Hidden Markov models with threshold effects and their applications to oil price forecasting

Dong-Mei Zhu, Wai-Ki Ching, Robert J. Elliott, Tak Kuen Siu, Lianmin Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In this paper, we propose a Hidden Markov Model (HMM) which incorporates the threshold effect of the observation process. Simulated examples are given to show the accuracy of the estimated model parameters. We also give a detailed implementation of the model by using a dataset of crude oil price in the period 1986-2011. The prediction of crude oil spot price is an important and challenging issue for both government policy makers and industrial investors as most of the world's energy comes from the consumption of crude oil. However, many random events and human factors may lead the crude oil price to a strongly fluctuating and highly non-linear behavior. To capture these properties, we modulate the mean and the variance of logreturns of commodity prices by a finite-state Markov chain. The h-day ahead forecasts generated from our model are compared with regular HMM and the Autoregressive Moving Average model (ARMA). The results indicate that our proposed HMM with threshold effect outperforms the other models in terms of predicting ability.

Original languageEnglish
Pages (from-to)757-773
Number of pages17
JournalJournal of Industrial and Management Optimization
Issue number2
Publication statusPublished - 1 Apr 2017


  • Filtering
  • Forecasting
  • Hidden Markov Model
  • Oil price
  • Threshold effect


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