1. Vance Ginn Chapter 1 of Dissertation Texas Tech University Sam Houston State University Fall 2011 Vance.Ginn@SHSU.EDU

2. Introduction Gasoline prices impact consumers similar to a tax. Edelstein & Kilian (2009): SUVs and complements Diesel prices affect decisions made by many firms. Brown & Theis (2009): increase costs Monetary policymakers may react to fuel prices. Pindyck (1999): reversion to mean Bernanke (2010, 2011): transitory, but closely watching Friedman (1968): information about economic events My goal is to provide good models to forecast fuel prices during different periods of volatility in an era of rising petroleum prices. 2

3. Literature Review Anderson, Kellogg, and Sallee (2011) use the Michigan Survey of Consumers: what do consumers believe the price of gasoline will be in the future? Forecast by consumers is similar to a random walk Ginn and Gilbert (2009): prices of crude oil futures and gas There is a 2% increase in the average weekly price of gasoline for every 10% increase in average weekly oil price futures. There were periods that the model did not perform very well. This lack of efficiency indicates that there may be better measures to predict gas prices. Crude oil is the main component (42 gallons in barrel) Chouinard and Perloff (2002): gas prices (19 gallons) Brown and Thies (2009): diesel prices (10 gallons) 3

4. Gasoline and Diesel Price Components 4

5. Literature Review Futures prices: Fama and French (1987): efficient market hypothesis (EMH). Chinn and Coibion (2010): gasoline price futures and heating oil futures are good predictors of their future spot prices. Oil price futures appear to not be as efficient: Alquist and Kilian (2010): not a reliable predictor in 2000s. Buyuksahin and Harris (2011): speculators distort price futures? Not likely, due to investors following oil market fundamentals. Wu and McCallum (2005) show that light trading exists in longer term contracts than short-term ones, reducing the ability for prices to be valued correctly. So the question remains, what variable(s) will provide good forecasts for future gas and diesel prices? 5

6. Data I use monthly data from the EIA for the sample period January 1983 to March 2010 for the following variables: Motor gasoline regular grade retail price (GP) (including all taxes) On-highway diesel fuel price (DP) (including all taxes) New York Harbor No. 2 heating oil future contract 1 (DPFut) Imported crude oil price (OP) Crude oil price futures (OilFut) that are traded on the New York Mercantile Exchange (NYMEX) New York Harbor regular gasoline future contract 1 (GPFut), which is available from January 1985 to March 2010. Split into two types: Reformulated regular gasoline: January 1985 to December 2006 Reformulated gasoline blendstock for oxygenate blending (RBOB) includes a percentage of ethanol that was added to gasoline in 2005: January 2007 to March 2010. 6

7. An Era of Rising Petro Prices

8. MeanStd. Dev.GPGPFutDPDPFutOPOilFut Retail Prices of Gasoline (GP) $1.130.171 Gas Price Futures (GPFut) $0.610.150.861 Retail Prices of Diesel (DP) $1.140.160.940.821 Diesel Price Futures (DPFut) $0.600.150.750.880.831 Imported Crude Oil Prices (OP) $19.995.490.760.910.780.941 Crude Oil Price Futures (OilFut) $21.835.510.800.940.830.960.981 Table 1: Statistics for Variables for Estimation Period from 1983:1-2002:12 Summary Stats Correlation Stats MeanStd. Dev.GPGPFutDPDPFutOPOilFut Retail Prices of Gasoline (GP) $2.390.631 Gas Price Futures (GPFut) $1.700.600.981 Retail Prices of Diesel (DP) $2.520.760.960.941 Diesel Price Futures (DPFut) $1.730.690.96 0.991 Imported Crude Oil Prices (OP) $56.4223.480.960.970.960.981 Crude Oil Price Futures (OilFut) $61.8824.270.960.97 0.990.9951 Table 2: Statistics for Variables for Forecast Period from 2003:1-2010:3 Summary Stats Correlation Stats

9. Log Levels:Log(GP)Log(GPFut)Log(DP)Log(DPFut)Log(OP)Log(OilFut) ADF Test Statistics:-2.26-3.03-2.33-3.15-3.09-3.38 DLOG:ΔGP ΔGPFutΔDPΔDPFutΔOPΔOilFut ADF Test Statistics:-4.52-12.02-10.33-11.80-8.07-10.24 Test Critical Values: 1% level-3.46 5% level-2.87 10% level-2.57 Log Levels:Log(GP)Log(GPFut)Log(DP)Log(DPFut)Log(OP)Log(OilFut) PP Test Statistics:-2.26-2.95-2.13-2.91-2.90-2.51 DLOG:ΔGP ΔGPFutΔDPΔDPFutΔOPΔOilFut PP Test Statistics:-10.26-13.05-10.52-11.73-8.38-11.04 Test Critical Values: 1% level-3.46 5% level-2.87 10% level-2.57 Table 4: Phillips-Perron Unit Root Tests Null Hypotheses: GP, GPFut, DP, DPFut, OP, OilFut has a unit root Exogenous: Constant, Bandwidth: (Newey-West automation) using Bartlett kernel Table 3: Augmented Dickey-Fuller Unit Root Tests Null Hypotheses: GP, GPFut, DP, DPFut, OP, OilFut has a unit root Exogenous: Constant, Lag Length: Automatic selection based on AIC, max lag is 14) Note: The sample period is 1983:1-2002:12, except for gasoline price futures (GPFut) which is from 1985:1-2002:12.

10. Dependent: GPTau-statProb.*z-statProb.* Gas Price Futures (GPFut)-3.440.0409-23.610.0228 Imported Crude Oil Prices (OP)-2.430.3157-11.570.2714 Crude Oil Price Futures (OilFut)-3.030.1079-18.120.0756 Dependent: DPTau-statProb.*z-statProb.* Diesel Price Futures (DPFut)-2.520.2739-11.650.2678 Imported Crude Oil Prices (OP)-2.110.4706-8.200.4765 Crude Oil Price Futures (OilFut)-2.520.2750-11.880.2570 Table 5: Engle-Granger Cointegration Tests *MacKinnon (1996) p-values. Notes: The data are in logs and the sample period is 1983:1-2002:12, except for gasoline price futures (GPFut) which is from 1985:1-2002:12. The null hypothesis is that the series are not cointegrated. The automatic lag specification is based on the Schwarz Bayesian Criterion (SBC). 10

11. AR ARIMA AROilFutS AROPS DPFut GPFut MA OilFut OilFutS OP Table 7: Forecast Model Representations 11

12. Forecast Period2003:1-2004:122005:6-2007:52008:4-2010:3 h=1h=3h=9h=12h=1h=3h=9h=12h=1h=3h=9h=12 OilFut(AIC=1)0.070.120.130.170.180.300.350.290.180.330.510.31 Relative RMSEs OilFut RMSE1.00 ARCH(OilFut) 1.020.990.951.021.031.021.001.061.071.091.061.16 ARCH(OilFutS) 1.021.000.800.890.980.950.881.021.091.081.051.25 Random Walk 1.211.371.471.851.101.281.241.661.351.831.812.72 ARCH(RW) 1.211.351.371.751.111.261.181.641.381.831.732.73 AR(3)S 1.201.411.301.650.991.141.181.501.351.731.722.65 ARIMA(1,1,2) 1.191.372.442.131.091.432.192.211.321.64 2.40 ARIMAS 1.181.301.421.560.971.111.151.451.331.711.472.62 GPFut 0.680.560.980.290.620.550.460.650.700.660.670.55 ARCH(AROFS) 0.960.990.960.800.930.940.811.061.031.090.981.33 MA(1) 1.161.261.501.840.991.231.301.591.331.841.672.74 MAS 1.181.331.361.580.981.131.211.531.301.731.602.67 ARCH(OP) 1.010.990.911.090.95 0.890.981.000.980.871.09 ARCH(OPS) 0.990.970.760.92 0.880.780.941.020.960.891.11 AROPS 0.910.950.800.840.790.840.780.820.870.830.710.89 Table 10: Gas Out-of-Sample Rolling Forecast RMSEs

13. Table 11: Diesel Out-of-Sample Rolling Forecast RMSEs Forecast Period2003:1-2004:122005:6-2007:52008:4-2010:3 h=1h=3h=9h=12h=1h=3h=9h=12h=1h=3h=9h=12 OilFut(AIC=3)0.050.080.10 0.150.190.230.190.100.160.21 Relative RMSEs OilFut RMSE1.00 OilFutS1.031.061.211.180.991.040.991.051.171.221.061.25 Random Walk1.631.843.022.531.231.461.572.191.972.792.714.50 AR(2)1.551.773.012.511.181.451.572.072.092.952.444.25 ARS1.661.943.302.651.181.511.442.122.172.942.814.48 MA(1)1.561.793.042.521.181.441.572.102.072.892.534.33 MAS1.651.903.302.641.171.471.422.102.202.962.774.44 ARIMA(1,1,2)1.611.953.282.831.271.803.093.242.082.643.333.47 ARIMAS1.651.893.332.631.171.491.432.142.152.952.804.49 ARCH(DPFut)0.880.590.630.940.860.840.820.881.001.010.771.23 AROilFutS1.001.031.241.170.961.030.981.031.231.281.131.22 ARCH(AROPS)1.081.171.461.390.890.940.770.921.171.191.681.12 OP(3)1.111.201.301.280.960.950.800.910.960.981.650.98 ARCH(OPS)1.121.231.671.420.940.960.770.951.771.931.191.17

14. Rolling Out-of-Sample Forecasts: 2003:1-2004:12 GP Forecast Using GPFut DP Using ARCH(DPFut) 14

15. Rolling Out-of-Sample Forecasts: 2005:6-2007:5 GP Forecast Using GPFut DP Using ARCH(DPFut) 15

16. Rolling Out-of-Sample Forecasts: 2008:4-2010:3 GP Forecast Using GPFut DP Using OP 16

17. Conclusions No matter whether gas prices are stable or not, their futures price does better at forecasting them than others. Fama(1970): Efficient Market Hypothesis Similarly, diesel price futures perform well at predicting diesel prices during periods of stable increases and shocks, but spot oil prices helped predict prices during the latter period. Therefore, I construct good models to forecast fuel prices during recent periods of rising petroleum prices that achieves a further understanding of their future prices. 17

18. Future Research What do futures prices tell us about business cycles? What are the impacts on the term structure of interest rates from gas and diesel price shocks? What implications are there for monetary policy? Do asymmetric responses exist between fuel price futures and the price at the pump? 18