1 R ICE U NIVERSITY The Relationship between Crude Oil and Natural Gas Prices Peter Hartley Kenneth Medlock III Jennifer Rosthal James A. Baker III Institute of Public Policy RICE UNIVERSITY

2 R ICE U NIVERSITY Outline Motivation Methodology  OLS Engle-Granger  VECM Data Results  Cointegrating Relationships  OLS  VECM  Error Correction Model  OLS  3SLS Conclusions

3 R ICE U NIVERSITY Why focus on natural gas and oil prices? Historically, a 10:1 ratio between $/barrel of crude oil and $/mmbtu of natural gas  The “rule of thumb” has recently changed to 6:1 or 7:1, with some debate about what is appropriate  Some question whether there is a stable relationship at all Is such a relationship reliable, for example, for guiding futures or options trading or planning investments? What could lead to such relationship?  Substitution in end-use  Energy content relationship

4 R ICE U NIVERSITY Real values of natural gas and oil products Natural gas prices appear to related to residual fuel oil prices They also have brief periods where they spike above even the WTI price in energy-equivalent terms

5 R ICE U NIVERSITY Error correction model Basic idea: there is a long run equilibrium stable functional relationship between a small number of variables  Example: two variables tend to a particular ratio  Arbitrage is often the source of stable equilibrium price ratios Shocks lead to short run departures from this equilibrium relationship but adjustments tend to re-establish the relationship in the long run Research strategy:  Identify a stable long run relationship  OLS  VECM – Johansen MLE  Identify shocks that cause departures from that relationship  Estimate the adjustment process  OLS/IV  3SLS

6 R ICE U NIVERSITY Electricity sector role Energy input is a very important cost  Investments have been made to limit differences between fuels with respect to pollution and other non-energy characteristics Some NERC regions have plants with switching capability In more regions, substitution is possible by running plants for different periods of time  Plants move up and down the supply stack as fuel prices change Leads us to a focus on:  Residual fuel oil rather than WTI  Technical change affecting heat rates of CCGT as an explanation for the apparent changed equilibrium relative price ratio Cost of generation:  $/MWh = ($/Btu)*(Btu/MWh) = fuel price*heat rate

7 R ICE U NIVERSITY Data Monthly prices of natural gas (HH) from Natural Gas Weekly, wholesale price of fuel oil and WTI crude from the EIA  Convert prices to real values using industrial electricity retail price as the deflator  Logarithmic transformations re-scale fluctuations by the levels of the variables Heat rate data were constructed from the EPA NEEDS data and the Annual Electric Generator Report (Form-860)  A capacity-weighted heat rate was calculated for each plant type in each NERC region  The heat rate data forces us to the monthly frequency Other variables used to model the short run adjustment process:  Beginning of month storage levels (EIA)  Heating (HDD) and cooling (CDD) degree days  Deviations from the average to measure unusual weather  Extreme HDD variable measures the top decile of the HDD distribution  A constructed variable to reflect Gulf Coast hurricanes  Chicago February 1996 incident  Monthly fixed effects

8 R ICE U NIVERSITY Heat Rates Capacity weighted heat rates calculated: Heat Rate ratios have a nonlinear time trend over the time period due to the adoption of CCGT plants Lower heat rates for gas plants decrease the cost of electricity generation using gas ceteris paribus

9 R ICE U NIVERSITY Stationarity and cointegration When variables have trends, there is a danger that standard statistical methods can lead to spurious relationships A variable with a stochastic trend -- a random walk -- is said to be integrated, which is one type of non-stationarity Two integrated variables are cointegrated if they are functionally related in way that leaves a stationary residual error term  Because the function eliminates the trending components of the variables, it represents a long run equilibrium tendency  The residual error measures departures from that long run equilibrium tendency  If the system is dynamically stable, departures should lead to self- correcting movements over time  The short run adjustment process is called an error correcting mechanism Testing revealed the logarithms of prices and relative heat rates were non-stationary but rates of change were in all cases stationary

10 R ICE U NIVERSITY Estimated long run relationships - OLS Both residuals are stationary, but if the first equation  omits the relative heat rate, or  uses WTI instead of residual fuel oil the residual from that equation becomes non-stationary

11 R ICE U NIVERSITY Resulting Oil-Gas Price Relationship

12 R ICE U NIVERSITY Estimated long run relationships - VECM Johansen VECM uses a maximum likelihood approach to simultaneously solve the cointegrating equations Again, we find a cointegrating relationship between NG and RFO and a second cointegrating relationship between RFO and WTI The NG-RFO relationship is dependent on relative heat rates with the expected sign

13 R ICE U NIVERSITY Estimated dynamic adjustment equations

14 R ICE U NIVERSITY Error Correction Model OLS cointegrating equation  IV ECM – due to the potential endogeneity of the change in residual fuel oil price, we use a set of instruments (lagged residual price change, current and lagged WTI price change, weather, and storage)  Hausman test shows that we can treat a change in the price of residual fuel oil as exogenous in the natural gas price adjustment equation VECM  The error correction model is obtained using three stage least squares  The VECM approach allows us to impulse response functions Results using the OLS and VECM approach are similar

15 R ICE U NIVERSITY Impulse Response Functions IRFs measure the effect of a one standard deviation change in the impulse variable on the response variable

16 R ICE U NIVERSITY Some observations on the dynamic equations All variables have the expected signs  The adjustment process in response to deviations is stable  Both HDD and CDD deviations have significant, but short-lived, effects on short run NG price movements  Extreme HDD deviations affect RFO prices and the Feb 1996 extreme weather in Chicago had a strong but temporary effect on NG prices  Hurricanes are another significant factor for NG prices  1 bcf shut-in from hurricanes  approximately $1.03/mmbtu NG increase  Higher storage levels at the beginning of a month lead to lower prices over the month  Seasonal patterns in price movements appear reasonable Changes in the residual fuel oil price affect natural gas prices more than vice versa Changes in WTI have a large effect on residual fuel oil prices but not directly on natural gas prices We find evidence of a chain of causation WTI  RFO  NG

17 R ICE U NIVERSITY Thanks ! P.S. I am currently on the job market in case anyone is looking