One-day-ahead value-at-risk estimations with dual long-memory models: evidence from the Tunisian stock market

Samir Mabrouk and Chaker Aloui

International Journal of Financial Services Management, 2010, vol. 4, issue 2, 77-94

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.

Keywords: dual long-range memory; ARFIMA-FIGARCH; ARFIMA-FIAPARCH; skewed student innovations; value-at-risk; VaR estimations; Tunisia; Tunisian stock exchange. (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (9)

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