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Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | A regime switching GARCH model with mixed frequency data and exogenous information |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 48 |
| Date | 2018 |
| Abstract Text | In this paper I conduct an autoregressive multivariate regression analysis of stock returns, which includes the use of lower sampled macroeconomic inputs. I apply the Mixed Data Sampling (MIDAS) weighting scheme to handle the frequency mismatch between the higher sampled daily return data and the lower sampled monthly macroeconomic variables, which allows me to parsimoniously weigh the higher frequency variable. This so-called reverse MIDAS approach is rather new to the literature, as typically mostly the inverse relationship is considered. I compare this model with multiple di erent vector autoregressive (VAR) con gurations. I nd that the Gaussian based approach with homoscedastic errors does not adequately model the underlying nancial data. Additionally, the inclusion of macroeconomic regressors does not increase the performance of the model. Increasing the lag length of the autoregressive component does not lead to an increased performance either. As a potentially more suitable con guration I outline the use of a non-Gaussian model that is based on a generalized hyperbolic distribution, which accounts for non-normality and GARCH e ects among others. |
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