Abstract
This paper uses autoregressive jump intensity (ARJI) model to show that the oil price has both GARCH and conditional jump component. In fact, the distribution of oil prices is not normal, and oil price returns have conditional heteroskedasticity. Here the authors compare constant jump intensity with the dynamic jump intensity and evidences demonstrate that oil price returns have dynamic jump intensity. Therefore, there is strong evidence of time varying jump intensity Generalized Autoregressive Heteroscedasticity (GARCH) behavior in the oil price returns. The findings have several implications: first, it shows that oil price is highly sensitive to news, and it does settle around a trend in long-run. Second, the model separates variances of high volatilities from smooth volatilities. Third, the model rejects an optimal path for extracting oil and technology transmission. In fact, the lack of a long-term pattern can cause excessive oil extracting which can result in heavy climatic effects. Keywords: generalized autoregressive heteroscedasticity (GARCH), jumps, basket, oil price, Organization of Petroleum Exporting Countries (OPEC), Autoregre-ssive jump intensity (ARJI). JEL Classification: C32, C52, F31
Highlights
The subject of oil price includes an extensive literature consisting of both theoretical and practical researches
This paper shows that many oil price behaviors are the same as the stock market behavior, since one can use models, which are used for modeling volatility of stock return, to model volatility of oil price return
The results show that the GEP outperforms artificial neural network (ANN) and ARIMA models in predicting oil prices
Summary
The subject of oil price includes an extensive literature consisting of both theoretical and practical researches. The other important branch of the literature addresses the question that whether there is a permanent and systematic pattern for the price of exhaustible resources in the present time. Results obtained in this relation are not clear, but Slade (1998) does find practical evidence that the pattern is random; Slade (1982) and Lee et al (2006) conclude that there is a second order, a permanent pattern with structural break, as it was expected. Previous models show that daily data on oil price and complex practical techniques are heavily used. Notwithstanding the fact that techniques such as GARCH models, artificial neural networks and jump-diffusion processes have
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