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Crude oil markets

  

 

As fossil fuels remain in use, the research work took them into consideration in the economic models to advance the understanding of oil markets.

Researchers analysed the term structure of crude oil markets by using the innovative method of neural networks. The dynamic Nelson-Siegel model has been selected to explain the term structure of crude oil prices. And a generalised regression framework, which is based on neural networks, has been developed to forecast oil prices.

The newly proposed framework has been tested on twenty-four years of crude oil futures prices, which cover several important recessions and crisis periods. The forecasts of 1, 3, 6, and 12 month-ahead that have been obtained from a focused time-delay neural network are significantly more accurate than forecasts from other benchmark models. This forecasting strategy produces the lowest errors at all times to maturity.

 

Read further

Baruník, Jozef and Barbora Malinská (2016). "Forecasting the term structure of crude oil futures prices with neural networks." Applied Energy, 164: 366-379.