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Risk-Return Tradeoff in U.S. Stock Returns over the Business Cycle

Published online by Cambridge University Press:  15 December 2011

Henri Nyberg*
Affiliation:
Department of Political and Economic Studies, Economics, University of Helsinki, PO Box 17, Helsinki 00014, Finland, and HECER. [email protected]

Abstract

In the empirical finance literature, findings on the risk-return tradeoff in excess stock market returns are ambiguous. In this study, I develop a new qualitative response (QR)-generalized autoregressive conditional heteroskedasticity-in-mean (GARCH-M) model combining a probit model for a binary business cycle indicator and a regime-switching GARCH-M model for excess stock market return with the business cycle indicator defining the regime. Estimation results show that there is statistically significant variation in the U.S. excess stock returns over the business cycle. However, consistent with the conditional intertemporal capital asset pricing model (ICAPM), there is a positive risk-return relationship between volatility and expected return independent of the state of the economy.

Type
Research Articles
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2012

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References

Ang, A.; Piazzesi, M.; and Wei, M.. “What Does the Yield Curve Tell Us About the GDP Growth?Journal of Econometrics, 131 (2006), 359403.Google Scholar
Bali, T. G. “Testing Empirical Performance of Stochastic Volatility Models of the Short-Term Interest Rate.” Journal of Financial and Quantitative Analysis, 35 (2000), 191215.CrossRefGoogle Scholar
Bali, T. G. “The Intertemporal Relation between Expected Returns and Risk.” Journal of Financial Economics, 87 (2008), 101131.CrossRefGoogle Scholar
Bali, T. G., and Engle, R. F.. “Resurrecting the Conditional CAPM with Dynamic Conditional Correlations.” Working Paper, Georgetown University (2010a).CrossRefGoogle Scholar
Bali, T. G., and Engle, R. F.. “The Intertemporal Capital Asset Pricing Model with Dynamic Conditional Correlations.” Journal of Monetary Economics, 57 (2010b), 377390.Google Scholar
Bauwens, L.; Preminger, A.; and Rombouts, J. V. K.. “Theory and Inference for a Markov Switching GARCH Model.” Econometrics Journal, 13 (2010), 218244.CrossRefGoogle Scholar
Bernard, H., and Gerlach, S.. “Does the Term Structure Predict Recessions? The International Evidence.” International Journal of Finance and Economics, 3 (1998), 195215.3.0.CO;2-M>CrossRefGoogle Scholar
Boudoukh, J.; Richardson, M.; and Smith, T.. “Is the Ex Ante Risk Premium Always Positive? A New Approach to Testing Conditional Asset Pricing Models.” Journal of Financial Economics, 34 (1993), 387408.CrossRefGoogle Scholar
Campbell, J. Y., and Hentschel, L.. “No News Is Good News: An Asymmetric Model of Changing Volatility in Stock Returns.” Journal of Financial Economics, 31 (1992), 281318.CrossRefGoogle Scholar
Chauvet, M., and Potter, S.. (2000). “Coincident and Leading Indicators of the Stock Market.” Journal of Empirical Finance, 7(2000), 87111.CrossRefGoogle Scholar
Chauvet, M., and Potter, S.. “Nonlinear Risk.” Macroeconomic Dynamics, 5 (2001), 621646.CrossRefGoogle Scholar
Chen, N.-F. “Financial Investment Opportunities and the Macroeconomy.” Journal of Finance, 46 (1991), 529554.Google Scholar
Dueker, M.Dynamic Forecasts of Qualitative Variables: A Qual VAR Model of U.S. Recessions.” Journal of Business and Economic Statistics, 23 (2005), 96104.Google Scholar
Engle, R. F. “Stock Volatility and the Crash of ’87: Discussion.” Review of Financial Studies, 3 (1990), 103106.Google Scholar
Engle, R. F.; Lilien, D. M.; and Robins, R. P.. “Estimating Time Varying Risk Premia in the Term Structure: The ARCH-M Model.” Econometrica, 55 (1987), 391407.CrossRefGoogle Scholar
Engle, R. F., and Ng, V. K.. “Measuring and Testing the Impact of News on Volatility.” Journal of Finance, 48 (1993), 17491778.CrossRefGoogle Scholar
Estrella, A.A New Measure of Fit for Equations with Dichotomous Dependent Variables.” Journal of Business and Economic Statistics, 16 (1998), 198205.Google Scholar
Estrella, A., and Mishkin, F. S.. “Predicting U.S. Recessions: Financial Variables as Leading Indicators.” Review of Economics and Statistics, 80 (1998), 4561.Google Scholar
Fama, E. F. “Stock Returns, Expected Returns, and Real Activity.” Journal of Finance, 45 (1990), 10891108.CrossRefGoogle Scholar
Franses, P. H., and van Dijk, D.. Non-Linear Time Series Models in Empirical Finance. New York: Cambridge University Press (2000).Google Scholar
French, K. R.; Schwert, G. W.; and Stambaugh, R. F.. “Expected Stock Returns and Volatility.” Journal of Financial Economics, 19 (1987), 329.Google Scholar
Ghysels, E.; Santa-Clara, P.; and Valkanov, R.. “There Is a Risk-Return Trade-Off after All?Journal of Financial Economics, 76 (2005), 509548.Google Scholar
Glosten, L. R.; Jagannathan, R., , R.; and Runkle, D. E.. “On the Relation between the Expected Value of the Volatility of the Nominal Excess Return on Stocks.” Journal of Finance, 48 (1993), 17791801.Google Scholar
Guo, H., and Whitelaw, R. F.. “Uncovering the Risk-Return Relation in the Stock Market.” Journal of Finance, 61 (2006), 14331463.Google Scholar
Hamilton, J. D., and Lin, G.. “Stock Market Volatility and the Business Cycle.” Journal of Applied Econometrics, 11 (1996), 573593.3.0.CO;2-T>CrossRefGoogle Scholar
Hansen, B. E. “Autoregressive Conditional Density Estimation.” International Economic Review, 35 (1994), 705730.CrossRefGoogle Scholar
Kauppi, H., and Saikkonen, P.. “Predicting U.S. Recessions with Dynamic Binary Response Models.” Review of Economics and Statistics, 90 (2008), 777791.Google Scholar
Kim, S.-W., and Lee, B.-S.. “Stock Returns, Asymmetric Volatility, Risk Aversion, and Business Cycle: Some New Evidence.” Economic Inquiry, 46 (2008), 131148.Google Scholar
King, T. B.; Levin, A. T.; and Perli, R.. “Financial Market Perceptions of Recession Risk.” Finance and Economics Discussion Series, 57. Board of Governors of the Federal Reserve System (2007).Google Scholar
Lange, T., and Rahbek, A.. “An Introduction to Regime Switching Time Series Models.” In Handbook of Financial Time Series, Vol. I, Andersen, T. G., Davis, R. A., Kreiß, J. P., and Mikosch, T., eds. Berlin and Heidelberg, Germany: Springer-Verlag (2009).Google Scholar
Lanne, M., and Saikkonen, P.. “Why Is It So Difficult to Uncover the Risk-Return Tradeoff in Stock Returns?Economics Letters, 92 (2006), 118125.CrossRefGoogle Scholar
Leung, M. T.; Daouk, H.; and Chen, A.-S.. “Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models.” International Journal of Forecasting, 16 (2000), 173190.CrossRefGoogle Scholar
Merton, R. C. “An Intertemporal Capital Asset Pricing Model.” Econometrica, 41 (1973), 867887.Google Scholar
Merton, R. C. “On Estimating the Expected Return on the Market: An Exploratory Investigation.” Journal of Financial Economics, 8 (1980), 323361.Google Scholar
Nyberg, H.Dynamic Probit Models and Financial Variables in Recession Forecasting.” Journal of Forecasting, 29 (2010a), 215230.Google Scholar
Nyberg, H.QR-GARCH-M Model for Risk-Return Tradeoff in U.S. Stock Returns and Business Cycles.” HECER Discussion Paper 294, University of Helsinki (2010b).Google Scholar
Nyberg, H.Forecasting the Direction of the U.S. Stock Market with Dynamic Binary Probit Models.” International Journal of Forecasting, 27 (2011), 561578.Google Scholar
Perez-Quiros, G., and Timmermann, A.. “Business Cycle Asymmetries in Stock Returns: Evidence from Higher Order Moments and Conditional Densities.” Journal of Econometrics, 103 (2001), 259306.CrossRefGoogle Scholar
Pesaran, M. H., and Timmermann, A.. “A Simple Nonparametric Test of Predictive Performance.” Journal of Business and Economics Statistics, 10 (1992), 461465.CrossRefGoogle Scholar
Pesaran, M. H., and Timmermann, A.. “Predictability of Stock Returns: Robustness and Economic Significance.” Journal of Finance, 50 (1995), 12011228.Google Scholar
Rudebusch, G. D., and Williams, J. C.. “Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve.” Journal of Business and Economic Statistics, 27 (2009), 492503.Google Scholar
Rydberg, T. H., and Shephard, N.. “Dynamics of Trade-by-Trade Price Movements: Decomposition and Models.” Journal of Financial Econometrics, 1 (2003), 225.Google Scholar
Schwarz, G.Estimating the Dimension of a Model.” Annals of Statistics, 6 (1978), 461464.CrossRefGoogle Scholar
Schwert, G. W. “Why Does Stock Market Volatility Change Over Time?Journal of Finance, 44 (1989), 11151153.Google Scholar
Schwert, G. W. “Stock Returns and Real Activity: A Century of Evidence.” Journal of Finance, 45 (1990), 12371257.Google Scholar
Scruggs, J. T. “Resolving the Puzzling Intertemporal Relation between the Market Risk Premium and Conditional Market Variance: A Two-Factor Approach.” Journal of Finance, 53 (1998), 575603.Google Scholar
Whitelaw, R. F. “Time Variations and Covariations in the Expectation and Volatility of Stock Market Returns.” Journal of Finance, 49 (1994), 515541.Google Scholar
Wright, J. H. “The Yield Curve and Predicting Recessions.” Finance and Economics Discussion Series, 7. Board of Governors of the Federal Reserve System (2006).Google Scholar