Extreme Risk Forecast for Quantitative Financial Risk Management
| dc.contributor.author | Zhang, Lequn | |
| dc.contributor.supervisor | Honglei Xu | en_US |
| dc.contributor.supervisor | Song Wang | en_US |
| dc.contributor.supervisor | Quanxi Shao | en_US |
| dc.date.accessioned | 2022-09-27T01:39:10Z | |
| dc.date.available | 2022-09-27T01:39:10Z | |
| dc.date.issued | 2022 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/89362 | |
| dc.description.abstract |
Value at Risk (VaR) is one of the key risk measures for quantitative financial risk management. VaR measures extreme risk, which has a small probability but a significant consequence to financial institutions. This thesis develops methods based on an extended extreme value approach to improve the forecast skill of VaR. The proposed methods improve the forecasting accuracy, robustness, efficiency and outperform the existing methods in the literature. | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | Extreme Risk Forecast for Quantitative Financial Risk Management | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | PhD | en_US |
| curtin.department | School of Electrical Engineering, Computing and Mathematical Sciences | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Science and Engineering | en_US |
| curtin.contributor.orcid | Zhang, Lequn [0000-0001-8453-8454] | en_US |