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Type | Master's Thesis |
Scope | Discipline-based scholarship |
Title | Inf ere nee Algorithms for Hidden (Semi) Markov Models |
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Institution | University of Zurich |
Faculty | Faculty of Business, Economics and Informatics |
Number of Pages | 49 |
Date | 2018 |
Abstract Text | The goal of this thesis is to explore and test new methods to learn, describe and predict economic cycles. In particular, a comparison of inference algo-rithms for hidden Markov models (HMM) and hidden semi-Markov models (HSMM) is conducted. Both proposed approachesthe popular Expectation-maximization (EM) algorithm and a Markov chain Monte Carlo (MCMC) samplerhave advantages depending on the size and noise of the underlying data as well as whether interval estimation or the addition of data-specific knowledge is desired. Furthermore, HMMs and HSMMs are used to describe financial markets. While the hidden Markov model performs well as a trading tool, it is less suitable to model economic cycles, since its implicit geometric state duration distribution flips states unrealistically often. In contrast, the hidden semi-Markov regime switching model appears to be very promising, demonstrating high potential as a strategic asset allocation (SAA) overlay from a finance perspective and as a model for economic cycle predictions from an economics perspective. Keywords: Hidden Markov Models, Hidden Semi-Markov Models, Statistics, Machine Learning, Algorithms, EM-Algorithm, MCMC, Economic Cycles, Financial Markets, ETH Zurich, University of Zurich |
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