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Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility

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Author Info

  • Cem Cakmakli

    (Erasmus University Rotterdam)

  • Dick van Dijk

    (Erasmus Universiteit Rotterdam)

Abstract

This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only include valuation ratios and interest rate related variables, and possibly individual macro variables, as well as the historical average excess return. The improvements in out-of-sample forecast accuracy are both statistically and economically significant. The factor-augmented predictive regressions have superior market timing ability and volatility timing ability, while a mean-variance investor would be willing to pay an annual performance fee of several hundreds of basis points to switch from the predictions offered by the benchmark models to those of the factor-augmented models. An important reason for the superior performance of the factor-augmented predictive regressions is the stability of their forecast accuracy, whereas the benchmark models suffer from a forecast breakdown during the 1990s.

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File URL: http://papers.tinbergen.nl/10115.pdf
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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 10-115/4.

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Date of creation: 22 Nov 2010
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Handle: RePEc:dgr:uvatin:20100115

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Web page: http://www.tinbergen.nl

Related research

Keywords: return predictability; model uncertainty; dynamic factor models; variable selection;

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References

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  1. Della Corte, Pasquale & Sarno, Lucio & Tsiakas, Ilias, 2007. "An Economic Evaluation of Empirical Exchange Rate Models," CEPR Discussion Papers 6598, C.E.P.R. Discussion Papers.
  2. Michiel de Pooter & Martin Martens & Dick van Dijk, 2008. "Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 199-229.
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Cited by:
  1. Charlotte Christiansen & Maik Schmeling & Andreas Schrimpf, 2010. "A Comprehensive Look at Financial Volatility Prediction by Economic Variables," CREATES Research Papers 2010-58, School of Economics and Management, University of Aarhus.
  2. Baetje, Fabian & Menkhoff, Lukas, 2013. "Macro determinants of U.S. stock market risk premia in bull and bear markets," Hannover Economic Papers (HEP) dp-520, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

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