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

Robina Iqbal (), Ghulam Sorwar (), Rose Baker () and Taufiq Choudhry ()
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Robina Iqbal: University of Salford
Ghulam Sorwar: University of Salford
Rose Baker: University of Salford
Taufiq Choudhry: University of Southampton

Review of Quantitative Finance and Accounting, 2020, vol. 55, issue 3, No 1, 803-825

Abstract: 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.

Keywords: Generalize t distribution; Asymmetric t distribution; Expected shortfall; EGARCH models; Multi-days ahead expected shortfall (search for similar items in EconPapers)
JEL-codes: C13 C15 C51 C52 C53 C58 G17 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11156-019-00860-1

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