English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The paper contributes to the rare literature modeling term structure of crudeoil markets. We explain term structure of crude oil prices using dynamicNelson-Siegel model, and propose to forecast them with the generalizedregression framework based on neural networks. The newly proposed framework isempirically tested on 24 years of crude oil futures prices covering severalimportant recessions and crisis periods. We find 1-month, 3-month, 6-month and12-month-ahead forecasts obtained from focused time-delay neural network to besignificantly more accurate than forecasts from other benchmark models. Theproposed forecasting strategy produces the lowest errors across all times tomaturity.

Forecasting the term structure of crude oil futures prices with neural networks