Event-study analysis by using dynamic conditional score models

Szabolcs Blazsek and Luis Antonio Monteros

Applied Economics, 2017, vol. 49, issue 45, 4530-4541

Abstract: This article considers the most important information technology (IT) products in order to perform an event-study analysis of the out-of-sample predictability of IT stock returns. We define two subperiods, the estimation and forecast windows, for each IT product that are separated by the product release date. We investigate whether post-release-date returns can be predicted by using data on pre-release-date returns. We use static one-step-ahead density forecasting. We compare the forecast performance of autoregressive moving average (ARMA) plus generalized autoregressive conditional heteroscedasticity (GARCH) and quasi-ARMA (QARMA) plus Beta-$$t$$t -EGARCH (exponential-GARCH). QARMA plus Beta-$$t$$t -EGARCH belongs to the family of dynamic conditional score (DCS) models. We find that the in-sample statistical performance of DCS is superior to that of ARMA plus GARCH for most of the IT stocks. We also find that the out-of-sample density predictive performance of ARMA plus GARCH is never significantly superior to that of DCS. However, the predictive performance of DCS significantly dominates that of ARMA plus GARCH for several IT products. We undertake a Monte Carlo value-at-risk (VaR) application of our results to Windows 95 of Microsoft.

Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://hdl.handle.net/10.1080/00036846.2017.1284996 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:49:y:2017:i:45:p:4530-4541

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20

DOI: 10.1080/00036846.2017.1284996

Access Statistics for this article

Applied Economics is currently edited by Anita Phillips

More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().