Analysis of the Behavior of Volatility in Crude Oil Price

Authors

  • Fernando Antonio Lucena Aiube
  • Tara Keshar Nanda Baidya

DOI:

https://doi.org/10.18533/jefs.v2i01.129

Keywords:

GARCH models, Two-factor model, Crude oil, Volatility.

Abstract

This article analyzes volatility in the spot price of crude oil. In recent years the price has also increased reaching more than US$ 140/barrel in the last decade. Moreover, the negotiated trading volume in the futures market in recent years higher than the trading volume of the earlier years. How these changes have affected the volatility in the oil prices? Does the presence of huge players, which leads to an increase in the volume under negotiation, increase volatility? Has the persistence been affected? To answer these questions, we first estimated spot prices using the two-factor model of Schwartz and Smith. With this filtering process we can capture the entire information from the future term-structure. We then analyzed the estimated spot-price series to identify the stylized facts and then adjusted conditional volatility models of GARCH family. Our findings show that the volatility in the high prices period is not different from that of low prices. The shocks behaved as transitory and the persistence in the high prices period decreased. This fact has pricing and hedging implications for short-term derivatives.

References

Baillie, R. T., Myers, R. J., 1991. Bivariate GARCH estimation of the optimal commodity futures hedge. Journal of Applied Econometrics, 6: 109-124. http://dx.doi.org/10.1002/jae.3950060202

Barsky, R., Kilian, L., 2004. Oil and the macroeconomy since the 1970s.Working paper 10855, NBER, http://www.nber.org/papers/w10855.

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

Brock, W., Dechert, W. D., Scheinkman, J., 1987. A test for independence based on the correlation dimension. Working paper, Department of Economics, University of Wisconsin, Madison, University of Houston, and University of Chicago.

Duffie, D., Kan, R., 1996. A yield-factor model of interest rates. Mathematical Finance, 6(4): 379-406. http://dx.doi.org/10.1111/j.1467-9965.1996.tb00123.x

Duffie, D., Pan, J., Singleton, K. J., 2000. Transformation analysis and asset pricing for affine jump-diffusions, Econometrica, 68(6): 1343-1376. http://dx.doi.org/10.1111/1468-0262.00164

Durbin, J., Koopman, S. J., 2002. Time Series Analysis by State Space Methods. Oxford Statistical Science Series, 24. Oxford University Press, Oxford.

Engle, R., Ng, V., 1993. Measuring and testing the impact of news on volatility. Journal of Finance, 48: 1749-1778. http://dx.doi.org/10.1111/j.1540-6261.1993.tb05127.x

Franses, P. H., van Dijk, D., 2000. Non-linear Time Series Models in Empirical Finance. Cambridge University Press, Cambridge. http://dx.doi.org/10.1017/CBO9780511754067

Gibson, R., Schwartz, E. S., 1990. Stochastic convenience yield and the pricing of oil contingent claims. The Journal of Finance, 45(3): 959-976. http://dx.doi.org/10.1111/j.1540-6261.1990.tb05114.x

Glosten, L. R., Jagannathan, R., Runkle, D., 1993. On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48: 1779-1801. http://dx.doi.org/10.1111/j.1540-6261.1993.tb05128.x

Hadsell, L., Marathe, A., Shawky, A., 2004. Estimating the volatility of wholesale electricity spot prices in the US. The Energy Journal, 25(4): 23-40. http://dx.doi.org/10.5547/ISSN0195-6574-EJ-Vol25-No4-2

Harvey, A. C., 1989. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge.

Hentschel, L., 1995. All in the family nesting symmetric and asymmetric GARCH models. Journal of Financial Economics, 39: 71-104. http://dx.doi.org/10.1016/0304-405X(94)00821-H

Horan, S. M., Peterson, J. H., Mahar, J.,2004. Implied volatility of oil futures options surrounding OPEC meetings. The Energy Journal, 25(3): 103-125. http://dx.doi.org/10.5547/ISSN0195-6574-EJ-Vol25-No3-6

Hsieh, D. A., 1989. Testing for nonliner dependence in daily foreign exchange rates. Journal of Business, 62(3): 339-368. http://dx.doi.org/10.1086/296466

Lautier, D., Riva, F., 2004. Volatility in the American crude oil futures market. Working paper, University Paris Dauphine.

Manoliu, M., Tompaidis, S., 2000. Energy futures prices: Term structure models with Kalman filter estimation. Working paper, University of Texas, Austin.

Mu, X., 2007. Weather, storage, and natural gas price dynamics: Fundamentals and volatility. Energy Economics, 29(1): 46-63. http://dx.doi.org/10.1016/j.eneco.2006.04.003

Nelson, D. B., 1991. Conditional heteroskedasticity in asset returns: a new approach. Econometrica, 59: 347-370. http://dx.doi.org/10.2307/2938260

Pindyck, R. S., 2004. Volatility in natural gas and oil market. The Journal of Energy and Development, 30(1): 1-19.

Schwartz, E. S., Smith, J. E., 2000. Short term-variations and long-term dynamics in commodity prices. Management Science, 46: 893-911. http://dx.doi.org/10.1287/mnsc.46.7.893.12034

_______________. Short term-variations and long-term dynamics in commodity prices: Incorporating a stochastic growth rate, available at http://faculty.fuqua.duke.edu/%7Ejes9/bio/Linked_Paper_List.htm

Sørensen, C., 2002. Modeling seasonality in agricultural commodity futures. Journal of Futures Markets, 22: 393-426. http://dx.doi.org/10.1002/fut.10017

U.S.-EIA, 2002. Derivatives and risk management in the petroleum, natural gas, and electricity industries. Tech. rep., U.S. Energy Information Administration.

Wilson, B., Aggarwal, R., Inclan, C., 1996. Detecting volatility changes across the oil sector. The Journal of Futures Markets, 16(3): 313-330. http://dx.doi.org/10.1002/(SICI)1096-9934(199605)16:3<313::AID-FUT4>3.0.CO;2-M

Zakoian, J. M., 1994. Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18: 931-955. http://dx.doi.org/10.1016/0165-1889(94)90039-6

Downloads

Published

2014-02-25

Issue

Section

Articles