Title: Value-at-risk and expected shortfall: a dual long memory framework

Authors: Zouheir Mighri; Faysal Mansouri; Geoffrey J.D. Hewings

Addresses: Department of Quantitative Methods and Information Technology, Laboratoire de Recherche en Economie, Management et Finance Quantitative (LAREMFQ), Institut Supérieur de Gestion de Sousse, University of Sousse, Rue Abedelaziz El Bahi – B.P. No. 763 – 4000 Sousse, Tunisia ' Department of Quantitative Methods, Laboratoire de Recherche en Economie, Management et Finance Quantitative (LAREMFQ), Institut des Hautes Etudes Commerciales de Sousse, University of Sousse, Route Hzamia Sahloul 3 – B.P. No. 40 – 4054 Sousse, Tunisia ' University of Illinois, 220 Davenport Hall, 607 South Mathews Avenue, Urbana, Illinois, 61801-3671, USA

Abstract: In this article, we use the dual long memory properties to assess the value-at-risk and expected shortfall for the Argentinean stock market under both short and long daily trading positions. We attempt to show whether considering for long memory properties in both the returns and volatility, volatility asymmetry and fat-tails could provide more accurate value-at-risk's and expected shortfall's estimations. For this purpose, the joint ARFIMA-FIGARCH, ARFIMA-HYGARCH and ARFIMA-FIAPARCH models are applied to the MERVAL stock price index under normal, student-t and skewed student-t distributed innovations. We show that the skewed student-t-ARFIMA-FIAPARCH model performs better in predicting the in-sample and out-of-sample one-step ahead value-at-risk and expected shortfall for both short and long trading positions.

Keywords: value-at-risk; VaR; expected shortfall; dual long memory; Argentina; stock markets; volatility asymmetry; fat-tails.

DOI: 10.1504/GBER.2014.065364

Global Business and Economics Review, 2014 Vol.16 No.4, pp.416 - 451

Received: 22 Apr 2013
Accepted: 22 Jul 2013
Published online: 31 Oct 2014 *

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