Research on the Forecast of Thermal Coal Price based on ARMA-GARCH

Wangping Yu ； Yanhua Huang ； Mingdong Han ； Ranran Fang

《Frontiers in Economics and Management》 2卷12期 (2021/12) Pp. 128-134

https://doi.org/10.6981/FEM.202112_2(12).0016

摘要

This paper takes the thermal coal at Qinhuangdao Port as the research object, collects the daily price time series data of the thermal coal, performs stationarity test and first-order difference on the series, obtains the stable daily thermal coal price time series after the difference. Autocorrelation graph and partial autocorrelation graph are distinguished. By introducing the arithmetic average of AIC and BIC as the main basis for determining the model parameters, the ARMA(7,4) model is finally determined to fit the sequence. Secondly, in view of the ＂volatility aggregation＂ in the time series diagram, the LM test is used to determine that the residual square is not a white noise sequence, and it is concluded that the sequence has a GRACH effect. Finally, we revised the ARMA model based on the GRACH model, determined that GARCH(2,2)-ARMA(7,4) was the model with the highest degree of fit, and used this model to extrapolate the fitting results, and found that the price of thermal coal was After that, the volatility will gradually become smaller, prices will stabilize, and market supply and demand will gradually return to balance.

關鍵字

Time Series ； ARMA Model ； GARCH Effect ； AIC ； BIC

參考文獻

T.G.Zhang, D.Z.Zhao: Study on the Influencing Factors of Coal Price’Fluctuation in China[J].Energy of China , 2018,40(03):16-21.

S.J.Zhao: Research on Coal Price Prediction Based on Fitting Curve[J].Hebei Enterprise, 2020(08): 53-54.

J.B.Xia: Coal Price Prediction Model Based on BP Neural Network[J].Digital Space,2020(08):192-193.

J.h.Sun,J.P.Peng:Application of Temporal Sequence Analysis Technique in Coal Price Prediction[J]. Contemporary Economic Management,2000(03):33-40.

J.L.Guo,L.Cheng,B.C.Sun,R.Yan:Study on Prediction and Verification of Coal Prices Based on ARIMA-SVM[J].Coal Economic Research,2016,36(02):6-10.