Parametric approaches to risk management for natural gas prices: an out-of-sample evaluation

2011 ◽  
Vol 4 (2) ◽  
pp. 43-66
Author(s):  
Paolo Zagaglia
Keyword(s):  
Risk Management ◽  
Natural Gas ◽  
Out Of Sample ◽  
Natural Gas Prices

scholarly journals Dependence and extreme dependence of crude oil and natural gas prices with applications to risk management

2014 ◽  
Vol 42 ◽  
pp. 332-342 ◽  
Author(s):  
Riadh Aloui ◽  
Mohamed Safouane Ben Aïssa ◽  
Shawkat Hammoudeh ◽  
Duc Khuong Nguyen
Keyword(s):  
Risk Management ◽  
Natural Gas ◽  
Crude Oil ◽  
Extreme Dependence ◽  
Natural Gas Prices ◽  
Oil And Natural Gas

Mexico and U.S. power systems under variations in natural gas prices

Energy Policy ◽  
2021 ◽  
Vol 156 ◽  
pp. 112378
Author(s):  
Luis Sarmiento ◽  
Anahi Molar-Cruz ◽  
Charalampos Avraam ◽  
Maxwell Brown ◽  
Juan Rosellón ◽  
...  
Keyword(s):  
Natural Gas ◽  
Power Systems ◽  
Natural Gas Prices

scholarly journals News-Based Research on Forecast of International Natural Gas Price Trend

2020 ◽  
Author(s):  
Tianxiang Li ◽  
Xiaosong Han ◽  
Aoqing Wang ◽  
Hui Li ◽  
Guosheng Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Natural Gas ◽  
Predictive Accuracy ◽  
Price Trend ◽  
Learning Network ◽  
Natural Gas Prices ◽  
Deep Learning Network ◽  
Sentiment Score ◽  
Gas Price

In this paper, we build a deep learning network to predict the trends of natural gas prices. Given a time series, for each day, the gas price trend is classified as “up” and “down” according to the price compared to the last day. Meanwhile, we collect news articles as experimental materials from some natural gas related websites. Every article was then embedded into vectors by word2vec, weighted with its sentiment score, and labeled with corresponding day’s price trend. A CNN and LSTM fused network was then trained to predict price trend by these news vectors. Finally, the model’s predictive accuracy reached 62.3%, which outperformed most of other traditional classifiers.

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