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Extreme Value Theory and long-memory-GARCH Framework: Application to Stock Market

Manel Youssef (), Lotfi Belkacem and Khaled Mokni ()
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Manel Youssef: Research Laboratory for Economy, Management and Quantitative Finance (LaREMFiQ), IHEC, University of Sousse, B.P. 40 - Route de la ceinture - Sahloul III 4054 Sousse – Tunisia
Lotfi Belkacem: Research Laboratory for Economy, Management and Quantitative Finance (LaREMFiQ), IHEC, University of Sousse, B.P. 40 - Route de la ceinture - Sahloul III 4054 Sousse – Tunisia

International Journal of Economics and Empirical Research (IJEER), 2015, vol. 3, issue 8, 371-388

Abstract: Purpose: During last decades, financial markets have witnessed large losses due to their exposure to unexpected market crash. Resulting in these financial disasters, financial institutions, regulators and academics have developed intensive research to provide better measurement techniques and hedging tools. Value-at-Risk (VaR) is the most popular risk measure in the financial industry. Methodology: In this paper we evaluate the usefulness of Extreme Value Theory (EVT) to forecast VaR for same stock market. We use same long-range memory GARCH-type models (FIAGARCH, HYGARCH and FIAPARCH) and EVT to forecast the financial market risk. Findings: Results show that The FIAPARCH-EVT approach performs better in predicting the one day ahead VaRs for different series studied. Recommendations: Our findings reveals that models considering for some stylized facts such that volatility clustering, long range memory and leptokurtosis in the time series behavior, combined with filtering process such that EVT enhances the VaR predicting for both high and low confidence levels.

Keywords: Extreme Value Theory (EVT); Long range-memory; Value-at-risk (search for similar items in EconPapers)
JEL-codes: G11 G12 G15 (search for similar items in EconPapers)
Date: 2015
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