Research ArticleOpen AccessGoogle Scholar indexed
Financial Time Series Modelling of Trends and Patterns in the Energy Markets
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
School of Mathematics, University of Nairobi, Nairobi, Kenya
School of Mathematics, University of Nairobi, Nairobi, Kenya
School of Mathematics, University of Nairobi, Nairobi, Kenya
- 1 Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
- 2 School of Mathematics, University of Nairobi, Nairobi, Kenya
- 3 School of Mathematics, University of Nairobi, Nairobi, Kenya
- 4 School of Mathematics, University of Nairobi, Nairobi, Kenya
Journal of Mathematical Finance·Volume 06 (2016)·Pages 324–337·Published 9 March 2016·DOI10.4236/jmf.2016.62027
Copy link · social · email
Abstract
Precise recognition of a time series path is important to policy makers, statisticians, economists, traders, hedgers and speculators alike. The correct time series path is also a key ingredient in pricing models. This study uses daily futures prices of crude oil and other distillate fuels. This paper considers the statistical properties of energy futures and spot prices and investigates the trends that underlie the price dynamics in order to gain further insights into possible nuances of price discovery and energy market dynamics. The family of ARMA-GARCH models was explored. The trends depict time varying variability and persistence of oil price shocks. The return series conform to a constant mean model with GARCH variance.
KeywordsFinancial Time SeriesTrends and Patterns in Energy MarketsFutures and Spot PricesARCH EffectsARMA-GARCH Models
- Dorsman, A., Simpson, J.L. and Westerman, W. (2013) Energy Economics and Financial Markets. Springer, Berlin. http://dx.doi.org/10.1007/978-3-642-30601-3
- Ramos, S.B. and Veiga, H. (2014) The Interrelationship between Financial and Energy Markets. Springer, Berlin. http://dx.doi.org/10.1007/978-3-642-55382-0
- Liu, H.P., Erdem, E. and Shi, J. (2011) Comprehensive Evaluation of ARMA-GARCH (-M) Approaches for Modeling the Mean and Volatility of Wind Speed. Applied Energy, 88, 724-732. http://dx.doi.org/10.1016/j.apenergy.2010.09.028
- Beck, A., Shin, Y., Kim, A., Rachev, S., Feindt, M. and Fabozzi, F. (2013) Empirical Analysis of ARMA-GARCH Models in Market Risk Estimation on High-Frequency US Data. Studies in Nonlinear Dynamics and Econometrics, 17, 167-177. http://dx.doi.org/10.1515/snde-2012-0033
- Chinn, M.D., LeBlanc, M. and Coibion, O. (2005) The Predictive Content of Energy Futures: An Update on Petroleum, Natural Gas, Heating Oil and Gasoline. National Bureau of Economic Research, Cambridge.
- Alvarez-Ramirez, J., Rodriguez, E., Martina, E. and Ibarra-Valdez, C. (2012) Cyclical Behavior of Crude Oil Markets and Economic Recessions in the Period 1986-2010. Technological Forecasting and Social Change, 79, 47-58. http://dx.doi.org/10.1016/j.techfore.2011.04.010
- Cont, R. (2001) Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues. Quantitative Finance, 1, 223-236. http://dx.doi.org/10.1080/713665670
- Tsay, R.S. (2010) Analysis of Financial Time Series. 3rd Edition, John Wiley & Sons, Inc., Hoboken.
- Mandelbrot, B. (1963) The Variation of Certain Speculative Prices. The Journal of Business, 36, 394-419. http://dx.doi.org/10.1086/294632
- Engle, R.F. and Patton, A.J. (2001) What Good Is a Volatility Model. Quantitative Finance, 1, 237-245. http://dx.doi.org/10.1088/1469-7688/1/2/305
- Black, F. (1976) The Pricing of Commodity Contracts. Journal of Financial Economics, 3, 167-179. http://dx.doi.org/10.1016/0304-405x(76)90024-6
- Box, G.E.P. and Jenkins, G.M. (1976) Time Series Analysis: Forecasting and Control, Revised Edition, Holden-Day.
- Francq, C. and Zakoian, J.-M. (2011) GARCH Models: Structure, Statistical Inference and Financial Applications. John Wiley & Sons.
- Hamilton, J.D. (1994) Time Series Analysis. Princeton University Press, Princeton.