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Causality effects in return volatility measures with random times

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  • Renault, Eric
  • Werker, Bas J.M.

Abstract

We provide a structural approach to identify instantaneous causality effects between durations and stock price volatility. So far, in the literature, instantaneous causality effects have either been excluded or cannot be identified separately from Granger type causality effects. By giving explicit moment conditions for observed returns over (random) duration intervals, we are able to identify an instantaneous causality effect. The documented causality effect has significant impact on inference for tick-by-tick data. We find that instantaneous volatility forecasts for, e.g., IBM stock returns must be decreased by as much as 40% when not having seen the next quote change before its (conditionally) median time. Also, instantaneous volatilities are found to be much higher than indicated by standard volatility assessment procedures using tick-by-tick data. For IBM, a naive assessment of spot volatility based on observed returns between quote changes would only account for 60% of the actual volatility. For less liquidly traded stocks at NYSE this effect is even stronger.

Suggested Citation

  • Renault, Eric & Werker, Bas J.M., 2011. "Causality effects in return volatility measures with random times," Journal of Econometrics, Elsevier, vol. 160(1), pages 272-279, January.
  • Handle: RePEc:eee:econom:v:160:y:2011:i:1:p:272-279
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    Cited by:

    1. Großmaß Lidan, 2014. "Liquidity and the Value at Risk," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 234(5), pages 572-602, October.
    2. Yogo Purwono & Irwan Adi Ekaputra & Zaäfri Ananto Husodo, 2018. "Estimation of Dynamic Mixed Hitting Time Model Using Characteristic Function Based Moments," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 295-321, February.
    3. Aldrich, Eric M. & Heckenbach, Indra & Laughlin, Gregory, 2016. "A compound duration model for high-frequency asset returns," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 105-128.
    4. Li, Yingying & Zhang, Zhiyuan & Zheng, Xinghua, 2013. "Volatility inference in the presence of both endogenous time and microstructure noise," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2696-2727.
    5. Filip Žikeš & Jozef Baruník & Nikhil Shenai, 2017. "Modeling and forecasting persistent financial durations," Econometric Reviews, Taylor & Francis Journals, vol. 36(10), pages 1081-1110, November.
    6. Jacod, Jean & Mykland, Per A., 2015. "Microstructure noise in the continuous case: Approximate efficiency of the adaptive pre-averaging method," Stochastic Processes and their Applications, Elsevier, vol. 125(8), pages 2910-2936.
    7. Barndorff-Nielsen, Ole E. & Hansen, Peter Reinhard & Lunde, Asger & Shephard, Neil, 2011. "Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading," Journal of Econometrics, Elsevier, vol. 162(2), pages 149-169, June.
    8. Yuta Koike, 2017. "Time endogeneity and an optimal weight function in pre-averaging covariance estimation," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 15-56, April.
    9. Wei Wei & Denis Pelletier, 2015. "A Jump-Diffusion Model with Stochastic Volatility and Durations," CREATES Research Papers 2015-34, Department of Economics and Business Economics, Aarhus University.
    10. Renault, Eric & van der Heijden, Thijs & Werker, Bas J.M., 2014. "The dynamic mixed hitting-time model for multiple transaction prices and times," Journal of Econometrics, Elsevier, vol. 180(2), pages 233-250.
    11. Dimitrakopoulos, Stefanos & Tsionas, Mike G. & Aknouche, Abdelhakim, 2020. "Ordinal-response models for irregularly spaced transactions: A forecasting exercise," MPRA Paper 103250, University Library of Munich, Germany, revised 01 Oct 2020.

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