A cross-country comparison of Expected Shortfall estimation models
This dissertation aims to find a better-performed model in estimating risk measures for certain countries. The risk measures are estimated under five distributional assumptions (normal, Student-t, skewed Student-t, historical distribution, and generalized pareto) for five financial markets (Nasdaq,...
| Main Author: | |
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| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2017
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| Online Access: | https://eprints.nottingham.ac.uk/46082/ |
| _version_ | 1848797253897027584 |
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| author | Du, Cong |
| author_facet | Du, Cong |
| author_sort | Du, Cong |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This dissertation aims to find a better-performed model in estimating risk measures for certain countries. The risk measures are estimated under five distributional assumptions (normal, Student-t, skewed Student-t, historical distribution, and generalized pareto) for five financial markets (Nasdaq, FTSE100, SSEC, BVSP, and Nifty50), three estimation windows (250, 500, 1000), and two significance levels (0.05 and 0.01). A two-stage ES backtest and a direct ES test are conducted. The violation ratio comparisons and the backtesting results indicate that student’s t distribution and normal distribution have overall the best and the worst performance. Unconditional models also have its deficiencies in modeling financial markets. |
| first_indexed | 2025-11-14T20:00:57Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-46082 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:00:57Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-460822018-04-17T15:07:57Z https://eprints.nottingham.ac.uk/46082/ A cross-country comparison of Expected Shortfall estimation models Du, Cong This dissertation aims to find a better-performed model in estimating risk measures for certain countries. The risk measures are estimated under five distributional assumptions (normal, Student-t, skewed Student-t, historical distribution, and generalized pareto) for five financial markets (Nasdaq, FTSE100, SSEC, BVSP, and Nifty50), three estimation windows (250, 500, 1000), and two significance levels (0.05 and 0.01). A two-stage ES backtest and a direct ES test are conducted. The violation ratio comparisons and the backtesting results indicate that student’s t distribution and normal distribution have overall the best and the worst performance. Unconditional models also have its deficiencies in modeling financial markets. 2017-09-13 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/46082/1/A%20cross-country%20comparison%20of%20es%20final%20.pdf Du, Cong (2017) A cross-country comparison of Expected Shortfall estimation models. [Dissertation (University of Nottingham only)] |
| spellingShingle | Du, Cong A cross-country comparison of Expected Shortfall estimation models |
| title | A cross-country comparison of Expected Shortfall estimation models |
| title_full | A cross-country comparison of Expected Shortfall estimation models |
| title_fullStr | A cross-country comparison of Expected Shortfall estimation models |
| title_full_unstemmed | A cross-country comparison of Expected Shortfall estimation models |
| title_short | A cross-country comparison of Expected Shortfall estimation models |
| title_sort | cross-country comparison of expected shortfall estimation models |
| url | https://eprints.nottingham.ac.uk/46082/ |