Kombinování predikcí mnohorozměrné volatility v úloze optimalizace portfolia

Šípka, Stanislav

The selection of the best-performing model is always a challenge when solving financial-economic problems. The final model might prove to be suboptimal even after a short time if the economic climate changes suddenly. This thesis aims to construct a final model capable of estimating large-scale covariance matrices via the utilization of time-varying weights. A set of multivariate GARCH mod- els to be used as an input in the final combined estimate is used to introduce a weighting scheme based on the metrics of risk-adjusted return of the individ- ual model portfolios. As large-scale modeling often faces problems connected with the underlying dimensionality, the composite likelihood approach to model parameter estimation is proposed as a solution and compared to the standard maximum likelihood and its SVD modification. The resulting weighted covari- ance matrix prediction is used to construct optimal portfolios and their properties are compared in an empirical study. The thesis is concluded by noting the real-life limitation and possible improvements of the defined investing methodology. 1

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