Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Portfolio Value at Risk Forecasting with GARCH-Type Models |
| Organization Unit | |
| Authors |
|
| Supervisors |
|
| Language |
|
| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 58 |
| Date | 2023 |
| Abstract Text | This thesis examines the value at risk (VaR) forecasting ability of various univariate and multivariate models for a long equity portfolio. All of the considered models involve a generalized autoregressive conditional heteroskedasticity (GARCH)-type structure. The resulting forecasts are checked for desirable properties using violation-based backtests and compared in terms of predictive ability. We find that the VaR forecasts of almost all univariate models are inadequate, while the multivariate models have few problems passing these backtests. However, we do not find evidence that the multivariate models systematically outperform their univariate counterparts with regards to predictive accuracy, or vice versa. |
| PDF File |
Download
|
| Export |
BibTeX
|