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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Comparison of Statistical and Machine Learning Methods in Modelling Time-Varying Volatility |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2022 |
| Abstract Text | In this thesis we extend well established statistical models on time-varying volatil- ity of financial returns with promising machine learning techniques. We work with models from the GARCH family as baseline and update their recursive volatility functions via more complicated estimators from the modern machine learning literature. The introduced models are fitted in financial datasets via optimization with respect to likelihood-based functions and as a result the ex- tended models inherit all the distributional assumptions of the baseline ones. The modelling methodology is additionally expanded to the multidimensional case, where we estimate the conditional correlation of a portfolio of assets over time. We perform an extensive comparison of the proposed models with the bench- marking ones and conclude to a not significant outperformance of the challenger models. At the second part, we work in two applications in the area of Lombard Lending. Under the assumption of time-varying volatility we use the described methodology of the first part to provide (i) a risk monitoring tool and (ii) an estimation of the lending value of a Lombard loan. |
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