Abstract
This paper aims to explain crude oil price volatility and its relationship respect to some macroeconomic and financial variables. Finding the main drivers of oil price dynamics is a crucial element for the definition of adequate monetary policies and risk management purposes. The role of macroeconomic and financial variables is analyzed in a Vector Error Correction Model framework, in order to test the existence of a long run equilibrium in the oil price dynamics. We use monthly data for crude oil prices, the Dollar/Euro exchange rate, the US interest rate, the crude oil Futures open interest, the US oil imports and the gold price over the period 1993–2009. One cointegrating relationship is found which allows to identify a long run equilibrium between the variables.
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Notes
According to the International Energy Agency (EIA) the OECD countries still represent 60 % of the world energy consumption.
WTI is the benchmark for crude oil spot prices and the underlying commodity of the NYMEX’s oil future contracts (Geman 2005).
The GAUSS code is available at: http://qed.econ.queensu.ca/jae/2003-v18.1/bai-perron/.
In the following, for the VAR(p) model we exclude the presence of exogenous variables.
\(-\Upgamma\) is the matrix of adjustment coefficients which has dimension n × r and the coefficients, γ i , describe the speed of adjustment of the particular series Y t to deviation from the cointegration relationship, i.e., the equilibrium errors.
In an Engle–Granger framework (Engle and Granger 1987) given two price series Y 1,t and Y 2,t , both I(1), the “cointegration regression”, Y 1,t = α + β Y 2,t + z t , is estimated to fit equilibrium relationship. The Ordinary Least Squares (OLS) residuals z t from the cointegrating regression are estimates of the equilibrium errors.
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Bencivenga, C., D’Ecclesia, R.L. & Triulzi, U. Oil prices and the financial crisis. Rev Manag Sci 6, 227–238 (2012). https://doi.org/10.1007/s11846-012-0083-z
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DOI: https://doi.org/10.1007/s11846-012-0083-z