One-day-ahead value-at-risk estimations with dual long-memory models: evidence from the Tunisian stock market
by Samir Mabrouk, Chaker Aloui
International Journal of Financial Services Management (IJFSM), Vol. 4, No. 2, 2010

Abstract: In this paper, we assess the one-day-ahead Value-at-Risk (VaR) performance for the Tunisian Stock Market (TSE). Using the ARFIMA-FIGARCH and ARFIMA-FIAPARCH models under three alternative innovation distributions: normal, Student and skewed Student, we show that the ARFIMA-FIAPARCH with skewed Student innovations outperforms the other models since it jointly considers the asymmetry, long-range memory and fat-tails in the TSE return behaviour. This model provides the better results for in and out-of-sample VaR estimations for both short and long trading positions.

Online publication date: Fri, 02-Apr-2010

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