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Daily Stock Market Volatility: 1928--1989

Author

Listed:
  • Andrew L. Turner

    (Frank Russell Company, P.O. Box 1616, Tacoma, Washington 98401)

  • Eric J. Weigel

    (Frank Russell Company, P.O. Box 1616, Tacoma, Washington 98401)

Abstract

This paper examines the daily return variability of the S&P 500 and the Dow Jones indices over the 1928--1989 period. We use the traditional close-to-close standard deviation of returns, two alternative estimators incorporating the daily high and low of the index, and a robust estimator to measure the volatility of stock index returns. The 1980s were the third most volatile decade behind the 1920s and 30s. To a large extent, this was caused by the anomalous behavior of the fourth quarter of 1987. Returns in the 1980s had far more skewness and kurtosis than in any other decade studied; these results were not entirely due to 1987, as returns in 1988 and 1989 had large measures of both skewness and kurtosis. The frequency of extreme-return events increased in the 1980s, but was still dramatically less than the 1920s and 30s. When extreme negative days occurred in the 1980s the losses tended to be more severe than in the previous four decades. Extreme-return days are preceded by significant losses and are intertemporally clustered. There is no evidence of short-term market reversals after either positive or negative jumps in stock index returns.

Suggested Citation

  • Andrew L. Turner & Eric J. Weigel, 1992. "Daily Stock Market Volatility: 1928--1989," Management Science, INFORMS, vol. 38(11), pages 1586-1609, November.
    • Handle: RePEc:inm:ormnsc:v:38:y:1992:i:11:p:1586-1609
    DOI: 10.1287/mnsc.38.11.1586
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    Citations

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    Cited by:

    1. John Randal & Peter Thomson & Martin Lally, 2004. "Non-parametric estimation of historical volatility," Quantitative Finance, Taylor & Francis Journals, vol. 4(4), pages 427-440.
    2. Georgios Mamanis, 2021. "Analyzing the Performance of a Two-Tail-Measures-Utility Multi-objective Portfolio Optimization Model," SN Operations Research Forum, Springer, vol. 2(4), pages 1-18, December.
    3. Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2015. "Efficient Skewness/Semivariance Portfolios," GEMF Working Papers 2015-05, GEMF, Faculty of Economics, University of Coimbra.
    4. Alexandros M. Goulielmos, 2015. "The Multi-faceted Character of Risk in Maritime Freight Markets (Panamax) 1996-2012," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 65(1-2), pages 67-86, January-M.
    5. Joro, Tarja & Na, Paul, 2006. "Portfolio performance evaluation in a mean-variance-skewness framework," European Journal of Operational Research, Elsevier, vol. 175(1), pages 446-461, November.
    6. Lee, Kangsan & Jeong, Daeyoung, 2023. "Too much is too bad: The effect of media coverage on the price volatility of cryptocurrencies," Journal of International Money and Finance, Elsevier, vol. 133(C).
    7. Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2016. "Efficient skewness/semivariance portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 17(5), pages 331-346, September.
    8. Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2015. "Portfolio Management With Higher Moments: The Cardinality Impact," GEMF Working Papers 2015-15, GEMF, Faculty of Economics, University of Coimbra.
    9. Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2015. "Portfolio Management With Higher Moments: The Cardinality Impact," GEMF Working Papers 2015-15, GEMF, Faculty of Economics, University of Coimbra.
    10. David Walsh & Glenn Yu-Gen Tsou, 1998. "Forecasting index volatility: sampling interval and non-trading effects," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 477-485.
    11. Bernhard Nietert, 1999. "Dynamische Portfolio-Selektion unter Berücksichtigung von Kurssprüngen," Schmalenbach Journal of Business Research, Springer, vol. 51(9), pages 832-866, September.
    12. J. Francisco Rubio & Neal Maroney & M. Kabir Hassan, 2018. "Can Efficiency of Returns Be Considered as a Pricing Factor?," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 25-54, June.
    13. Qiuyun Wang & Lu Liu, 2022. "Pandemic or panic? A firm-level study on the psychological and industrial impacts of COVID-19 on the Chinese stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-38, December.
    14. Philip Z. Maymin, 2011. "The minimal model of financial complexity," Quantitative Finance, Taylor & Francis Journals, vol. 11(9), pages 1371-1378, February.
    15. Philip Maymin, 2009. "The Minimal Model of Financial Complexity," Papers 0901.3812, arXiv.org, revised Feb 2010.
    16. Juho Kanniainen, 2009. "Can properly discounted projects follow geometric Brownian motion?," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 70(3), pages 435-450, December.
    17. Kyuho Jin & Joowon Lee & Sung Min Hong, 2021. "The Dark Side of Managing for the Long Run: Examining When Family Firms Create Value," Sustainability, MDPI, vol. 13(7), pages 1-20, March.

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