TY - JOUR
T1 - Probability density forecasts for steam coal prices in China
T2 - The role of high-frequency factors
AU - Ding, Lili
AU - Zhao, Zhongchao
AU - Han, Meng
N1 - Funding Information:
This work is supported by China Scholarship Council (CSC); National Science Foundation of China ( 71973132 ); National Social Science Fund of China ( 19VHQ002 ); Taishan Scholar Program ( tsqn20161014 , ts201712014 ).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/4/1
Y1 - 2021/4/1
N2 - Abstract Coal plays a key role in China's economy as a dominant primary energy resource. In this paper, we provide probability density forecasts for weekly steam coal prices in China based on daily factors such as renewable energy source, Daqing oil, Japanese natural gas, Australia steam coal prices, coal mining industry index, A-share power sector index, A-share index, coal industry index, and temperature. The empirical results show that the influence of temperature lasts longer than other factors, while the Australia steam coal prices, renewable energy source and A-share index are the three best predictors for steam coal prices. It is also shown that the high-frequency factors are useful to forecast steam coal prices and that considering the nonlinearity of coal prices can improve the forecast accuracy by about 22%. We further provide the probability density forecasts for steam coal prices based on the influence of all the selected factors, the results suggest that our proposed method can provide accurate and satisfying probability density forecasts. Given these results, the policy-makers can make effective strategies which can not only adjust the energy structure but also ensure economic growth.
AB - Abstract Coal plays a key role in China's economy as a dominant primary energy resource. In this paper, we provide probability density forecasts for weekly steam coal prices in China based on daily factors such as renewable energy source, Daqing oil, Japanese natural gas, Australia steam coal prices, coal mining industry index, A-share power sector index, A-share index, coal industry index, and temperature. The empirical results show that the influence of temperature lasts longer than other factors, while the Australia steam coal prices, renewable energy source and A-share index are the three best predictors for steam coal prices. It is also shown that the high-frequency factors are useful to forecast steam coal prices and that considering the nonlinearity of coal prices can improve the forecast accuracy by about 22%. We further provide the probability density forecasts for steam coal prices based on the influence of all the selected factors, the results suggest that our proposed method can provide accurate and satisfying probability density forecasts. Given these results, the policy-makers can make effective strategies which can not only adjust the energy structure but also ensure economic growth.
KW - High-frequency factor
KW - MIDAS regression
KW - Probability density forecast
KW - Steam coal prices
KW - XGBoost
UR - http://www.scopus.com/inward/record.url?scp=85099389438&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2021.119758
DO - 10.1016/j.energy.2021.119758
M3 - Article
AN - SCOPUS:85099389438
SN - 0360-5442
VL - 220
JO - Energy
JF - Energy
M1 - 119758
ER -