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

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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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Official URL: https://doi.org/10.1080/1540496x.2014.998557

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:

Number: