evidence of temporary and permanent stock-price innovations: a multivariate approach," Applied Economics Letters, Taylor & Francis Journals, vol. 10(8), pages 499-503.
  49. McQueen, Grant & Roley, V Vance, 1993. "Stock Prices, News, and Business Conditions," Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 683-707.
  50. Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183.
  51. Cheung, Yin-Wong & Ng, Lilian K., 1998. "International evidence on the stock market and aggregate economic activity," Journal of Empirical Finance, Elsevier, vol. 5(3), pages 281-296, September.
  52. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De.
  53. McMillan, David G., 2005. "Non-linear dynamics in international stock market returns," Review of Financial Economics, Elsevier, vol. 14(1), pages 81-91.
  54. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  55. De Gooijer, Jan G. & Kumar, Kuldeep, 1992. "Some recent developments in non-linear time series modelling, testing, and forecasting," International Journal of Forecasting, Elsevier, vol. 8(2), pages 135-156, October.
  56. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
  57. Golinelli, Roberto & Parigi, Giuseppe, 2008. "Real-time squared: A real-time data set for real-time GDP forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 368-385.
  58. Allan Timmermann & Massimo Guidolin, 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.
  59. Balke, Nathan S & Fomby, Thomas B, 1997. "Threshold Cointegration," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 38(3), pages 627-45, August.
  60. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
  61. Rapach, David E. & Wohar, Mark E. & Rangvid, Jesper, 2005. "Macro variables and international stock return predictability," International Journal of Forecasting, Elsevier, vol. 21(1), pages 137-166.
  62. Nicholas Barberis, 2000. "Investing for the Long Run when Returns Are Predictable," Journal of Finance, American Finance Association, vol. 55(1), pages 225-264, 02.
  63. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
  64. Andrew Ang & Geert Bekaert, 2001. "Stock Return Predictability: Is it There?," NBER Working Papers 8207, National Bureau of Economic Research, Inc.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:25:y:2009:i:2:p:373-399. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.