The long-run oil–natural gas price relationship and the shale gas revolution

The gas extraction technological developments of the 2000s have allowed shale gas production, which in the US has become a significant part of the total gas production. Such a significant change might have affected the long-run relationship between oil and natural gas prices postulated by several authors. By using monthly data of oil and gas prices, as well as gas quantities from 1997 to 2013, we test for the presence of a long-run relationship, allowing also for possible breaks. We first show the stationarity of gas quantity data before the production of shale gas and the existence of a break in the trend (and in the intercept) on the integrated gas price time series, by the time shale gas enters the market. Then, applying a Vector Error Correction Model, we show that shale gas production has affected the relationship across variables. Gas quantities become relevant in the formation of gas prices after the beginning of shale gas production, while impact of oil prices on the gas ones doubles. However, on the basis of the available data, it is not unequivocally possible to assess whether or not a new long-run relationship between oil and gas has been established.     

Interpreting the oil risk premium: Do oil price shocks matter?

This paper analyzes the link between the economic fundamentals of the global crude oil markets and the oil futures risk premium. The compensation for risk required by speculators in the oil futures market is modelled as part of the endogenous transmission of oil price shocks. The empirical approach is based on a Structural Vector Autoregressive model of the international market for crude oil. The dynamic response functions show a negative relationship between the risk premium and the real price of oil, triggered by shocks to economic fundamentals. Moreover, the expected returns of a long futures investment are largely explained by a specific shock component related to oil speculators and a shift in the global demand for crude oil.     

Do all clean energy stocks respond homogeneously to oil price?

This paper investigates whether the relationship between oil price and clean energy stock is homogeneous across sub-sectors of the clean energy stock market and its implications for portfolio diversification and clean energy finance policy. We contribute to the literature by being the first empirical paper to document the oil price-clean energy stock relationship at a disaggregate level, thereby providing a more detailed picture of the clean energy stock market. Our findings show that the relationship between oil price and clean energy stock varies largely across clean energy stock sub-sectors. Specifically, biofuel and energy management stocks are the most connected to oil price, while wind, geothermal, fuel cell stocks are among the least connected to oil price. This implies that the hedging cost and effectiveness of a clean energy investment portfolio is dependent on the type of clean energy stock included, therefore, active portfolio management at a disaggregate level is of particular importance. Additionally, policy should take into account the specific characteristics of individual clean energy sub-sectors in order to effectively promote clean energy investment.     

What drives the commodity price beta of oil industry stocks?

We test theoretical drivers of the oil price beta of oil industry stocks. The strongest statistical and economic support comes for market conditions-type variables as the prime drivers: namely, oil price (+), bond rate (+), volatility of oil returns (−) and cost of carry (+). Though statistically significant, exogenous firm characteristics and oil firms' financing decisions have less compelling economic significance. There is weaker support for the prediction that financial risk management reduces the exposure of oil stocks to crude oil price variation. Finally, extended modelling shows that mean reversion in oil prices also helps explain cross-sectional variation in the oil beta.     

How is volatility in commodity markets linked to oil price shocks?

This study investigates the effects of oil price shocks on volatility of agricultural and metal commodities. We decompose an oil price shock to its underlying components, including macroeconomics and oil specific shocks. The applied method is the structural vector autoregressive (SVAR) model and the time span is from April 1983 to May 2014. The investigation is divided into two subsamples, before and after May 2006 for agriculture taking into account the 2006–2008 food crisis and change in U.S. ethanol production policy, and before and after January 2008 for metals considering the recent global financial crisis. We find that, based on impulse response functions, the response of volatility of each commodity to an oil price shock differs significantly depending on the underlying cause of the shock for the both periods. Moreover, according to variance decomposition the explanatory power of oil shocks becomes stronger after the crisis. The different responses of commodities are described in detail by investigating market characteristics in each period.     

What is behind the increase in oil prices? Analyzing oil consumption and supply relationship with oil price

The continuing increases in oil prices have renewed the argument over the real culprits behind these movements. The growth in demand for oil in international markets, especially from the United States and China, is often identified as the main source of consumption pressure on prices, and thus the upward trend in oil prices. This paper uses unit root tests with two endogenous breaks to analyze the characteristics of oil prices, production, and consumption for several countries. By taking into account structural breaks, we find that many countries’ oil consumption and oil prices are stationary, while other countries are not. We also perform causality tests to determine the direction of any possible relationship between oil price and oil consumption and production. Our statistical analysis reveals that production variables cause oil prices, while oil prices tend to cause consumption. As a result, we claim that the blame for the recent fluctuations in oil prices is more appropriately associated with supply factors, not consumption influences.     

Is shale development drilling holes in the human capital pipeline?

Using the Synthetic Control Method (SCM) and a novel method for measuring changes in educational attainment we examine the link between educational attainment and shale oil and gas extraction for the states of Montana, North Dakota, and West Virginia. The three states examined are economically-small, relatively more rural, and have high levels of shale oil and gas reserves. They also are varied in that West Virginia is intensive in shale gas extraction, while the other two are intensive in shale oil extraction. We find significant reductions in high school and college attainment among all three states' initial residents because of the shale booms.     

How do oil price shocks affect the output volatility of the U.S. energy mining industry? The roles of structural oil price shocks

This paper focuses on how explicit structural shocks that characterize the endogenous character of international oil price change affect the output volatility of the U.S. crude oil and natural gas mining industries. To this end, we employ a modified structural vector autoregressive model (SVAR) to decompose real oil-price changes into four components: U.S. supply shocks, non-U.S. supply shocks, aggregate demand shocks, and oil-specific demand shocks mainly driven by precautionary demand. The results indicate that output volatility of the U.S. crude oil and natural gas mining industry has significantly negative responses to U.S. supply shocks, aggregate demand shocks, and oil-specific demand shocks, while lacks significant response to non-U.S. supply shocks. Variance decomposition and historical decomposition confirm that U.S. supply shocks occupy most explaining variations in output volatility among the four structural oil shocks. Moreover, the oil-specific demand shocks explain more variation than that of aggregate demand shocks for the crude oil mining industry, but the opposite is true for the natural gas mining industry.     

Examining crude oil price – Exchange rate nexus for India during the period of extreme oil price volatility

This study probes crude oil price – exchange rate nexus for India using daily data for the time span July 2, 2007–November 28, 2008. Generalized autoregressive conditional heteroskedasticity (GARCH) and exponential GARCH (EGARCH) models have been employed to examine the impact of oil price shocks on nominal exchange rate. The study reveals that an increase in the oil price return leads to the depreciation of Indian currency vis-à-vis US dollar. The study also establishes that positive and negative oil price shocks have similar effects, in terms of magnitude, on exchange rate volatility and oil price shocks have permanent effect on exchange rate volatility.     

Can stale oil price news predict stock returns?

We hand-collect time-series data on positive and negative oil price news from 100 news sources from around the world, covering 59,129 news articles on oil prices. Using time-series predictive regression models estimated for 45 countries, we show that: (a) positive and negative news predict stock returns for at most 12 countries for which the oil price does not predict returns; and (b) together the three oil price measures predict returns for at most 23/45 countries. Therefore, oil price news turns out to be more powerful in predicting returns in a horserace with oil price. We show that the ability of oil to predict returns is through the discount rate and cash flow channels. Our results survive a battery of robustness tests.     

Which risk factors drive oil futures price curves?

We develop extensions that introduce regression structure to the multi-factor stochastic models of commodity futures price term structure dynamics. We demonstrate the accuracy with which these models can be calibrated to oil futures data and how they improve on existing models both in model fit and in model interpretation. We found leading observable factors that contribute to explaining the term structure of oil futures, in the presence of long and short term stochastic factors, included the dollar index, inventories, commodity indices and risk aversion associated to financial intermediaries. Furthermore, we determine the time frame on which these factors are explanatory.     

Slow oil shocks and the “weakening of the oil price–macroeconomy relationship”

Many papers have been documenting and analysing the asymmetry and the weakening of the oil price–macroeconomy relationship as off the early eighties. While there seems to be a consensus about the factors causing the asymmetry, namely adjustment costs which offset the benefits of low energy prices, the debate about the weakening of the relationship is not over yet. Moreover, the alternative oil price specifications which have been proposed by Mork (1989), Lee et al. (1995), and Hamilton (1996) to restore the stability of the relationship fail to Granger cause output or unemployment in post-1980 data. By using the concept of accelerations of the oil price, we show that the weakening of this relationship corresponds to the appearance of slow oil price increases, which have less impact on the economy. When filtering out these slow oil price variations from the sample, we manage to rehabilitate the causality running from the oil price to the macroeconomy and show that far from weakening, the oil price accelerations–GDP relationship has even been growing stronger since the early eighties.     

Does oil price affect the value of firms? Evidence from Tunisian listed firms

A new debate on the potential impact of oil price changes on the value of firms was initiated in this paper. Using a stochastic frontier approach, an attempt was made to derive the optimal value Q* of firms and calculate the Q value observed. Then the shortfall (Q*–Q) which represents the inefficiency term was explained. Starting from 19 industrial Tunisian firms listed on the Tunis Stock Exchange between 2007 and 2011, the fact that variation of oil prices can largely explain distortions in the value of firms was empirically demonstrated.     

How does oil price volatility affect non-energy commodity markets?

The influence of price volatility in the crude oil market is expanding to non-energy commodity markets. With the substitution of fossil fuels by biofuel and hedge strategies against inflation induced by high oil prices, the link between crude oil market and agriculture markets and metal markets has increased. This study measures the influence of the crude oil market on non-energy commodity markets before and after the 2008 financial crisis. By introducing the US dollar index as exogenous shocks, we investigate price and volatility spillover between commodity markets by constructing a bivariate EGARCH model with time-varying correlation construction. The results reveal that the crude oil market has significant volatility spillover effects on non-energy commodity markets, which demonstrates its core position among commodity markets. The overall level of correlation strengthened after the crisis, which indicates that the consistency of market price trends was enhanced affected by economic recession. In addition, the influence of the US dollar index on commodity markets has weakened since the crisis.     

Dynamic structure of the spot price of crude oil: does time aggregation matter?

This paper assesses nonlinear structures in the time series data generating mechanism of crude oil prices. We apply well-known univariate tests for nonlinearity, with distinct power functions over alternatives, but with different null hypotheses reflecting the existence of different concepts of linearity and nonlinearity in the time series literature. We utilize daily data on crude oil spot price for over 26 years, as well as monthly data on crude oil spot price for 41 years. Investigating the monthly price of crude oil along with the daily price distinguishes the approach of this paper from existing studies focusing on the time series structure of crude oil price. All the tests detect strong evidence of general nonlinear serial dependence, as well as nonlinearity in the mean, variance, and skewness functions in the daily spot price of crude oil. Since evidence of nonlinear dependence is less dramatic in monthly observations, nonlinear serial dependence is moderated by time aggregation in crude oil prices but not significantly.     

Explaining the price of oil 1971–2014 : The need to use reliable data on oil discovery and to account for ‘mid-point’ peak

This paper explains, in broad terms, the price of oil from 1971 to 2014 and focuses on the large price increases after 1973 and 2004. The explanation for these increases includes the quantity of conventional oil (i.e. oil in fields) discovered, combined with the decline in production of this oil that occurs typically once ‘mid-point’ is passed. Many past explanations of oil price have overlooked these two constraints, and hence provided insufficient explanations of oil price. Reliable data on conventional oil discovery cannot come from public-domain proved (‘1P’) oil reserves, as such data are very misleading. Instead oil industry backdated proved-plus-probable (‘2P’) data must be used. It is recognised that accessing 2P data can be expensive, or difficult. The ‘mid-point’ peak of conventional oil production results from a region's field-size distribution, its fall-off in oil discovery, and the physics of field decline. In terms of the future price of oil, estimates of the global recoverable resource of conventional oil show that the oil price will remain high on average, unless dramatic changes occur in the volume of production and cost of non-conventional oils, or if the overall demand for oil were to decline. The paper concludes with policy recommendations.     

Has oil price predicted stock returns for over a century?

This paper contributes to the debate on the role of oil prices in predicting stock returns. The novelty of the paper is that it considers monthly time-series historical data that span over 150 years (1859:10–2013:12) and applies a predictive regression model that accommodates three salient features of the data, namely, a persistent and endogenous oil price, and model heteroscedasticity. Three key findings are unraveled: first, oil price predicts US stock returns. Second, in-sample evidence is corroborated by out-sample evidence of predictability. Third, both positive and negative oil price changes are important predictors of US stock returns, with negative changes relatively more important. Our results are robust to the use of different estimators and choice of in-sample periods.     

Are the macroeconomic effects of oil price shock symmetric?: A Factor-Augmented Vector Autoregressive approach

This paper aims to examine the asymmetric effect of oil price shocks on real economic activity in the U.S. within the context of a nonlinear Factor-Augmented Vector Autoregressive (FAVAR) model. By employing simulation methods, we trace the effects of positive and negative oil price shocks on the macroeconomic variables through the Impulse Response Function (IRF). It is found that the negative impacts of higher oil prices are larger than the positive effects of lower oil prices. And the asymmetric effects are more evident when the oil price shocks are larger. The results are robust to different lag specification and choice of factors.     

How do oil producers respond to giant oil field discoveries?

The classical Hotelling model predicts that the optimal extraction level rises immediately after an unexpected resource discovery, whereas, in reality, there are substantial adjustment costs in petroleum production and an average lag of several years between a discovery and the start of production. Using a large panel of country-level production data and a difference-in-differences identification approach, I show that domestic production levels respond before a newly found oil field comes on line and that this increase is driven by non-OPEC producers, consistent with different responses of OPEC and non-OPEC drilling activity. Offshore fields and exceptionally large “super-” or “mega-giant” fields are also more likely to raise country-level production. Given that domestic petroleum consumption rises by less in response to a discovery, at least part of the increase in production seems to go into (net) oil exports.     

Reviewing the oil price–GDP growth relationship: A replication study

This paper presents a thorough replication of Hamilton (2003) which in turn replicates and extends the findings of four seminal papers regarding the oil price–GDP growth relationship. Firstly, we replicate the empirical results obtained with the oil price measures of Hamilton (1983), Mork (1989), Lee et al. (1995), Hamilton (1996), and Hamilton (2003) by using an identical data set of real and nominal oil prices. Secondly, we extend the data sets to 2019Q4 and apply the same methodology. We find that for more recent data the explanatory power of the proposed oil price measures on GDP growth rates is still present, albeit on a slightly weaker magnitude. Extending the ARX(4), we only find little evidence that oil price decreases impact GDP growth rates on the full data set. Parameter stability tests suggest that the impact of oil price decreases might be limited to certain periods.