Assessing the Power and Size of the Event Study Method Through the Decades

Aktas, Nihat; Bodt, Eric de; Cousin, Jean-Gabriel

doi:10.2139/ssrn.1018641

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Here, a growing literature attempts to explain the trend in idiosyncratic volatility first documented by Campbell, Lettau, Malkiel and Xu (2001), CLMX henceforth. Aktas, de Bodt and Cousin (2007) and Kothari and Warner (2004) study how this permanent increase affects the use of one of the most powerful empirical techniques in finance, the event study. Comin and Mulani (2006) examine how and why trends in the macro-economy seem to diverge from the "micro-trend".…”

Section: Introductionmentioning

confidence: 99%

Aggregate Idiosyncratic Volatility

Bekaert¹,

Hodrick²,

Zhang³

2012

J. Financ. Quant. Anal.

172

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We examine aggregate idiosyncratic volatility in 23 developed equity markets, measured using various methodologies. We find no evidence of upward trends after extending the sample to 2008. Instead, idiosyncratic volatility is well described by a stationary autoregressive process that occasionally switches into a higher-variance regime that has relatively short duration. We also document that idiosyncratic volatility is highly correlated across countries. Most of the time variation in idiosyncratic volatility can be attributed to variation in a growth opportunity proxy, total (U.S.) market volatility, and in most specifications, the variance premium, a business cycle sensitive risk indicator.

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Section: Introductionmentioning

confidence: 99%

Aggregate Idiosyncratic Volatility

Bekaert¹,

Hodrick²,

Zhang³

2012

J. Financ. Quant. Anal.

172

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“…Such a contaminated event is necessary to artificially induce variance in the estimation parameters of the market model. 6 Campbell et al (2001) document a noticeable increase in firm-level volatility relative to market volatility over the period from 1962 to 1997 which is associated with a decline in the explanatory power of the market model (see also empirical evidence in Aktas et al 2007b, as well as Arora et al 2009 for emerging markets). Kothari and Warner (2007) note that this is highly relevant on the implication behind the event study because it suggests a time-variation to the power of test statistics to detect abnormal performance for certain events.…”

Section: Methods and Experimental Designmentioning

confidence: 98%

Short-Horizon Event Study Estimation with a Star Model and Real Contaminated Events

2014

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“…A commonly used measure of average abnormal returns in the presence of heteroskedasticity is average standardized abnormal return (ASAR) (Strong, 1992;Atkas, N., E. de Bodt and J. Cousin, 2007),…”

Section: Py Y mentioning

confidence: 99%

Information asymmetry, market segmentation, and cross-listing: Implications for event study methodology

Reed

2013

Journal of Asian Economics

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This paper connects three subjects related to international financial markets-(i) information asymmetry, (ii) market segmentation, and (iii) cross-listings-and highlights their implication for event study methodology. When firms list equities on more than one exchange, and the exchanges are characterized by different information sets, a problem arises as to which exchange(s) to include in the event study sample. If market segmentation impedes the arbitrage of these multiple responses, then the use of a single listing (for a firm that is cross-listed) can yield abnormal return estimates that are biased. In such circumstances, using returns from all the markets in which a firm's securities are listed not only increases the sample size (often an important consideration when undertaking event studies in emerging markets), but also enables full-information abnormal return estimates to be obtained. What is required is a method that extracts the independent information from each listing while counting the common information only once. In this paper, we develop an estimation procedure that achieves these twin objectives. We then apply our approach to an event study of Chinese OMAs and compare results from alternative samples and estimators. We demonstrate that including return data from cross-listings of the same firm can result in substantially different conclusions.

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Assessing the Power and Size of the Event Study Method Through the Decades

Cited by 6 publications

References 28 publications

Aggregate Idiosyncratic Volatility

Aggregate Idiosyncratic Volatility

Short-Horizon Event Study Estimation with a Star Model and Real Contaminated Events

Information asymmetry, market segmentation, and cross-listing: Implications for event study methodology

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