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Infinite Density at the Median and the Typical Shape of Stock Return Distributions

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  • Chirok Han
  • Jin Seo Cho
  • Peter C. B. Phillips

Abstract

Statistics are developed to test for the presence of an asymptotic discontinuity (or infinite density or peakedness) in a probability density at the median. The approach makes use of work by Knight (1998) on L 1 estimation asymptotics in conjunction with nonparametric kernel density estimation methods. The size and power of the tests are assessed, and conditions under which the tests have good performance are explored in simulations. The new methods are applied to stock returns of leading companies across major U.S. industry groups. The results confirm the presence of infinite density at the median as a new significant empirical evidence for stock return distributions.

Suggested Citation

  • Chirok Han & Jin Seo Cho & Peter C. B. Phillips, 2011. "Infinite Density at the Median and the Typical Shape of Stock Return Distributions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 282-294, April.
    • Handle: RePEc:taf:jnlbes:v:29:y:2011:i:2:p:282-294
    DOI: 10.1198/jbes.2010.07327
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    1. Scholes, Myron & Williams, Joseph, 1977. "Estimating betas from nonsynchronous data," Journal of Financial Economics, Elsevier, vol. 5(3), pages 309-327, December.
    2. Rustam Ibragimov, 2005. "On Efficiency of Linear Estimators Under Heavy-Tailedness," Harvard Institute of Economic Research Working Papers 2085, Harvard - Institute of Economic Research.
    3. Rogers, Alan J., 2001. "Least Absolute Deviations Regression Under Nonstandard Conditions," Econometric Theory, Cambridge University Press, vol. 17(4), pages 820-852, August.
    4. Rustam Ibragimov & Johan Walden, 2006. "The Limits of Diversification When Losses May Be Large," Harvard Institute of Economic Research Working Papers 2104, Harvard - Institute of Economic Research.
    5. Kalman J. Cohen & Gabriel A. Hawawini & Steven F. Maier & Robert A. Schwartz & David K. Whitcomb, 1983. "Estimating and Adjusting for the Intervalling-Effect Bias in Beta," Management Science, INFORMS, vol. 29(1), pages 135-148, January.
    6. Dimson, Elroy, 1979. "Risk measurement when shares are subject to infrequent trading," Journal of Financial Economics, Elsevier, vol. 7(2), pages 197-226, June.
    7. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    8. Lo, Andrew W. & Craig MacKinlay, A., 1990. "An econometric analysis of nonsynchronous trading," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 181-211.
    9. Ibragimov, Rustam & Walden, Johan, 2007. "The limits of diversification when losses may be large," Scholarly Articles 2624460, Harvard University Department of Economics.
    10. Phillips, P.C.B., 1991. "A Shortcut to LAD Estimator Asymptotics," Econometric Theory, Cambridge University Press, vol. 7(4), pages 450-463, December.
    11. Pollard, David, 1991. "Asymptotics for Least Absolute Deviation Regression Estimators," Econometric Theory, Cambridge University Press, vol. 7(2), pages 186-199, June.
    12. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    13. Koenker, Roger & Zhao, Quanshui, 1996. "Conditional Quantile Estimation and Inference for Arch Models," Econometric Theory, Cambridge University Press, vol. 12(5), pages 793-813, December.
    14. Cohen, Kalman J. & Hawawini, Gabriel A. & Maier, Steven F. & Schwartz, Robert A. & Whitcomb, David K., 1983. "Friction in the trading process and the estimation of systematic risk," Journal of Financial Economics, Elsevier, vol. 12(2), pages 263-278, August.
    15. Ibragimov, Rustam & Walden, Johan, 2007. "The limits of diversification when losses may be large," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2551-2569, August.
    16. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2003. "A theory of power-law distributions in financial market fluctuations," Nature, Nature, vol. 423(6937), pages 267-270, May.
    17. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    18. repec:cup:etheor:v:23:y:2007:i:03:p:501-517 is not listed on IDEAS
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    Cited by:

    1. Cho, Jin Seo & Han, Chirok & Phillips, Peter C.B., 2010. "Lad Asymptotics Under Conditional Heteroskedasticity With Possibly Infinite Error Densities," Econometric Theory, Cambridge University Press, vol. 26(3), pages 953-962, June.
    2. Cho, Jin Seo & Kim, Tae-hwan & Shin, Yongcheol, 2015. "Quantile cointegration in the autoregressive distributed-lag modeling framework," Journal of Econometrics, Elsevier, vol. 188(1), pages 281-300.
    3. Byung-hill Jun & Hosin Song, 2019. "Tests for Detecting Probability Mass Points," Korean Economic Review, Korean Economic Association, vol. 35, pages 205-248.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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