Hidden Markov models with threshold effects and their applications to oil price forecasting
By Dong-Mei Zhu, Wai Ki Ching, Robert J. R. Elliott, Tak Kuen Siu, Lianmin Zhang. 2016
21 citations recorded by the source corpus.
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 log-returns 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.
Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research
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Citations in the corpus
- Forecasting Crude Oil Prices: a Deep Learning based Model (2017, background): “[3] proposed a Hidden Markov Model (HMM) with threshol effect to model hidden factors influ ncing the crude oil price movement.”