This document outlines a study that uses a factor-augmented vector autoregression (FAVAR) model to forecast oil prices. The study uses a large dataset of 150 monthly time series variables including macroeconomic indicators, financial indices, and energy price and quantity data from 1983 to 2011. Four common factors are extracted from this dataset that explain oil price movements. The FAVAR model is shown to produce more accurate out-of-sample 1-step ahead forecasts of spot and futures oil prices than alternative models like a VAR using only yields or factors alone. Plots of actual and fitted oil prices from the FAVAR model indicate it tracks price movements closely. The study aims to next improve forecasts during breaks and analyze recent oil price spikes.

1. Outline Introduction Model Data Empirical Work Results Conclusion
Macroeconomic Variables and Oil Prices: A Data
Rich Model
Hanan Naser
Department of Economics
The University of Sheffield
March 17, 2013
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2. Outline Introduction Model Data Empirical Work Results Conclusion
1 Introduction
2 Model
3 Data
4 Empirical Work
5 Results
6 Conclusion
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3. Outline Introduction Model Data Empirical Work Results Conclusion
Introduction
Background
Oil Role:
2/3 of world energy demand is met from crude oil (Alvarez Ramirez et
al.(2003))
The worlds’ largest and most actively traded commodity, over 10% of
total world trade (Verleger (1993))
Determined by its supply and demand But strongly influenced by
many irregular past/present/future events(Hagen (1994), Stevens (1995))
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4. Outline Introduction Model Data Empirical Work Results Conclusion
Introduction
Background
Reliable forecasts are interest for:
Central banks and private sectors: generating macroeconomic
projections and assessing macroeconomic risks
Helpful in predicting recessions (Hamilton (2009))
Predicting carbon emissions and designing regulatory policies
Modeling investment decisions in energy sector
Control usage
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5. Outline Introduction Model Data Empirical Work Results Conclusion
Introduction
Background
Figure 1: Historical Oil Prices
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6. Outline Introduction Model Data Empirical Work Results Conclusion
Introduction
Motivation
Fundamental Indicators Models
(Chevillon and Rifflart (2009), Kaufmann et al.(2004), Ye at al.(2005),
Zamani(2004), Killian (2008a), Killian (2008b), Alquist and Killian (2008))
Trichet (2008): ’Clarifies the role that factors unrelated to energy demand and supply can
play in oil markets’
’Financial press and speculation have been behind the recent spikes’ (Chung (2008) and
Makintosh (2008))
Financial Indicator Models
(Killian (2007), Askari and Krichene (2008), Chong and Miffre (2006), Gorton,
Hayashi and Rouwenhorst (2007), Marzo, Spargoli and Zagaglia (2009)
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7. Outline Introduction Model Data Empirical Work Results Conclusion
Introduction
Motivation
Econometric Models ( linear and non-linear )such as:
Simple econometric models ( Ye et.al (2002, 2005, 2006 ))
Co-integration analysis (Gulen (1998))
GARCH/ ARCH models (Chin Wen Cheong (2009))
VAR (Killian (2007, 2008a, 2008b), Mirmirani and Li (2004)), Yanan He et al
(2011)
Error Correction Models ( Lanza et al (2005), Zamani(2004))
AI, ANN, SVR (Mirmrani (2004), Xie et al (2006))
Limitations, ’Limited information’ & ’Specific information’
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8. Outline Introduction Model Data Empirical Work Results Conclusion
Introduction
Objectives
Objectives of the study:
Adapt a data rich model by employing a large dataset that includes
global macroeconomic indicators, financial market indices, quantities
and prices of energy products to forecast real spot and futures price of
oil.
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9. Outline Introduction Model Data Empirical Work Results Conclusion
Model
Factor Augmented VAR (FAVAR)
General Form:
Bernanke, Boivin and Eliasz (2005)
Ft
Yt
= φ(L)
Ft−1
Yt−1
+ υt (1)
φ(L) ⇒(K + M) × (K + M) matrix of lag polynomials
υt ⇒ (K + M) × 1 vector of standardized normal shocks
Yt = [yt, yt−1, ....] ⇒ M × 1 of observed variables
Ft = [ft , ft−1, ....] ⇒ K × 1 unobservable factors vector
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10. Outline Introduction Model Data Empirical Work Results Conclusion
Model
Factor Augmented VAR (FAVAR)
Observation Equation
Xt = Λf
Ft + Λy
Yt + t (2)
Λf ⇒N × K matrix of factor loadings
Λy ⇒ N × M matrix of Y loadings
t ⇒ N × 1 vector of error term
Xt ⇒ N × 1 informational series
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11. Outline Introduction Model Data Empirical Work Results Conclusion
Data
Time series variables and sources
Monthly data ⇒ 1983:03 to 2011:12
Oil Prices ⇒ WTI spot prices and Futures prices (one and three
months) of crude oil
All data obtained from Energy Information Administration (EIA) and
US statistics (USS)
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12. Outline Introduction Model Data Empirical Work Results Conclusion
Data
Dataset
Dataset
Price data ⇒ 43
Macroeconomic and Financial data ⇒ 14
Flow and Stock data ⇒ 93
Total ⇒ 150
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13. Outline Introduction Model Data Empirical Work Results Conclusion
Empirical Work
Preliminary Evidences
Table 1: Factors Correlation coefficients
Factor 1 Factor 2 Factor 3 Factor 4
Factor 1 1.000
Factor 2 -0.0506 1.000
Factor 3 -0.1373 -0.005 1.000
Factor 4 0.0517 0.0035 0.0017 1.000
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14. Outline Introduction Model Data Empirical Work Results Conclusion
Empirical Work
Preliminary Evidences
Table 2: Unrestricted regressions of yields on factors (Yt = ΛFt + εt)
WTI Spot Prices Future 1 Future 3
Factor 1 2.136420 2.135399 2.122267
(0.0559) (0.0562) (0.0582)
Factor 2 0.3518783 0.335605 0.2242803
(0.0859) (0.0862) (0.0891)
Factor 3 0.0684813 0.0722385 0.0942811
(0.09987) (0.1003) (0.1037)
Factor 4 -0.1482975 -0.1699506 -0.1791998
(0.10728) (0.1077) (0.1114)
R2
0.8137 0.8122 0.7993
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15. Outline Introduction Model Data Empirical Work Results Conclusion
Preliminary Evidences
Estimated Factors
Table 3: Share of explained variance of highly correlated series
Factor 1 Link to Figure 2 R2
Refiner Acquisition Cost of Crude Oil, Imported 0.9353
Landed Cost of Crude Oil Imports From All Non-OPEC Countries 0.9341
Refiner Acquisition Cost of Crude Oil, Composite 0.9327
Landed Cost of Crude Oil Imports 0.9296
Average F.O.B. Cost of Crude Oil Imports From All Non-OPEC Countries 0.9228
Factor 2 Link to Figure 3
U.S. Ending Stocks of Asphalt and Road Oil 0.3169
Other Petroleum Products Stocks 0.1123
Petroleum Consumption, Japan 0.0692
U.S. Ending Stocks of Gasoline Blending Components 0.0672
U.S. Ending Stocks of Total Gasoline 0.044
Petroleum Consumption, South Korea 0.0441
Factor 3 Link to Figure 4
U.S. Ending Stocks excluding SPR of Crude Oil and Petroleum Products 0.5093
Total Petroleum Stocks 0.5064
U.S. Ending Stocks of Crude Oil and Petroleum Products 0.5055
Other Petroleum Products Stocks 0.2193
Crude Oil Stocks, Non-SPR 0.2097
Factor 4 Link to Figure 5
Crude Oil Stocks, Non-SPR 0.2931
U.S. Ending Stocks excluding SPR of Crude Oil 0.2886
Crude Oil Stocks, Total 0.285
U.S. Ending Stocks of Crude Oil 0.2779
Other Petroleum Products Stocks 0.1605
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Results
Estimated Factors and Highest Correlated series
Figure 2: Factor 1
Return
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17. Outline Introduction Model Data Empirical Work Results Conclusion
Results
Estimated Factors and Highest Correlated series
Figure 3: Factor 2
Return
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18. Outline Introduction Model Data Empirical Work Results Conclusion
Results
Estimated Factors and Highest Correlated series
Figure 4: Factor 3
Return
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Results
Estimated Factors and Highest Correlated series
Figure 5: Factor 4
Return
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20. Outline Introduction Model Data Empirical Work Results Conclusion
Results
Out of Sample forecasts
Table 4: 1-Step ahead forecast RMSE
Dependent Variable
Model
FAVAR VAR with yeilds only Factor only
WTI Spot Prices 0.9733 1.0011 1.0754
Future 1 0.9731 0.9890 1.0721
Future 3 0.9946 0.9913 1.0933
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Results
Plot of Fitted and Actual series
Figure 6: WTI Spot Prices
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Results
Plot of Fitted and Actual series
Figure 7: Future 1 Prices
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Results
Plot of Fitted and Actual series
Figure 8: Future 3 Prices
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Preliminary Conclusion
I showed that extracted factors generate information, which once
combined with yield, improves the forecasting performance for oil
prices
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25. Outline Introduction Model Data Empirical Work Results Conclusion
Next
Augment the model with Differenced model to improve forecasting
during breaks following Castle et al.(2011)
Produce IR for selected variables included in the main dataset (X)
Analyze the source of recent price spikes
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The End
Thank You
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