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Financial Risk and Management Reviews

June 2018, Volume 4, 1, pp 1-23

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

Divine N. Obodoechi

,

Anthony Orji

,

Onyinye I. Anthony-Orji

Divine N. Obodoechi 1 Anthony Orji 1 Onyinye I. Anthony-Orji 1 ,
;

  1. Department of Economics, University of Nigeria, Nsukka, Nigeria 1

Pages: 1-23

DOI: 10.18488/journal.89.2018.41.1.23

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Article History:

Received: 17 September, 2018
Revised: 22 October, 2018
Accepted: 27 November, 2018
Published: 20 December, 2018


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.
Contribution/ Originality
This is among the first studies that found EGARCH model to outperform the ARCH, and GARCH model in forecasting volatility using a combination of Japan, UK and US data.

Keywords:

Equity market, Volatility, ARCH, GARCH, EGARCH.

Reference:


Statistics:

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Funding:

This study received no specific financial support.

Competing Interests:

The authors declare that they have no competing interests.

Acknowledgement:

All authors contributed equally to the conception and design of the study.

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