An analysis of the global oil market using SVARMA models

Abstract

The paper analyses the importance of supply versus demand shocks on the global oil market from 1974 to 2017, using a parsimonious structural vector autoregressive moving average (SVARMA) model. The superior out-of-sample forecasting performance of the reduced form VARMA compared to VAR alternatives advocates the suitability of this framework. We specifically account for the changes in the oil market over three distinctive sub-periods-pre moderation, great moderation and post moderation periods, to provide a means of identifying the changing nature of shock transmission mechanism across times. The findings shed some light on the effects of supply versus demand related oil shocks under different economic environment. Oil supply shocks explain large fraction of the movements in the global oil market in the pre and post moderation periods, i.e. during the slower economic growth periods. The importance of global activity shock on oil price movements is obvious during the 2003-2008 boom period. The oil specific shock has an interesting transmission path on the global economic activity, where the global activity responded positively and negatively during the global economic expansion and contraction respectively, emphasising the precautionary nature of the shock.

Dynamic Interrelationship and Volatility Spillover among Sustainability Stock Markets, Major European Conventional Indices, and International Crude Oil

“…Researchers have widely studied the relationship between the oil market as a major source of energy and economic activity (e.g., [14][15][16][17][18][19][20]), as well as the impact of oil prices on conventional stock returns (e.g., [21][22][23][24][25][26][27][28][29]). Some authors have investigated the nexus between oil prices and the stock market in oil-exporting countries.…”

Analysis of the oil market and the impact of integration processes on socio-economic development indicators of Russia

“…The methods used in market research include: statistical data processing, multivariate methods, simulation methods, regression methods, correlation methods, statistical theory, hybrid methods, deterministic methods and other methods [23][24][25][26][27].…”