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: https://orcid.org/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:	Number Type 10.1080/1540496x.2014.998557 DOI 1344778 Other
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:	https://irep.ntu.ac.uk/id/eprint/42865

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