Volatility modeling and value-at-risk (VaR) forecasting of emerging stock markets in the presence of long memory, asymmetry, and skewed heavy tails

Gaye Gencer, H. and Demiralay, S. ORCID: 0000-0003-2543-7914, 2016. Volatility modeling and value-at-risk (VaR) forecasting of emerging stock markets in the presence of long memory, asymmetry, and skewed heavy tails. Emerging Markets Finance and Trade, 52 (3), pp. 639-657. ISSN 1540-496X

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Abstract

In this article, we elaborate some empirical stylized facts of eight emerging stock markets for estimating one-day- and one-week-ahead Value-at-Risk (VaR) in the case of both short- and long-trading positions. We model the emerging equity market returns via APARCH, FIGARCH, and FIAPARCH models under Student-t and skewed Student-t innovations. The FIAPARCH models under skewed Student-t distribution provide the best fit for all the equity market returns. Furthermore, we model the daily and one-week-ahead market risks with the conditional volatilities generated from the FIAPARCH models and document that the skewed Student-t distribution yields the best results in predicting one-day-ahead VaR forecasts for all the stock markets. The results also reveal that the prediction power of the models deteriorate for longer forecasting horizons.

Item Type: Journal article
Publication Title: Emerging Markets Finance and Trade
Creators: Gaye Gencer, H. and Demiralay, S.
Publisher: Informa UK Limited
Date: 3 March 2016
Volume: 52
Number: 3
ISSN: 1540-496X
Identifiers:
NumberType
10.1080/1540496x.2014.998557DOI
1344778Other
Divisions: Schools > Nottingham Business School
Record created by: Laura Ward
Date Added: 14 May 2021 13:29
Last Modified: 14 May 2021 13:29
URI: http://irep.ntu.ac.uk/id/eprint/42865

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