Forecasting Equity Index Volatility: Empirical Evidence from Japan, UK and USA Data

Divine N Obodoechi (), Anthony Orji () and Onyinye Anthony-Orji
Authors registered in the RePEc Author Service: Anthony Orji

Financial Risk and Management Reviews, 2018, vol. 4, issue 1, 1-23

Abstract: Using non-linear models to forecast volatility for three equity index samples, this study examines weekly returns of three indices; Dow Jones Industrial index, FTSE 100 index, and Nikkei 225 index. The sample covers a twenty year sample period. The study employs an in sample and out of sample volatility forecast using standard symmetric loss functions in order to identify an appropriate model that best forecast volatility. Using the mean error (ME), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), the study finds the EGARCH model to outperform the ARCH, and GARCH model in forecasting volatility.

Keywords: Equity market; Volatility; ARCH; GARCH; EGARCH (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
https://archive.conscientiabeam.com/index.php/89/article/view/1725/2394 (application/pdf)
https://archive.conscientiabeam.com/index.php/89/article/view/1725/4953 (text/html)

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:pkp:frmrev:v:4:y:2018:i:1:p:1-23:id:1725

Access Statistics for this article

More articles in Financial Risk and Management Reviews from Conscientia Beam
Bibliographic data for series maintained by Dim Michael ().