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

Applied Energy

Volumn 164, Issue , 2016, Pages 366-379

  Baruník, Jozef ^a,b   Malinská, Barbora ^a  

^a CHARLES UNIVERSITY   (Czech Republic)

^b INSTITUTE OF INFORMATION THEORY AND AUTOMATION   (Czech Republic)

Author keywords

Crude oil futures; Dynamic neural networks; Nelson Siegel model; Term structure

Indexed keywords

COSTS; ELECTRONIC TRADING; FINANCIAL MARKETS; FORECASTING; NEURAL NETWORKS; TIME DELAY;

CRUDE OIL FUTURES; CRUDE OIL MARKET; CRUDE OIL PRICES; DYNAMIC NEURAL NETWORKS; FOCUSED TIME-DELAY NEURAL NETWORKS; GENERALIZED REGRESSION; NELSON-SIEGEL MODELS; TERM STRUCTURE;

CRUDE OIL;

ARTIFICIAL NEURAL NETWORK; CRUDE OIL; FORECASTING METHOD; MARKET CONDITIONS; NUMERICAL MODEL; PRICING POLICY; REGRESSION ANALYSIS;

EID: 84951017088     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2015.11.051     Document Type: Article

References (54)

1. Hamilton JD. Causes and consequences of the oil shock of 2007-08. Technical report, National Bureau of Economic Research; 2009.
2. Pan H., Haidar I., Kulkarni S. Daily prediction of short-term trends of crude oil prices using neural networks exploiting multimarket dynamics. Front Comput Sci China 2009, 3(2):177-191.
3. Büyüksahin B., Harris J.H. Do speculators drive crude oil futures prices?. Energy J 2011, 32(2):167-202.
4. Lautier D. Term structure models of commodity prices: a review. Technical report, CEREG; 2005.
5. Zhang Y.-J. Speculative trading and WTI crude oil futures price movement: an empirical analysis. Appl Energy 2013, 107:394-402.
6. Narayan P.K., Narayan S., Zheng X. Gold and oil futures markets: are markets efficient?. Appl Energy 2010, 87(10):3299-3303.
7. Narayan P.K., Narayan S., Popp S. Investigating price clustering in the oil futures market. Appl Energy 2011, 88(1):397-402.
8. 2 futures market. Appl Energy 2013, 112:1544-1547.
9. Brennan M.J., Schwartz E.S. Evaluating natural resource investments. J Bus 1985, 58(2):135-157.
10. Schwartz E.S. The stochastic behavior of commodity prices: implications for valuation and hedging. J Finance 1997, 52(3):923-973.
11. Gabillon J. The term structures of oil futures prices. Oxford Institute for Energy Studies. Working paper; 1991.
12. Cortazar G., Schwartz E.S. Implementing a stochastic model for oil futures prices. Energy Econ. 2003, 25(3):215-238.
13. Diebold F.X., Li C. Forecasting the term structure of government bond yields. J Economet 2006, 130(2):337-364.
14. Karstanje D, van der Wel M, van Dijk D. Common factors in commodity futures curves; 2015.
15. Nomikos N.K., Pouliasis P.K. Petroleum term structure dynamics and the role of regimes. J Futures Markets 2015, 35(2):163-185.
16. Almansour A. Convenience yield in commodity price modeling: a regime switching approach. Energy Econ 2014.
17. Heidorn T., Mokinski F., Rühl C., Schmaltz C. The impact of fundamental and financial traders on the term structure of oil. Energy Econ 2015, 48:276-287.
18. Grønborg N.S., Lunde A. Analyzing oil futures with a dynamic Nelson-Siegel model. J Futures Markets 2015.
19. Haykin S. Neural networks: a comprehensive foundation 2007, Prentice Hall, Englewood Cliffs, NJ.
20. Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Netw 1989, 2(5):359-366.
21. Siami-Irdemoosa E., Dindarloo S.R. Prediction of fuel consumption of mining dump trucks: a neural networks approach. Appl Energy 2015, 151:77-84.
22. Kelesa D., Scelleb J., Paraschivc F., Fichtnera W. Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks (ann). Appl Energy 2016, 162:218-230.
23. Kialashaki A., Reisel J.R. Modeling of the energy demand of the residential sector in the united states using regression models and artificial neural networks. Appl Energy 2013, 108:271-280.
24. An H., Gao X., Fang W., Ding Y., Zhong W. Research on patterns in the fluctuation of the co-movement between crude oil futures and spot prices: a complex network approach. Appl Energy 2014, 136(5):1067-1075.
25. Fan Y., Liang Q., Wei Y.-M. A generalized pattern matching approach for multi-step prediction of crude oil price. Energy Econ 2008, 30(3):889-904.
26. Yu L., Wang S., Lai K.K. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Econ 2008, 30(5):2623-2635.
27. Xiong T., Bao Y., Hu Z. Beyond one-step-ahead forecasting: evaluation of alternative multi-step-ahead forecasting models for crude oil prices. Energy Econ 2013, 40:405-415.
28. Jammazi R., Aloui C. Crude oil price forecasting: experimental evidence from wavelet decomposition and neural network modeling. Energy Econ 2012, 34(3):828-841.
29. Papadimitriou T., Gogas P., Stathakis E. Forecasting energy markets using support vector machines. Energy Econ. 2014.
30. Baruník J, Křehlík T. Coupling high-frequency data with nonlinear models in multiple-step-ahead forecasting of energy markets' volatility. Available at SSRN 2429487; 2014.
31. Chiroma H., Abdulkareem S., Herawan T. Evolutionary neural network model for west texas intermediate crude oil price prediction. Appl Energy 2015, 142:266-273.
32. Wang L., An H., Liu X., Huang X. Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach. Appl Energy 2015.
33. Baumeister C., Guérin P., Kilian L. Do high-frequency financial data help forecast oil prices? The MIDAS touch at work. Int J Forecast 2015, 31(2):238-252.
34. Holton G.A. Value-at-risk: theory and practice 2003, Academic Press.
35. Hagan P.S., West G. Interpolation methods for curve construction. Appl Math Finance 2006, 13(2):89-129.
36. Kilian L., Murphy D.P. The role of inventories and speculative trading in the global market for crude oil. J Appl Economet 2014, 29(3):454-478.
37. Mahadeva B.F., Kilian L., et al. The role of speculation in oil markets: what have we learned so far?. Energy J 2013, 34(3).
38. Hotelling H. The economics of exhaustible resources. J Polit Econ 1931, 137-175.
39. Litzenberger R.H., Rabinowitz N. Backwardation in oil futures markets: theory and empirical evidence. J Finance 1995, 50(5):1517-1545.
40. Haubrich J.G., Higgins P., Miller J. Oil prices: backward to the future 2004, Federal Reserve Bank of Cleveland, December.
41. Nelson C.R., Siegel A.F. Parsimonious modeling of yield curves. J Bus 1987, 60(4):473-489.
42. Steeley J.M. Testing term structure estimation methods: evidence from the uk strips market. J Money Credit Bank 2008, 40(7):1489-1512.
43. Sarker R.A., Kamruzzaman J., Begg R. Artificial neural networks in finance and manufacturing 2006, IGI Global.
44. Bliss RR. Testing term structure estimation methods. Technical report, Federal Reserve Bank of Atlanta; 1996.
45. Duffie D., Kan R. A yield-factor model of interest rates. Math Finance 1996, 6(4):379-406.
46. Vela D. Forecasting latin-american yield curves: an artificial neural network approach. Borradores de Economia 2013, 761. Banco de la Republica de Colombia.
47. Clouse D.S., Giles C.L., Horne B.G., Cottrell G.W. Time-delay neural networks: representation and induction of finite-state machines. IEEE Trans Neural Netw 1997, 8(5):1065-1070.
48. Nomikos N.K., Pouliasis P.K. Forecasting petroleum futures markets volatility: the role of regimes and market conditions. Energy Econ 2011, 33(2):321-337.
49. Wang Y., Wu C. Forecasting energy market volatility using Garch models: can multivariate models beat univariate models?. Energy Econ 2012, 34(6):2167-2181.
50. Brailsford T.J., Faff R.W. An evaluation of volatility forecasting techniques. J Bank Finance 1996, 20(3):419-438.
51. Diebold F.X., Mariano R.S. Comparing predictive accuracy. J Bus Econ Stat 2002, 20(1):134-144.
52. White H. A reality check for data snooping. Econometrica 2000, 1097-1126.
53. Hansen P.R. A test for superior predictive ability. J Bus Econ Stat 2005, 23(4).
54. Hansen P.R., Lunde A., Nason J.M. The model confidence set. Econometrica 2011, 79(2):453-497.