Behavior Of Oil Prices Research Articles

Diversifying away the risk of war and cross-border political crisis

This paper investigates the behavior of crude oil prices, government bonds, and stock market indices around outbreaks of severe international crises and wars. Using a constant mean return event study, we show that these events are associated with positive and significant abnormal returns on oil and bonds, which means that these two asset classes can potentially shelter shareholders from plummeting equity values during international crises. A formal safe haven analysis confirms this insight. Such price movements may reflect a reallocation of funds across asset classes in response to the events, as well as shifts in the demand for oil due to precautionary, speculative, and military motives. We also calculate the weights for optimal portfolios, which could provide insurance against conflict risk.

Nonlinearity and fractional integration in the world crude oil prices

ABSTRACTThis article investigates the time-series behavior of the world crude oil prices over the period from January 1997 to May 2011 by taking into account nonlinearity and fractional integration in the same framework. As a first step, the author examines the nonlinear behavior of the data through the estimation of a two-regime threshold autoregressive model and find that world crude oil prices exhibit a nonlinear behavior. After finding nonlinearity, the author also allows disturbances to be fractionally integrated. The results point to a stationary process with long memory.

Investigating the Behavior of Iran's Light Crude Oil Price in Short Term

<p class="zhengwen">Investigate the causes of changing the oil price and modeling for predicting its volatility has always been one of the most important fields of Iran's economic literature study due to its position in Iran's economy. On the other hand, oil price volatility lead to the difficulty in the development programs. Empirical studies show that oil prices volatility are caused the structural bottlenecks (trade balance bottleneck, budget bottlenecks, etc.) in Iran's economic.<strong></strong></p>Understanding the mechanism of oil prices formation can reduce the risk of oil price volatility and its negative impacts on Iran's economy. With the development of oil bourse and oil futures market, oil market changed the crude oil price formation so that the cash flow between financial markets and oil market will deviant the crude oil price from its long term direction by changing in interest rate in short-term. In this paper, it is investigated the crude oil price deviation from its long-term direction with regard to the relationship between mentioned markets in short-term. For this purpose, Fisher price jump model and Frankel theory will be used for test by using daily time series data of 2005-13 about Iran's light crude oil in different areas (different markets), as well as multivariate GARCH technique method. Also, the results show that the pricing strategy is false signal in the use of Urals crude oil in the determining of crude oil price in the Mediterranean and North West Europe markets.

Operating Activities of Energy Companies in the Context of Crude Oil Risk and its Impact on the Polish Power Exchange

Different factors have significant impact on the development of the Polish Power Exchange and the volatility of electricity contracts prices, which are quoted on this part of energy market. The electricity prices are strongly influenced by such determinants as: the level of turnover volume, fuel prices, weather conditions, the CO2 emission allowance prices, political situation, the changeability of the energy demand level. The purpose of this paper is to examine and compare the volatility behaviour of world crude oil prices and electricity prices established on the Polish Power Exchange (POLPX) in the turbulent period, which covers the subprime financial crisis, geopolitical crisis, and changes in the rules of the European Union's energy policy. The background for the empirical research is a comparative analysis of business strategies of Polish energy companies and their adaptation to the factors that in the significant way influence the development of energy sector in Poland.

Structure Matters: Oil Markets Enter the Adelman Era

The 2014/2015 oil price collapse surprised the many economists who have published brilliant econometric explanations of oil price behavior. The sharp decline would not have caught Morris Adelman unawares. Professor Adelman’s lifelong research focused on the link between market structure and price behavior. His seminal work on industry structure, beginning with grocery retailer A&P, has provided a framework that can be used to explain oil price fluctuations that have occurred over the past four decades. His approach may not be as accurate as the elegant econometric models that dominate today’s literature but it does have one clear advantage: it provides far greater clarity on the way forward.

Do oil spot and futures prices move together?

This paper investigates the time-varying correlation and the causal relationship between crude oil spot and futures prices using a newly developed approach — wavelet coherency analysis in time–frequency domain. First, we find evidence of a long-run cointegration relationship between oil spot and futures prices. Moreover, the short-run causality is more significant in shorter maturity pairs versus longer maturity pairs in the vector error correction framework. Second, the results from wavelet coherency analysis show significant dynamic correlations between variables in the time–frequency domain. Third, the illustration of the phase-difference series around zero suggests that spot and futures prices contribute to the dynamics of the long-run equilibrium. Fourth and finally, we provide reasons for the structural changes in oil prices and also recommend investment strategies corresponding to risk diversification. Future studies focusing on the behavior of oil prices should consider the characteristics of the time–frequency space and lead–lag dynamic relationships.

Crude Oil as a Safe Haven Asset in Times of War

This paper investigates the behavior of crude oil prices, government bonds and stock market indices around outbreaks of severe international crises and wars. Using a constant-mean-return event study, we show that these events are associated with positive and significant abnormal returns on oil and bonds, which means that these two asset classes can potentially shelter shareholders from plummeting equity values during international crises. A formal safe haven analysis confirms this insight. Such price movements may reflect a reallocation of funds across asset classes in response to the events, as well as shifts in the demand for oil due to precautionary, speculative and military motives. We also calculate the weights for optimal portfolios, which could provide insurance against conflict risk.

Oil prices and UK industry-level stock returns

In this article, we study whether the behaviour of oil prices can be used as a reliable predictor for the disaggregated industry-level stock market indices. We find strong evidence for the relevance of changes in oil price as a predictor for the returns of UK industry portfolios, while this relevance is heterogeneous across industries. In an out-of-sample framework, we find that both the contemporaneous and lagged oil price changes do predict UK industry stock market returns. The predictive power is more transient for the latter case, and mostly appearing after allowing for time variation in the relative performance. In addition, we find some evidence of asymmetry in the oil–stock price relationships.

Stochastic Models for Oil Prices and the Pricing of Futures on Oil

In this article, we investigate and compare the performance of various one-factor diffusion models in their ability to capture the behaviour of Brent crude oil prices. New proposed models, which have a three-quarters power in the diffusion term, are found to outperform all other popular models tested. Analytic solutions for futures prices under the new models are found and used to calibrate market prices. Results from the calibration show that one of the new three-quarters models with a mean-reverting property outperforms other popular models in fitting and forecasting futures prices.

Production-Based Asset Pricing and the Oil Market

What is the role of macroeconomic fluctuations and of oil supply shocks for oil prices, volatilities, and risk premia? I analyze this question within a general equilibrium asset pricing framework with an oil sector. The benchmark calibration shows that short-run macroeconomic growth shocks and oil productivity shocks account for the largest part of the volatility in the oil market and are responsible for the mean-reversion behavior of oil prices. On the other hand, long-run macroeconomic growth risks are the main driver of risk premia on oil futures and their upward-sloping term structure, which is observed in the data. The model consistently explains quantity and price dynamics in the oil sector and in the general macroeconomy, and furthermore sheds light on the intricate relationship between oil and equity returns.

Time Series Models to Obtain the Barrel Crude Oil Prices

The use of time series as an additional tool in decision making for the oil industry has been established as a mechanism for predicting the behavior of crude oil price. Especially in Brazil, after the discovery in this decade of the pre-salt reservoirs, the estimate of the price of a crude oil barrel through the use of modern techniques can minimize risks in exploration and production of oil. The more appropriate pricing for crude oil aims to minimize the risks to the economic activity for both exporters and importers of oil. This paper presents six different methods for obtaining crude oil future pricesi.e.Multiple regression (MR), Holt ́s method (HM), Holt-Winter (HW), Kalman filter (KF), Auto-Regression/Moving-Average (ARIMA) and stochastic simulation based on the use of the Monte Carlo method (SMC). The methods are compared to determine their advantages and disadvantages against each other, seeking to determine which of the generated models has the best potential to determine the future fair price of a barrel of oil. As a result, the most appropriate methodology capable of projecting a more precise future barrel oil fair price was determined, among the six alternatives studied.

Predicting oil price movements: A dynamic Artificial Neural Network approach

Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to develop a NARX model. The NARX model is trained with historical data from 1974 to 2004 and the results are verified with data from 2005 to 2009. The results show that NARX model is more accurate than time series and static ANN models in predicting oil prices in general as well as in predicting the occurrence of oil price shocks.

Non-linearities in the dynamics of oil prices

Examining stationarity is of particular importance and represents the first step in empirical time-series research. Non-stationarity invalidates many of the results obtained from standard techniques and, therefore, requires special treatment. Because oil prices play an important role in affecting economic variables, this paper examines the stationarity of real oil prices (Brent, Dubai, WTI and the World) over the period 1973:2–2011:2. Real oil prices are expressed in the currencies of seven Asian countries (Indonesia, Japan, Korea, Malaysia, the Philippines, Singapore and Thailand) and in the U.S. dollar. While using linear unit root tests without structural breaks shows no evidence of stationarity, allowing for breaks shows very limited evidence of stationarity. We argue that these results are attributed to the presence of nonlinearities in the behavior of oil prices. Testing for nonlinearity shows significant evidence of nonlinearity in all the cases with evidence of exponential smooth transition autoregression (ESTAR) nonlinearity-type in most cases. Applying unit root tests that account for two types of nonlinearities (smooth transition and nonlinear deterministic trends) reveals evidence of stationarity in all the cases.

A characterization of oil price behavior — Evidence from jump models

This paper is concerned with the statistical behavior of oil prices in two ways. It, firstly, applies a combined jump GARCH model in order to characterize the behavior of daily, weekly as well as monthly oil prices. Secondly, it relates its empirical results to implications of Hotelling-type resource extraction models. The empirical analysis shows that oil prices are characterized by GARCH as well as conditional jump behavior and that a considerable portion of the total variance is triggered by sudden extreme price movements. This finding implies that, first, oil price signals are not reliable and, as a consequence, both finding optimal extraction paths and decisions regarding the transmission to alternative technologies are likely to be compromised. Second, this behavior is in stark contrast to the notion of deterministic trends in the price of oil.

Uncertainty, expectations, and fundamentals: whatever happened to long-term oil prices?

One of the major features of the oil market during the 1990s was the relative stability of the long-term oil price. While the spot price exhibited sharp price volatility, that volatility was only partially transmitted to the back end of the futures curve which was anchored around the $20–22 per barrel range. However, as oil prices rose sharply during the boom years, the consensus on the oil price that would balance the long-term fundamentals of the oil market broke down and the whole futures curve became subject to a series of shifts. Our empirical evidence suggests that in the late 1990s and early 2000s there was limited evidence of adjustment between short-term and long-term oil prices. These dynamics, however, changed in early 2005 with the long-term price making most of the adjustment towards the prompt price. We suggest an interpretation of the long-term behaviour of oil prices based on the insights of two models. The first is based on a signal extraction mechanism and shows that when the private beliefs by investors about the long-run determinants of oil prices become less precise relative to the information contained in the current spot price, then the expected future oil price becomes closer to the current spot price. The second model is based on Bayesian updating and shows that if the variability of the spot price increases and/or if the spot price remains higher over a sustained period of time than anticipated by investors, then the probability distribution of the parameter capturing the speed of mean reversion will shift and the expected future price will move closer to the current spot price. Our analysis predicts that in the face of increased uncertainty the long-term and short-term prices are bound to exhibit similar movements. These changes have important consequences on the oil price formation process.

A Characterization of Oil Price Behavior - Evidence from Jump Models

This paper is concerned with the statistical behavior of oil prices in two ways. It, firstly, applies a combined jump GARCH in order to characterize the behavior of daily, weekly as well as monthly oil prices. Secondly, it relates its empirical results to implications of Hotelling-type resource extraction models. The empirical analysis shows that oil prices are characterized by GARCH as well as conditional jump behavior and that a considerable portion of the total variance is triggered by sudden extreme price movements. This finding implies that, first, oil price signals are not reliable and, as a consequence, both finding optimal extraction paths and decisions regarding the transmission to alternative technologies are likely to be compromised. Second, this behavior is in stark contrast to the notion of deterministic trends in the price of oil.

A geopolitical theory of oil price behaviour: an econometric evaluation

Previous work on crude oil price modelling has generally focused on two theoretical approaches, either the+ optimal control analysis of pricing of a depletable resource or Organization of the Petroleum Exporting Countries (OPEC) as a partial monopolist setting oil prices to maximize net present value. Neither has been wholly satisfactory. We consider a different perspective: a cooperative framework in which political and military factors interacted with economic considerations for oil exporters and importers to define a Target Price Zone (TPZ). We analyse several issues in this context: monthly versus annual average prices, beginning and ending dates for TPZs, degree of stability in several price series (West Texas Intermediate (WTI), Brent, etc.), Free On Board (FOB) and landed prices, real or nominal prices, OPEC behaviour and effect of the Euro exchange rate on dollar denominated oil prices. We conclude that a TPZ system was in operation from 1986 through 2003. The TPZ worked imperfectly but with a substantial degree of predictability for 18 years. In 2004, the TPZ system deteriorated for several reasons and has not been re-established. Perhaps the US government supports the Saudis because it has always believed it had a two-way relation with OPEC generally and the Persian Gulf countries in particular. We give them protection and they supply oil.1 The Bush trip [1986] came as an additional incentive to restore some stability to prices … What they [the Saudis] heard was the Vice President of the United States of America saying that the price collapse was destabilizing and threatened the security of the United States … The Saudis looked to the United States for their own security; surely, they thought in the aftermath of the Bush visit, they would have to be attentive to the security needs of the United States.2 1 See Adelman (2005). Adelman qualifies this quotation by observing that ‘OPEC countries owe us nothing’. 2 See Yergin (1992). This is Yergin's description of then Vice-President Bush's visit to Saudi Arabia in April 1986. In Yergin's view, this explains why Saudi Arabia later that year cut production, subsequently leading to higher prices.

Oil prices — Brownian motion or mean reversion? A study using a one year ahead density forecast criterion

For oil related investment appraisal, an accurate description of the evolving uncertainty in the oil price is essential. For example, when using real option theory to value an investment, a density function for the future price of oil is central to the option valuation. The literature on oil pricing offers two views. The arbitrage pricing theory literature for oil suggests geometric Brownian motion and mean reversion models. Empirically driven literature suggests ARMA–GARCH models. In addition to reflecting the volatility of the market, the density function of future prices should also incorporate the uncertainty due to price jumps, a common occurrence in the oil market. In this study, the accuracy of density forecasts for up to a year ahead is the major criterion for a comparison of a range of models of oil price behaviour, both those proposed in the literature and following from data analysis. The Kullbach Leibler information criterion is used to measure the accuracy of density forecasts. Using two crude oil price series, Brent and West Texas Intermediate (WTI) representing the US market, we demonstrate that accurate density forecasts are achievable for up to nearly two years ahead using a mixture of two Gaussians innovation processes with GARCH and no mean reversion.

Risk modeling in crude oil market: a comparison of Markov switching and GARCH models

PurposeThe purpose of this paper is to deal with the different phases of volatility behavior and the dependence of the variability of the time series on its own past, models allowing for heteroscedasticity like autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), or regime‐switching models have been suggested by reserachers. Both types of models are widely used in practice.Design/methodology/approachBoth regime‐switching models and GARCH are used in this paper to model and explain the behavior of crude oil prices in order to forecast their volatility. In regime‐switching models, the oil return volatility has a dynamic process whose mean is subject to shifts, which is governed by a two‐state first‐order Markov process.FindingsThe GARCH models are found to be very useful in modeling a unique stochastic process with conditional variance; regime‐switching models have the advantage of dividing the observed stochastic behavior of a time series into several separate phases with different underlying stochastic processes.Originality/valueThe regime‐switching models show similar goodness‐of‐fit result to GARCH modeling, while has the advantage of capturing major events affecting the oil market. Daily data of crude oil prices are used from NYMEX Crude Oil market for the period 13 February 2006 up to 21 July 2009.

Understanding Oil Price Behavior through an Analysis of a Crisis

This article seeks to identify and explain both the fundamental and the more controversial elements that shape the dynamics of the oil market, using the recent oil crisis as the basic framework for analysis. The article suggests that the price of oil is essentially a function of the current and future spare capacity of oil. However, spare capacity alone cannot fully explain the extent and timing of oil price movements. Other important factors must be taken into account, including, above all, the expectations of market operators, who tend to be influenced by both the perceived current and future level of spare capacity and the unreliable data that have plagued the oil world since its inception. The article also suggests that bottlenecks in the oil refining system may play an important role in oil price movements, but tends to downgrade the importance of other factors, such as the influence of OPEC and financial speculation. Finally, the article suggests some reforms to make the oil market more transparent and possibly more stable, which is a prerequisite for making investments in energy efficiency and renewables in the near future.