Forecasting rate of return after extreme values when using AR-t-GARCH and QAR-Beta-t-EGARCH

Szabolcs Blazsek, Daniela Carrizo, Ricardo Eskildsen and Humberto Gonzalez

Finance Research Letters, 2018, vol. 24, issue C, 193-198

Abstract: We compare the predictive performances of AR-t-GARCH and recent QAR-Beta-t-EGARCH models. We compare predictive performances for those days when an extreme value is observed, and also for the trading day after each day when an extreme value is observed. We use a historical dataset from the adjusted Dow Jones Industrial Average (DJIA) index. We assume that the forecast users of this study are DJIA options investors. We find that AR-t-GARCH dominates QAR-Beta-t-EGARCH on each day when an extreme value is observed, and QAR-Beta-t-EGARCH dominates AR-t-GARCH on the trading day after each day when an extreme value is observed.

Keywords: Dow Jones Industrial Average (DJIA); Beta-t-EGARCH; Extreme values (search for similar items in EconPapers)
JEL-codes: C22 C52 C58 G13 G17 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612317303598
Full text for ScienceDirect subscribers only

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:eee:finlet:v:24:y:2018:i:c:p:193-198

DOI: 10.1016/j.frl.2017.09.006

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().