Pairwise Markov models for stock index forecasting. Gorynin, I., Monfrini, E., & Pieczynski, W. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 2041-2045, Aug, 2017.
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Paper doi abstract bibtex
Paper doi abstract bibtex Common well-known properties of time series of financial asset values include volatility clustering and asymmetric volatility phenomenon. Hidden Markov models (HMMs) have been proposed for modeling these characteristics, however, due to their simplicity, HMMs may lack two important features. We identify these features and propose modeling financial time series by recent Pairwise Markov models (PMMs) with a finite discrete state space. PMMs are extended versions of HMMs and allow a more flexible modeling. A real-world application example demonstrates substantial gains of PMMs compared to the HMMs.
@InProceedings{8081568,
author = {I. Gorynin and E. Monfrini and W. Pieczynski},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Pairwise Markov models for stock index forecasting},
year = {2017},
pages = {2041-2045},
abstract = {Common well-known properties of time series of financial asset values include volatility clustering and asymmetric volatility phenomenon. Hidden Markov models (HMMs) have been proposed for modeling these characteristics, however, due to their simplicity, HMMs may lack two important features. We identify these features and propose modeling financial time series by recent Pairwise Markov models (PMMs) with a finite discrete state space. PMMs are extended versions of HMMs and allow a more flexible modeling. A real-world application example demonstrates substantial gains of PMMs compared to the HMMs.},
keywords = {asset management;economic forecasting;financial management;hidden Markov models;stock markets;time series;Hidden Markov models;PMMs;finite discrete state space;flexible modeling;stock index forecasting;financial asset values;volatility clustering;asymmetric volatility phenomenon;financial time series;HMM;pairwise Markov models;Hidden Markov models;Markov processes;Forecasting;Time series analysis;Mathematical model;Probability distribution;Signal processing algorithms;Hidden Markov models;Forecasting;Financial time series;Pairwise Markov models;Technical analysis},
doi = {10.23919/EUSIPCO.2017.8081568},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347653.pdf},
}Downloads: 0
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