An enquiry into the dynamics of real oil prices: A state space approach

Energy Economics, 2006

The purpose of this paper is to present a quantitative analyses of oil price's path. We try to argue that, despite its parsimony and simplicity, Geometric Brownian Motion can perform well as a proxy for the movement of oil prices and for a state variable to evaluate oil deposits. We base our argument on evidences of very low speed of mean reverting (or long half-life), since unit root tests only can reject its null hypothesis in a sample longer than 100 years. On the other hand, we reject the null hypothesis of unit root with two endogenous breaks, showing that the usual rejection can be attributed to omitted structural breaks. We conclude that the average half-life of oil price (between 4 and 8 years depending on the model chosen) is long enough to allow a good approximation as a Geometric Brownian Motion.

Business and Economic Research, 2013

As many researchers know, the price of oil was affected by a structural change, which we analyzed. We obtained the result of the Chow test (determining the date when the structural break occurred). We observed that important data such as the variance or the VAR vary

The recent commodity price boom has spurred interest to understand determinants of commodity price movements. This paper investigates the causal relationship between oil prices and the prices of 25 other commodities, which include both metals and agricultural products, in the presence of instability and nonlinearity. For this purpose, we make use of a long annual time series dataset spanning from 1900 to 2011, and analyze time-varying Granger causality test, since the inference drawn based on linear Granger causality tests could be invalid due to structural breaks and nonlinearity-which we show are present in the relationship between the variables of interest. We find that, under the case of time-varying causality there are fewer rejections of the null, than under the standard linear Granger causality test, thus highlighting the importance of accounting for instability and nonlinearity. Relying on the time-varying causality test, we observe stronger evidence of other commodity prices in predicting (in-sample) oil prices (15 cases) than the other way around (7 cases).

2014

The recent commodity price dynamics has spurred interest to understand determinants of commodity price movements. This paper investigates the causal relationship between oil prices and the prices of 25 other commodities, which include both metals and agricultural products, in the presence of instability and nonlinearity. For this purpose, we make use of a long annual time series dataset spanning from 1900 to 2011, and analyze time-varying Granger causality test, since the inference drawn based on linear Granger causality tests could be invalid due to structural breaks and nonlinearity – which we show are present in the relationship between the variables of interest. We find that, under the case of time-varying causality there are fewer rejections of the null than under the standard linear Granger causality test, thus highlighting the importance of accounting for instability and nonlinearity. Relying on the time-varying causality test, we observe stronger evidence of other commodity ...

Energies

This paper examines the dynamic relationship between crude oil prices and the U.S. exchange rate within the structural break detection context. Based on monthly data from January 1996 to April 2019, this paper identifies structural breaks in movements of oil price and examines the dynamic relationship between crude oil prices and the U.S. exchange rate movement by introducing the economic policy uncertainty and using the TVP-VAR (Time-Varying Parameter-Vector Auto Regression ) model. Empirical results indicate that shocks to crude oil prices have immediate and short-term impacts on movements in the exchange rate which are emphasized during the confidence intervals of structural breaks. Oil price shocks and economic policy uncertainty are interrelated and influence movements in the U.S. exchange rate. Since the U.S. dollar is the main currency of the international oil market and the U.S. has become a major exporter of crude oil, the transmission of price shocks to the U.S. exchange r...

Macroeconomic Dynamics, 2016

This special issue of Macroeconomic Dynamics presents a timely and fresh body of high-quality research on the complexity and evolution of the international oil markets, the dynamics of the price of oil, and the financialization and the interconnections of oil, energy, and nonenergy commodity markets. With major changes in the global energy scene in the aftermath of the global financial crisis and the changing energy and climate debate, there is great interest worldwide in the determinants of oil prices, as well as in the relationship between the price of oil, the level of economic activity, the prices of oil products, the prices of nonenergy commodities, and the role played by financial speculation. Among academics and applied professional economists, a high demand for qualified research is clearly perceived on a number of topics, which are subjects of current debate in the literature. An example of those major issues is the relationship between economic activity and oil prices. Although many empirical contributions suggest that this link is asymmetric, recent studies that use new methodologies to test for asymmetries have cast some doubts on that premise. Should those findings be confirmed by further research, important implications for the typical channels of transmission of oil price shocks would follow. Not only are asymmetries likely to characterize the link between economic activity and oil prices, but also they are often employed to offer more accurate descriptions of the pricing relationships between crude oil and refined products. In this respect, the effects of volatility in oil prices on the degree of asymmetry in the

The complexity of the world oil market has increased dramatically in recent years and new approaches are needed to understand, model, and forecast oil prices today. In addition to the commencement of the financialization era in oil markets, there have been structural changes in the global oil market. Financial instruments are communicating information about future conditions much more rapidly than in the past. Prices from long and short duration contracts have started moving more together. Sudden supply and demand adjustments, such as the financial crisis of 2008-2009, faster Chinese economic growth, the Libyan uprising, the Iranian nuclear standstill or the deepwater horizon oil spill, change expectations and current prices. The daily Brent spot price fluctuated between $30 and above $140 per barrel since the beginning of 2004. Both fundamental and financial explanations have been offered as explanatory factors. This paper selectively reviews the voluminous literature on oil price determinants since the early 1970s. It concludes that most researchers attribute the long-run oil price path to fundamental factors such as economic growth, resource depletion, technical advancements in both oil supply and demand, and the market organization of major oil petroleum exporting countries (OPEC). Short-run price movements are more difficult to explain. Many researchers attribute short-run price movements to fundamental supply and demand factors in a market with very little quantity response to price changes. Nevertheless, there appears to be some evidence of occasional financial bubbles particularly in months leading up to the financial collapse in 2008. These conflicting stories will not be properly integrated without a meeting of the minds between financial and energy economists.

We study the identification of oil shocks in a structural vector autoregressive (SVAR) model of the oil market. First, we show that the cross-equation restrictions of a SVAR impose a nonlinear relation between the short-run price elasticities of oil supply and oil demand. This relation implies that seemingly plausible restrictions on oil supply elasticity may map into implausible values of the oil demand elasticity, and vice versa. Second, we propose an identification scheme that restricts these elasticities by minimizing the distance between the elasticities allowed by the SVAR and target values that we construct from a survey of relevant studies. Third, we use the identified SVAR to analyze sources and consequences of movements in oil prices. We find that (1) oil supply shocks and global demand shocks explain 50 and 35 percent of oil price fluctuations, respectively;

There has been a well-known relationship between macro financial fundamentals and oil prices, yet there is also ample evidence that this relationship weakens during some periods. In this paper, we investigated whether the relationship between oil and macro financial fundamentals vary depending on gold price of oil. To achieve this, a Markov model is implemented to the monthly data for the period 1974-2010. In the Markov model utilized in this paper, transition probabilities are endogenous and governed by the volatilities of oil, gold, stock market and exchange rate. This allowed us to endogenously model the switching process. Our results provide evidence that the link between oil price and macro financial fundamentals disappears in the periods of inexpensive gold price of oil. Our findings also provide evidence that the volatilities of the variables matter only when gold price of oil is inexpensive.

Phys Rev E (Rapid …, 2007

We describe a method for analyzing a nonstationary stochastic process x(t), and utilize it to study the fluctuations in the oil price. Evidence is presented that the fluctuations in the returns y(t), defined as, y(t) = ln{x(t+1)/x(t)}, where x(t) is the datum at time t, constitute a Markov process, characterized by a Markov time scale t M . We compute the coefficients of the Kramers-Moyal expansion for the probability distribution function P (y, t|y 0 , t 0 ), and show that P (y, t|, y 0 , t 0 ) satisfies a Fokker-Planck equation, which is equivalent to a Langevin equation for y(t). The Langevin equation provides quantitative predictions for the oil price over Markov time scale t M . Also studied is the average frequency of positive-slope crossings, ν + α = P (y i > α, y i−1 < α), for the returns y(t), where T (α) = 1/ν + α is the average waiting time for observing y(t) = α again. The method described is applicable to a wide variety of nonstationary stochastic processes which, unlike many of the previous methods, does not require the data to have any scaling feature. PACS numbers(s): 05.10. Gg, 05.45.Tp