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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Comparison of Value-at-Risk using regime-switching GARCH models for industrial metals futures |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 33 |
| Date | 2023 |
| Abstract Text | This thesis compares GARCH models, Stochastic Volatility (SV) models, and Markov-switching GARCH (MSGARCH) models in terms of forecasting one-day-ahead Value-at-Risk (VaR) for industrial metals futures. GARCH and MSGARCH models are estimated with three innovation distributions: normal, student-t, and generalized error distributions (GED). For in-sample analysis, we implement these models to compare the Akaike information criterion (AIC) as well as their in-sample conditional volatility. Out-of-sample VaR forecasting performance is evaluated based on conditional coverage test. The results show MSGARCH models outperform the other models in predicting a one-day-ahead VaR for both long and short trading positions. |
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