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

Arbeitspapier

The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested on 24 years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month, 3-month, 6-month and 12-month-ahead forecasts obtained from focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity.

Objekt anzeigen

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IES Working Paper ; No. 25/2015

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Neural Networks and Related Topics ; Financial Forecasting and Simulation
• Thema: term structure ; Nelson-Siegel model ; dynamic neural networks ; crude oil futures

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Baruník, Jozef ; Malinská, Barbora
• Ereignis: Veröffentlichung
• (wer): Charles University in Prague, Institute of Economic Studies (IES)
• (wo): Prague
• (wann): 2015

Weitere Informationen

• Handle: http://hdl.handle.net/10419/146381