Semi-parametric Estimation and the Predictability of Stock Market Returns: Some Lessons from Japan
Abstract
This paper attempts to explore whether lagged variables that help predict stock returns are merely proxying for mismeasured risk. Therefore, three different ways of measuring risk are employed (i.e., semiparametric, GARCH, and lagged squared returns). In an application to Japanese data, four key predictor variables are shown to have nontrivial additional forecasting power irrespective of how they measure risk. Interestingly, unlike the United States, the level of the lagged dividend yield is not positively correlated with returns in either Japan or South Korea. Copyright 1991 by The Review of Economic Studies Limited.Download Info
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Bibliographic Info
Article provided by Wiley Blackwell in its journal Review of Economic Studies.
Volume (Year): 58 (1991)
Issue (Month): 3 (May)
Pages: 547-63
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- PERRON, Benoît, 1999.
"Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off,"
Cahiers de recherche
9901, Universite de Montreal, Departement de sciences economiques.
- Benoit Perron, 2003. "Semiparametric Weak-Instrument Regressions with an Application to the Risk-Return Tradeoff," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 424-443, May.
- Benoit Perron, 2002. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off," CIRANO Working Papers 2002s-88, CIRANO.
- Benoit Perron, 2000. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-return Trade-off," Econometric Society World Congress 2000 Contributed Papers 1576, Econometric Society.
- Peter F. Christoffersen & Francis X. Diebold, 2006.
"Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics,"
Management Science,
INFORMS, vol. 52(8), pages 1273-1287, August.
- Peter F. Christoffersen & Francis X. Diebold, 2003. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," NBER Working Papers 10009, National Bureau of Economic Research, Inc.
- Peter F. Christoffersen & Francis X. Diebold, 2004. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," CFS Working Paper Series 2004/08, Center for Financial Studies.
- Peter F. Christoffersen & Francis X.Diebold, 2003. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," PIER Working Paper Archive 04-009, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Nijman, T.E. & Palm, F.C., 1991.
"Recent developments in modeling volatility in financial data,"
Discussion Paper
1991-68, Tilburg University, Center for Economic Research.
- Nijman, T.E. & Palm, F.C., 1991. "Recent Developments in Modeling Volatility in Financial Data," Papers 9168, Tilburg - Center for Economic Research.
- Jonathan Manton & Anton Muscatelli & Vikram Krishnamurthy & Stan Hurn, . "Modelling Stock Market Excess Returns by Markov Modulated Gaussian Noise," Working Papers 9806, Business School - Economics, University of Glasgow.
- Marquering, W. & Verbeek, M.J.C.M., 2000. "The Economic Value of Predicting Stock Index Returns and Volatility," Discussion Paper 2000-78, Tilburg University, Center for Economic Research.
- Peter Christoffersen & Francis X. Diebold, 2002. "Financial Asset Returns, Market Timing, and Volatility Dynamics," CIRANO Working Papers 2002s-02, CIRANO.
- Hallam, Arne, 1992. "A Brief Overview Of Nonparametric Methods In Economics," Northeastern Journal of Agricultural and Resource Economics, Northeastern Agricultural and Resource Economics Association, vol. 21(2), October.
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