Title: One-day-ahead value-at-risk estimations with dual long-memory models: evidence from the Tunisian stock market
Authors: Samir Mabrouk, Chaker Aloui
Addresses: International Finance Group Tunisia, High School of Business (ESCT), Manouba University, Campus Universitaire de la Manouba, Manouba, Tunisia. ' International Finance Group Tunisia, High Institute of Accounting and Business, Manouba University, Campus Universitaire de la Manouba, Manouba, Tunisia
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.
International Journal of Financial Services Management, 2010 Vol.4 No.2, pp.77 - 94
Published online: 02 Apr 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article