Outline Introduction Model Data Empirical Work Results Conclusion

Macroeconomic Variables and Oil Prices: A DataRich Model

Hanan Naser

Department of EconomicsThe University of Sheffield

March 17, 2013

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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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Outline Introduction Model Data Empirical Work Results Conclusion

IntroductionBackground

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% oftotal world trade (Verleger (1993))

Determined by its supply and demand But strongly influenced bymany irregular past/present/future events(Hagen (1994), Stevens (1995))

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IntroductionBackground

Reliable forecasts are interest for:

Central banks and private sectors: generating macroeconomicprojections 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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IntroductionBackground

Figure 1: Historical Oil Prices

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IntroductionMotivation

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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IntroductionMotivation

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’7 / 26

Outline Introduction Model Data Empirical Work Results Conclusion

IntroductionObjectives

Objectives of the study:

Adapt a data rich model by employing a large dataset that includesglobal macroeconomic indicators, financial market indices, quantitiesand prices of energy products to forecast real spot and futures price ofoil.

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ModelFactor Augmented VAR (FAVAR)

General Form:Bernanke, Boivin and Eliasz (2005)

[Ft

Yt

]= φ(L)

[Ft−1Yt−1

]+ υt (1)

φ(L)⇒(K + M)× (K + M) matrix of lag polynomials

υt ⇒ (K + M)× 1 vector of standardized normal shocks

Yt = [y′t, y′t−1, ....]⇒ M × 1 of observed variables

Ft = [f ′t , f′t−1, ....]⇒ K × 1 unobservable factors vector

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Outline Introduction Model Data Empirical Work Results Conclusion

ModelFactor Augmented VAR (FAVAR)

Observation Equation

Xt = Λf Ft + ΛyYt + εt (2)

Λf ⇒N × K matrix of factor loadings

Λy⇒ N ×M matrix of Y loadings

εt ⇒ N × 1 vector of error term

Xt ⇒ N × 1 informational series

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DataTime series variables and sources

Monthly data⇒ 1983:03 to 2011:12

Oil Prices⇒WTI spot prices and Futures prices (one and threemonths) of crude oil

All data obtained from Energy Information Administration (EIA) andUS statistics (USS)

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DataDataset

Dataset

Price data⇒ 43

Macroeconomic and Financial data⇒ 14

Flow and Stock data⇒ 93

Total⇒ 150

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Outline Introduction Model Data Empirical Work Results Conclusion

Empirical WorkPreliminary 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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Empirical WorkPreliminary 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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Preliminary EvidencesEstimated 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 0.9353Landed Cost of Crude Oil Imports From All Non-OPEC Countries 0.9341Refiner Acquisition Cost of Crude Oil, Composite 0.9327Landed Cost of Crude Oil Imports 0.9296Average 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 0.3169Other Petroleum Products Stocks 0.1123Petroleum Consumption, Japan 0.0692U.S. Ending Stocks of Gasoline Blending Components 0.0672U.S. Ending Stocks of Total Gasoline 0.044Petroleum Consumption, South Korea 0.0441

Factor 3 Link to Figure 4

U.S. Ending Stocks excluding SPR of Crude Oil and Petroleum Products 0.5093Total Petroleum Stocks 0.5064U.S. Ending Stocks of Crude Oil and Petroleum Products 0.5055Other Petroleum Products Stocks 0.2193Crude Oil Stocks, Non-SPR 0.2097

Factor 4 Link to Figure 5

Crude Oil Stocks, Non-SPR 0.2931U.S. Ending Stocks excluding SPR of Crude Oil 0.2886Crude Oil Stocks, Total 0.285U.S. Ending Stocks of Crude Oil 0.2779Other Petroleum Products Stocks 0.1605

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ResultsEstimated Factors and Highest Correlated series

Figure 2: Factor 1

Return

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ResultsEstimated Factors and Highest Correlated series

Figure 3: Factor 2

Return

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ResultsEstimated Factors and Highest Correlated series

Figure 4: Factor 3

Return

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ResultsEstimated Factors and Highest Correlated series

Figure 5: Factor 4

Return

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ResultsOut 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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ResultsPlot of Fitted and Actual series

Figure 6: WTI Spot Prices

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ResultsPlot of Fitted and Actual series

Figure 7: Future 1 Prices

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ResultsPlot of Fitted and Actual series

Figure 8: Future 3 Prices

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Preliminary Conclusion

I showed that extracted factors generate information, which oncecombined with yield, improves the forecasting performance for oilprices

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Next

Augment the model with Differenced model to improve forecastingduring 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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