Causality effects in return volatility measures with random times

E. Renault, B.J.M. Werker

Research output: Contribution to journalArticleScientificpeer-review

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.
Original languageEnglish
Pages (from-to)272-279
JournalJournal of Econometrics
Volume160
Issue number1
DOIs
Publication statusPublished - 2011

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Causality
Return volatility
Ticks
Moment conditions
Stock price volatility
Structural approach
Median
Stock returns
Inference
Volatility forecasts
New York Stock Exchange

Cite this

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title = "Causality effects in return volatility measures with random times",
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.",
author = "E. Renault and B.J.M. Werker",
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Causality effects in return volatility measures with random times. / Renault, E.; Werker, B.J.M.

In: Journal of Econometrics, Vol. 160, No. 1, 2011, p. 272-279.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Causality effects in return volatility measures with random times

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AU - Werker, B.J.M.

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AB - 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.

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