Abstract
A commonly used stochastic model for derivative and commodity market analysis is the Barndorff-Nielsen and Shephard (BN–S) model. Though this model is very efficient and analytically tractable, it suffers from the absence of long range dependence and many other issues. For this paper, the analysis is restricted to crude oil price dynamics. A simple way of improving the BN–S model with the implementation of various machine learning algorithms is proposed. This refined BN–S model is more efficient and has fewer parameters than other models which are used in practice as improvements of the BN–S model. The procedure and the model show the application of data science for extracting a “deterministic component” out of processes that are usually considered to be completely stochastic. Empirical applications validate the efficacy of the proposed model for long range dependence.
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References
Abdullah SN, Zeng X (2010) Machine learning approach for crude oil price prediction with artificial neural networks-quantitative (ANN-Q) model. In: The 2010 international joint conference on neural networks (IJCNN). https://doi.org/10.1109/IJCNN.2010.5596602
Arriojas M, Hu Y, Mohammed S-E, Pap G (2007) A delayed black and scholes formula. Stoch Anal Appl 25:471–492
Barndorff-Nielsen OE (2001) Superposition of Ornstein–Uhlenbeck type processes. Theory Probab Appl 45:175–194
Bernard V, Thomas J (1989) Post-earnings-announcement drift: delayed price response or risk premium? J Account Res 27:1–36
Barndorff-Nielsen OE, Shephard N (2001) Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics. J R Stat Soc Ser B Stat Methodol 63:167–241
Barndorff-Nielsen OE, Shephard N (2001) Modelling by Lévy processes for financial econometrics. In: Barndorff-Nielsen OE, Mikosch T, Resnick S (eds) Lévy processes: theory and applications. Birkhäuser, Budapest, pp 283–318
Barndorff-Nielsen OE, Jensen JL, Sørensen M (1998) Some stationary processes in discrete and continuous time. Adv Appl Probab 30:989–1007
Booth G, Kallunki J, Martikainen T (1997) Delayed price response to the announcements of earnings and its components in Finland. Eur Account Rev 6:377–392
Brown I, Funk J, Sircar R (2017) Oil prices and dynamic games under stochastic demand. https://ssrn.com/abstract=3047390 or https://doi.org/10.2139/ssrn.3047390
Chan P, Sircar R (2017) Fracking, renewables, and mean field games. SIAM Rev 59(3):588–615
Chen Y, Kaijian H, Tso GKF (2017) Forecasting crude oil prices: a deep learning based model. Procedia Comput Sci 122:300–307
Frey G, Manera M, Markandya A, Scarpa E (2009) Econometric models for oil price forecasting: a critical survey. In: CESifo Forum, ifo Institute—Leibniz Institute for Economic Research at the University of Munich, vol 10(1), pp 29–44
Grinblatt M, Keloharju M (2001) What makes investors trade? J Finance 56:589–616
He XJ (2018) Crude oil prices forecasting: time series vs. SVR models. J Int Technol Inf Manag 27(2):25–42
Habtemicael S, Ghebremichael M, SenGupta I (2019) Volatility and variance swap using superposition of the Barndorff-Nielsen and Shephard type Lévy processes. Sankhya B. https://doi.org/10.1007/s13571-017-0145-y (to appear)
Issaka A, SenGupta I (2017) Analysis of variance based instruments for Ornstein–Uhlenbeck type models: swap and price index. Ann Finance 13(4):401–434
Issaka A, SenGupta I (2017) Feynman path integrals and asymptotic expansions for transition probability densities of some Lévy driven financial markets. J Appl Math Comput 54:159–182
Jiang J, Tian W (2018) Semi-nonparametric approximation and index options. Ann Finance. https://doi.org/10.1007/s10436-018-0341-4 (in press)
Kulkarni KS, Sabarwal T (2017) To what extent are investment bank-differentiating factors relevant for firms floating moderate-sized IPOs? Ann Finance 3(3):297–327
Li X, Shang W, Wang S (2019) Text-based crude oil price forecasting: a deep learning approach. Int J Forecast 35(4):1548–1560
Pasiouras F, Gaganis C, Doumpos M (2007) A multicriteria discrimination approach for the credit rating of Asian banks. Ann Finance 3(3):351–367
Roberts M, SenGupta I (2020) Infinitesimal generators for two-dimensional Lévy process-driven hypothesis testing. Ann Finance 16(1): 121–139
SenGupta I (2016) Generalized BN–S stochastic volatility model for option pricing. Int J Theor Appl Finance 19(02):1650014
SenGupta I, Wilson W, Nganje W (2019) Barndorff-Nielsen and Shephard model: oil hedging with variance swap and option. Math Financ Econ 13(2):209–226
Sensoy A, Hacihasanoglu E (2014) Time-varying long range dependence in energy futures markets. Energy Econ 46(C):318–327
Tabak BM, Cajueiro DO (2007) Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility. Energy Econ 29(1):28–38
Wilson W, Nganje W, Gebresilasie S, SenGupta I (2019) Barndorff-Nielsen and Shephard model for hedging energy with quantity risk. High Freq 2(3–4):202–214
Zhao Y, Li J, Yu L (2017) A deep learning ensemble approach for crude oil price forecasting. Energy Econ 66(C):9–16
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The authors would like to thank the anonymous reviewers for their careful reading of the manuscript and for suggesting points to improve the quality of the paper.
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SenGupta, I., Nganje, W. & Hanson, E. Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning. Ann. Data. Sci. 8, 39–55 (2021). https://doi.org/10.1007/s40745-020-00256-2
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DOI: https://doi.org/10.1007/s40745-020-00256-2