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State-dependent Momentum in International Stock Markets

  • Dirk G Baur
  • Thomas Dimpfl

    (University of Tubingen)

We estimate quantile autoregression (QAR) models to analyze variations in the autoregressive coefficients of 55 international stock index returns and demonstrate that it is important to allow the autoregressive parameters to vary with quantiles. The empirical results identify distinctively different patterns of autoregressive coefficients in the lower, central and upper quantiles of the distribution across all countries. More specifically, the study suggests that investors follow momentum strategies in lower quantiles or "bad states". We also demonstrate that the quantile autoregression estimates can be used to test for asymmetric responses of the volatility.

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File URL: http://www.finance.uts.edu.au/research/wpapers/wp169.pdf
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Paper provided by Finance Discipline Group, UTS Business School, University of Technology, Sydney in its series Working Paper Series with number 169.

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Length: 37
Date of creation: 01 Aug 2012
Date of revision:
Handle: RePEc:uts:wpaper:169
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  1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  2. Engle, Robert F & Ng, Victor K, 1993. "Time-Varying Volatility and the Dynamic Behavior of the Term Structure," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 25(3), pages 336-49, August.
  3. Baur, Dirk G. & Dimpfl, Thomas & Jung, Robert C., 2012. "Stock return autocorrelations revisited: A quantile regression approach," University of Tuebingen Working Papers in Economics and Finance 24, .
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  9. Hibbert, Ann Marie & Daigler, Robert T. & Dupoyet, Brice, 2008. "A behavioral explanation for the negative asymmetric return-volatility relation," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2254-2266, October.
  10. Goetzmann, William N. & Massa, Massimo, 2002. "Daily Momentum and Contrarian Behavior of Index Fund Investors," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 37(03), pages 375-389, September.
  11. James W. Taylor, 2008. "Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(3), pages 382-406, Summer.
  12. Brian H. Boyer & Tomomi Kumagai & Kathy Yuan, 2006. "How Do Crises Spread? Evidence from Accessible and Inaccessible Stock Indices," Journal of Finance, American Finance Association, vol. 61(2), pages 957-1003, 04.
  13. Hansen, Bruce E., 1995. "Rethinking the Univariate Approach to Unit Root Testing: Using Covariates to Increase Power," Econometric Theory, Cambridge University Press, vol. 11(05), pages 1148-1171, October.
  14. Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993. "On the relation between the expected value and the volatility of the nominal excess return on stocks," Staff Report 157, Federal Reserve Bank of Minneapolis.
  15. Asem, Ebenezer, 2009. "Dividends and price momentum," Journal of Banking & Finance, Elsevier, vol. 33(3), pages 486-494, March.
  16. Chuang, Chia-Chang & Kuan, Chung-Ming & Lin, Hsin-Yi, 2009. "Causality in quantiles and dynamic stock return-volume relations," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1351-1360, July.
  17. Ilan Goldfajn & Taimur Baig, 1999. "Financial market contagion in the Asian crisis," Textos para discussão 400, Department of Economics PUC-Rio (Brazil).
  18. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
  19. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
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