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US stock market volatility persistence: evidence before and after the burst of the IT bubble

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Abstract

In this paper we test whether the US stock market volatility presents a different behavior before and after the burst of the IT bubble. Using long range dependence techniques we examine the order of integration in the absolute and squared returns in three daily stock market indices (DJIA, S&P and NASDAQ). The results indicate that both absolute and squared returns present long memory behavior. In general, the highest orders of integration in the volatility processes correspond to the NASDAQ index. The results also show that in most cases the volatility is more persistent in the bear market than in the bull market.

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Notes

  1. Most of these papers studying stock market volatility define stock markets cycles (bull and bear markets) using a traditional algorithm developed by Bry and Boschan (1971) to stock prices. An alternative approach to identify bull and bear markets is the regime switching models (see, for e.g., Turner et al. 1989; Maheu and McCurdy 2000; Ang and Bekaert 2002; Guidolin and Timmermann 2005; recently, Tu 2006 among others).

  2. For the purpose of the present work, we define an I(0) process as a covariance stationary process with spectral density that is positive and finite at the zero frequency.

  3. Excellent surveys of fractionally integrated models can be found in Beran (1994), Baillie (1996) and Robinson (2003).

  4. See Velasco (1999) and more recently, Phillips and Shimotsu (2004, 2005) for further refinements of this procedure.

  5. The sample period before the burst of the IT bubble covers from January 3rd, 1994 to March 10th, 2000 yielding 1,614 observations, and the period after the burst of the bubble covers from March 11th, 2000 to October 16th, 2002 yielding 678 observations. The second subsample ends on October 2002 when the Nasdaq index presents a local minimum after the burst of the IT bubble. Thus, though only two and a half years are employed in the second subsample, it is sufficiently large (678 observations) to examine long range dependence.

  6. The methods employed in the article are robust against non-Gaussian disturbances.

  7. In the tests of Robinson (1994), we test H 0:d = d 0, with d 0-values from 0 to 2, with 0.001 increments.

  8. Some papers that find similar evidence of long memory in the volatility processes are Granger and Ding (1995a, b), Cavalcante and Assaf (2004).

  9. If they are white noise, the estimates of d are greater after the bubble for the DJIA and S&P indices with the absolute returns and for the S&P index with the squared returns.

  10. A very similar break-date was obtained when using other break-date detection methods based on non-fractional models (Andrews 1993, 2003; Kurozumi 2005).

  11. Confidence intervals for the break-date estimates are not computed. They can be obtained via bootstrapping though it is highly computationally intensive.

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Acknowledgment

We thank the editor and two anonymous referees for their helpful comments and constructive suggestions on the paper. Juncal Cunado and Luis A. Gil-Alana gratefully acknowledge financial support from the Spanish Ministry of Science and Technology (SEJ2005-07657/ECON). Fernando Perez de Gracia acknowledges research support from the Spanish Ministry of Science and Technology and FEDER through Grant SEJ2005-06302/ECON and from the Plan Especial de Investigacion de la Universidad de Navarra.

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  1. Department of Economics, Universidad de Navarra, Campus Universitario, 31080, Pamplona, Spain

    J. Cuñado, L. A. Gil-Alana & F. Perez de Gracia

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  1. J. Cuñado
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  3. F. Perez de Gracia
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Correspondence to F. Perez de Gracia.

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Cuñado, J., Gil-Alana, L.A. & de Gracia, F.P. US stock market volatility persistence: evidence before and after the burst of the IT bubble. Rev Quant Finan Acc 33, 233–252 (2009). https://doi.org/10.1007/s11156-009-0111-5

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