Multiday expected shortfall under generalized t distributions: evidence from global stock market

Iqbal, Robina; Sorwar, Ghulam; Baker, Rose; Choudhry, Taufiq

Abstract

We apply seven alternative t-distributions to estimate the market risk measures Value at Risk (VaR) and its extension Expected Shortfall (ES). Of these seven, the twin t-distribution (TT) of Baker and Jackson (in Twin t distribution, University of Salford Manchester. https://arxiv.org/abs/1408.3237, 2014) and generalized asymmetric distribution (GAT) of Baker (in A new asymmetric generalization of the t-distribution, University of Salford Manchester. https://arxiv.org/abs/1606.05203, 2016) are applied for the first time to estimate market risk. We analytically estimate VaR and ES over 1-day horizon and extend this to multi-day horizon using Monte Carlo simulation. We find that taken together TT and GAT distributions provide the best back-testing results across individual confidence levels and horizons for majority of scenarios. Moreover, we find that with the lengthening of time horizon, TT and GAT models performs well, such that at the 10-day horizon, GAT provides the best back-testing results for all of the five indices and the TT model provides the second best results, irrespective period of study and confidence level.

Citation

Iqbal, R., Sorwar, G., Baker, R., & Choudhry, T. (2020). Multiday expected shortfall under generalized t distributions: evidence from global stock market. Review of Quantitative Finance and Accounting, 55(3), 803-825. https://doi.org/10.1007/s11156-019-00860-1

• Journal Article Type: Article
• Online Publication Date: Jan 14, 2020
• Publication Date: 2020-10
• Deposit Date: Jun 1, 2023
• Journal: Review of Quantitative Finance and Accounting
• Print ISSN: 0924-865X
• Electronic ISSN: 1573-7179
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 55
• Issue: 3
• Pages: 803-825
• DOI: https://doi.org/10.1007/s11156-019-00860-1
• Keywords: Finance; General Business, Management and Accounting; Accounting
• Additional Information: First Online: 14 January 2020