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Business Cycle Asymmetries In Stock Returns: Robust Evidence

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  • KIANI, Khurshid M.

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Abstract

In this study we employ augmented and switching time series models to find possible existence of business cycle asymmetries in U.S. stock returns. Our approach is fully parametric and testing strategy is robust to any conditional heteroskedasticity, and outliers that may be present. We also approximate in sample as well as out-of-sample forecasts from artificial neural networks for testing business cycle nonlinearities in U.S. stock returns. Our results based on nonlinear augmented and switching time series models show a strong evidence of business cycle asymmetries in conditional mean dynamics of U.S. stock returns. These results also show that conditional heteroskedasticity is unimportant when testing for asymmetries in conditional mean. Moreover, the conditional volatility in stock returns is asymmetric and is more pronounced in recessions than in expansion phase of business cycles. Similarly, the results based on neural network models show a statistically significant evidence of business cycle nonlinearities in US stock returns. The magnitude of these nonlinearities is more obvious in post World War II era than in the full sample period.

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Bibliographic Info

Article provided by Euro-American Association of Economic Development in its journal International Journal of Applied Econometrics and Quantitative Studies .

Volume (Year): 4 (2007)
Issue (Month): 2 ()
Pages: 99-120

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Handle: RePEc:eaa:ijaeqs:v:4:y2007:i:2_6

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Related research

Keywords: asymmetries; business cycles; conditional heteroskedasticity; long memory; nonlinearities; outliers; excess returns; stable distributions;

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References

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  1. Prasad Bidarkota & Khurshid M. Kiani, 2003. "On Business Cycle Asymmetries in G7 Countries," Working Papers 0308, Florida International University, Department of Economics.
  2. Beaudry, Paul & Koop, Gary, 1993. "Do recessions permanently change output?," Journal of Monetary Economics, Elsevier, vol. 31(2), pages 149-163, April.
  3. Ghose, Devajyoti & Kroner, Kenneth F., 1995. "The relationship between GARCH and symmetric stable processes: Finding the source of fat tails in financial data," Journal of Empirical Finance, Elsevier, vol. 2(3), pages 225-251, September.
  4. Francis X. Diebold & Glenn D. Rudebusch, 1988. "A nonparametric investigation of duration dependence in the American business cycle," Working Paper Series / Economic Activity Section 90, Board of Governors of the Federal Reserve System (U.S.).
  5. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
  6. Khurshid M. Kiani & Prasad V. Bidarkota & Terry L. Kastens, 2005. "Forecast performance of neural networks and business cycle asymmetries," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 1(4), pages 205-210, July.
  7. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
  8. Pérez Quirós, Gabriel & Timmermann, Allan, 2001. "Business cycle asymmetries in stock returns: evidence from higher order moments and conditional densities," Working Paper Series 0058, European Central Bank.
  9. Danielsson, Jon, 1994. "Stochastic volatility in asset prices estimation with simulated maximum likelihood," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 375-400.
  10. Lee, Tae-Hwy & White, Halbert & Granger, Clive W. J., 1993. "Testing for neglected nonlinearity in time series models : A comparison of neural network methods and alternative tests," Journal of Econometrics, Elsevier, vol. 56(3), pages 269-290, April.
  11. Bidarkota Prasad V., 1999. "Sectoral Investigation of Asymmetries in the Conditional Mean Dynamics of the Real U.S. GDP," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(4), pages 1-12, January.
  12. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
  13. Simon M. Potter, 1993. "A Nonlinear Approach to U.S. GNP," UCLA Economics Working Papers 693, UCLA Department of Economics.
  14. Gary Koop & Simon M. Potter, 2001. "Are apparent findings of nonlinearity due to structural instability in economic time series?," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 38.
  15. Liu, Shi-Miin & Brorsen, B Wade, 1995. "Maximum Likelihood Estimation of a Garch-Stable Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(3), pages 273-85, July-Sept.
  16. Norwood, F. Bailey & Lusk, Jayson L. & Brorsen, B. Wade, 2004. "Model Selection for Discrete Dependent Variables: Better Statistics for Better Steaks," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(03), December.
  17. Khurshid Kiani, 2005. "Detecting Business Cycle Asymmetries Using Artificial Neural Networks and Time Series Models," Computational Economics, Society for Computational Economics, vol. 26(1), pages 65-89, August.
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