68th International Atlantic Economic Conference

October 08 - 11, 2009 | Boston, USA

What Can Futures Contracts Teach Us about the Crude Oil Spot Market?

Saturday, October 10, 2009: 9:10 AM
Michael M. Ye, Ph.D. , Economics, St. Mary's College of Maryland, St. Mary's City, MD
John Zyren, Ph.D. , Energy Information Administration,, U.S. Department of Energy, Washington, DC
Joanne Shore, M.B.A. , Energy Information Administration,, U.S. Department of Energy, Washington, DC
Thomas Lee, Ph.D. , Energy Information Administration, Washington, DC
This study investigates the relationship between the West Texas Intermediate (WTI) crude oil spot market price and the volume traded and price term structure of New York Mercantile Exchange (NYMEX) light sweet crude oil futures contracts.  Based on previous graphical investigation, this paper attempts to formalize statistical relations between the spot and futures markets in terms of (A) how information on futures price and volume may provide early indication of price shifts in the crude oil spot market; (B) any indication that seasonality in futures prices and volumes is changing over time; (C) static and dynamic correlation analysis and Granger causality tests to investigate relationships between head (contract maturity from 1 to 12 months) and tail (maturity from 13 to 84 months) volumes and price term structures and determine if those relationships can be used to predict changes in spot and futures prices, focusing particularly on predict near-term spot prices.  Daily data on spot WTI crude oil price and NYMEX Light Sweet Crude Oil futures contract prices and volumes for the period from 1990 to the present were obtained for all contract maturities from one to 84 months.  This daily data is aggregated to monthly frequency in order to match the availability of market variables such as spare production capacity and petroleum inventories.  This analysis will be used to determine if these relationships can be utilized to provide an early indication of market regime shifts and improve short-run crude oil price forecast models.