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Unexpected volatility and intraday serial correlation

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  • Simone Bianco
  • Roberto Reno

Abstract

We study the impact of volatility on intraday serial correlation, at time scales of less than 20 minutes, exploiting a data set with all transactions on SPX500 futures from 1993 to 2001. We show that, while realized volatility and intraday serial correlation are linked, this relation is driven by unexpected volatility only, that is by the fraction of volatility that cannot be forecasted by a linear model. The impact of predictable volatility is instead found to be negative (LeBaron effect). Our results are robust to microstructure noise, and they confirm the leading economic theories on price formation.

Suggested Citation

  • Simone Bianco & Roberto Reno, 2009. "Unexpected volatility and intraday serial correlation," Quantitative Finance, Taylor & Francis Journals, vol. 9(4), pages 465-475.
    • Handle: RePEc:taf:quantf:v:9:y:2009:i:4:p:465-475
    DOI: 10.1080/14697680802452050
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    Cited by:

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    2. Dimitrakopoulos, Stefanos & Tsionas, Mike, 2019. "Ordinal-response GARCH models for transaction data: A forecasting exercise," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1273-1287.
    3. Ajeet Jain & Sascha Strobl, 2017. "The effect of volatility persistence on excess returns," Review of Financial Economics, John Wiley & Sons, vol. 32(1), pages 58-63, January.
    4. Bommarito, Michael J. & Duran, Ahmet, 2018. "Spectral analysis of time-dependent market-adjusted return correlation matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 273-282.
    5. Jain, Ajeet & Strobl, Sascha, 2017. "The effect of volatility persistence on excess returns," Review of Financial Economics, Elsevier, vol. 32(C), pages 58-63.

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