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Contribution Details

Type Master's Thesis
Scope Discipline-based scholarship
Title Inf ere nee Algorithms for Hidden (Semi) Markov Models
Organization Unit
Authors
  • Patrick Aschermayr
Supervisors
  • Marc Paolella
Language
  • English
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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