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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Discrete Multivariate Gaussian Mixture GARCH Models for Financial Asset Allocation |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 59 |
| Date | 2022 |
| Abstract Text | This article proposes a weighted maximum likelihood approach for fitting DCC-GARCH with finite Gaussian mixtures. At its foundation, the method requires component separation to be conducted at an earlier step via either the conventional expectation-maximization algorithm for the MLE or the robust Minimum Covariance Determinant estimator. The overall compu-tational routine allows for parameter shrinkage via a quasi-Bayesian prior and can be further augmented with traditional time-based weighting schemes (e.g. hyperbolic) to account for model misspecificiation. It is shown in a backtesting exercise with DJIA data that portfolios built with this estimation approach consistently outperform their IID equivalents. Moreover, it is shown that the more complex DCC model doesn’t produce any improvements over the com-putationally faster CCC variant for a universe of 30 assets. By changing the assets considered for allocation along with the index composition over the years, the results also reveal the large influence that Survivorship Bias can exert on portfolio strategies in general if not accounted for when backtesting. |
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