TY - GEN
T1 - Pairwise markov models for stock index forecasting
AU - Gorynin, Ivan
AU - Monfrini, Emmanuel
AU - Pieczynski, Wojciech
N1 - Publisher Copyright:
© EURASIP 2017.
PY - 2017/10/23
Y1 - 2017/10/23
N2 - 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.
AB - 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.
KW - Financial Time Series
KW - Forecasting
KW - Hidden Markov Models
KW - Pairwise Markov Models
KW - Technical Analysis
UR - https://www.scopus.com/pages/publications/85038856589
U2 - 10.23919/EUSIPCO.2017.8081568
DO - 10.23919/EUSIPCO.2017.8081568
M3 - Conference contribution
AN - SCOPUS:85038856589
T3 - 25th European Signal Processing Conference, EUSIPCO 2017
SP - 2041
EP - 2045
BT - 25th European Signal Processing Conference, EUSIPCO 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th European Signal Processing Conference, EUSIPCO 2017
Y2 - 28 August 2017 through 2 September 2017
ER -