A Higher-order interactive hidden Markov model and its applications

Dong-Mei Zhu, Wai-Ki Ching, Robert J. Elliott, Tak Kuen Siu and Lianmin Zhang ()
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Dong-Mei Zhu: Southeast University
Wai-Ki Ching: The University of Hong Kong
Robert J. Elliott: University of Adelaide
Lianmin Zhang: Nanjing University

OR Spectrum: Quantitative Approaches in Management, 2017, vol. 39, issue 4, No 6, 1055-1069

Abstract: Abstract In this paper, we propose a higher-order interactive hidden Markov model, which incorporates both the feedback effects of observable states on hidden states and their mutual long-term dependence. The key idea of this model is to assume the probability laws governing both the observable and hidden states can be written as a pair of higher-order stochastic difference equations. We also present an efficient procedure, a heuristic algorithm, to estimate the hidden states of the chain and the model parameters. Real applications in SSE Composite Index data and default data are given to demonstrate the effectiveness of our proposed model and corresponding estimation method.

Keywords: Interactive hidden Markov model; Hidden Markov model; Feedback effect; Stochastic difference equations (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00291-017-0484-0

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