Coal Price Forecasting and Structural Analysis in China

Abstract

Coal plays an important role in China’s energy structure and its price has been continuously decreasing since the second half of 2012. Constant low price of coal affected the profits of coal enterprises and the coal use of its downstream firms; the precision of coal price provides a reference for these enterprises making their management strategy. Based on the historical data of coal price and related factors such as port stocks, sales volume, futures prices, Producer Price Index (PPI), and crude oil price rate from November 2013 to June 2016, this study aims to forecast coal price using vector autoregression (VAR) model and portray the dynamic correlations between coal price and variables by the impulse response function and variance decomposition. Comparing predicted and actual values, the root mean square error (RMSE) was small which indicated good precision of this model. Thus this short period prediction can help these enterprises make the right business decisions.

Probability density forecasts for steam coal prices in China: The role of high-frequency factors

“…These findings provide evidence that we should consider relevant drivers when forecasting steam coal prices instead of only considering coal prices itself, e.g. the research of Guo et al [36]. Yet, the related studies are very limited.…”

“…Besides, only very few studies consider the influence of related drivers when forecasting coal prices. Guo et al [36] apply vector autoregression model to forecast coal prices by considering historical coal prices and some related factors such as port stocks, futures prices, sales volume, crude oil price, and producer price index.…”

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