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Volatility of Stock-Market Indexes--An Analysis Based on SEMIFAR Models

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  • Beran, Jan
  • Ocker, Dirk

Abstract

By applying SEMIFAR models, we examine "long memory" in the volatility of worldwide stock-market indexes. Our analysis yields strong evidence of "long memory" in stock-market volatility, either in terms of stochastic long-range dependence or in the form of deterministic trends. In some cases, both components are detected in the data. Thus, at least partially, there appears to be even stronger and more systematic long memory than suggested by a stationary model with long-range dependence.

Suggested Citation

  • Beran, Jan & Ocker, Dirk, 2001. "Volatility of Stock-Market Indexes--An Analysis Based on SEMIFAR Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 103-116, January.
    • Handle: RePEc:bes:jnlbes:v:19:y:2001:i:1:p:103-16
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    Citations

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    Cited by:

    1. Choi, Kyongwook & Yu, Wei-Choun & Zivot, Eric, 2010. "Long memory versus structural breaks in modeling and forecasting realized volatility," Journal of International Money and Finance, Elsevier, vol. 29(5), pages 857-875, September.
    2. Kousik Guhathakurta & Sharad Nath Bhattacharya & Mousumi Bhattacharya, 2012. "Exploring Presence of Long Memory in Emerging and Developed Stock Markets," Working papers 107, Indian Institute of Management Kozhikode.
    3. Feng, Yuanhua, 2002. "Modelling Different Volatility Components," CoFE Discussion Papers 02/18, University of Konstanz, Center of Finance and Econometrics (CoFE).
    4. Chin, Wen Cheong, 2008. "Heavy-tailed value-at-risk analysis for Malaysian stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4285-4298.
    5. Feng, Yuanhua, 2004. "Simultaneously Modeling Conditional Heteroskedasticity And Scale Change," Econometric Theory, Cambridge University Press, vol. 20(3), pages 563-596, June.
    6. Ana Pérez & Esther Ruiz, 2002. "Modelos de memoria larga para series económicas y financieras," Investigaciones Economicas, Fundación SEPI, vol. 26(3), pages 395-445, September.
    7. Jan Beran & Yuanhua Feng, 2002. "Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(2), pages 291-311, June.
    8. Beran, Jan & Feng, Yuanhua, 2002. "Recent Developments in Non- and Semiparametric Regression with Fractional Time Series Errors," CoFE Discussion Papers 02/13, University of Konstanz, Center of Finance and Econometrics (CoFE).
    9. Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303.
    10. Kin-Yip Ho & Ka Cheng Tsui, 2004. "Volatility Dynamics of the Tokyo Stock Exchange: A Sectoral Analysis based on the Multivariate GARCH Approach," Money Macro and Finance (MMF) Research Group Conference 2004 12, Money Macro and Finance Research Group.
    11. TEYSSIERE, Gilles, 2003. "Interaction models for common long-range dependence in asset price volatilities," LIDAM Discussion Papers CORE 2003026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    12. Cheong, Chin Wen, 2008. "Time-varying volatility in Malaysian stock exchange: An empirical study using multiple-volatility-shift fractionally integrated model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 889-898.
    13. Chun-Hung Chen & Wei-Choun Yu & Eric Zivot, 2009. "Predicting Stock Volatility Using After-Hours Information," Working Papers UWEC-2009-01, University of Washington, Department of Economics.
    14. Axioglou, Christos & Skouras, Spyros, 2011. "Markets change every day: Evidence from the memory of trade direction," Journal of Empirical Finance, Elsevier, vol. 18(3), pages 423-446, June.

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