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The relationship between implied volatility and autocorrelation

Author

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  • Robert W. Faff
  • Michael D. McKenzie

Abstract

Purpose - This paper empirically assesses the determinants of conditional stock index autocorrelation with particular emphasis on the impact of return volatility that are theoretically linked through the behaviour of feedback traders. Design/methodology/approach - The S&P 100, 500 and the NASDAQ 100 index are considered and volatility in each series is captured using option‐implied estimates taken from the Chicago Board Options Exchange. A seemingly unrelated regression approach is used in which trading volume and volatility are simultaneously modelled. Findings - The results of this study suggest that low or even negative return autocorrelations are more likely in situations where: return volatility is high; price falls by a large amount; traded stock volumes are high; and the economy is in a recessionary phase. Research limitations/implications - The results confirm that previous related work showing a link between autocorrelation and volatility is not induced by a mechanical relation. Practical implications - Usage of endogenously determined volatility measures in this area of the literature is justified. Originality/value - This study provides a robustness test of the autocorrelation/volatility relation, as well as a further exploration of the utility inherent in option‐implied volatility.

Suggested Citation

  • Robert W. Faff & Michael D. McKenzie, 2007. "The relationship between implied volatility and autocorrelation," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 3(2), pages 191-196, April.
    • Handle: RePEc:eme:ijmfpp:v:3:y:2007:i:2:p:191-196
    DOI: 10.1108/17439130710738736
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    Citations

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    Cited by:

    1. Andrikopoulos, Panagiotis & Cui, Yueting & Gad, Samar & Kallinterakis, Vasileios, 2020. "Feedback trading and the ramadan effect in frontier markets," Research in International Business and Finance, Elsevier, vol. 51(C).
    2. Rakesh Kumar & Raj S. Dhankar, 2011. "Non Linearity and Heteroskedasticity Effect on Stock Returns Volatility," Global Business Review, International Management Institute, vol. 12(2), pages 319-329, June.
    3. Geoffrey M. Ngene & Catherine Anitha Manohar & Ivan F. Julio, 2020. "Overreaction in the REITs Market: New Evidence from Quantile Autoregression Approach," JRFM, MDPI, vol. 13(11), pages 1-28, November.
    4. Rakesh Kumar, 2017. "Examining the Dynamic and Non-linear Linkages between Crude Oil Price and Indian Stock Market Volatility," Global Business Review, International Management Institute, vol. 18(2), pages 388-401, April.
    5. Rakesh Kumar, 2016. "Integration of Stock Returns and Volatility of Emerging Equity Markets," Review of Market Integration, India Development Foundation, vol. 8(1-2), pages 79-102, April.
    6. Rakesh Kumar & Raj S. Dhankar, 2010. "Empirical Analysis of Conditional Heteroskedasticity in Time Series of Stock Returns and Asymmetric Effect on Volatility," Global Business Review, International Management Institute, vol. 11(1), pages 21-33, January.

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