Speculation in the Oil Market

The run‐up in oil prices since 2004 coincided with growing investment in commodity markets and increased price co‐movement among different commodities. We assess whether speculation in the oil market played a role in driving this salient empirical pattern. We identify oil shocks from a large dataset using a dynamic factor model. This method is motivated by the fact that a small‐scale vector autoregression is not informationally sufficient to identify the shocks. The main results are as follows. (i) While global demand shocks account for the largest share of oil price fluctuations, speculative shocks are the second most important driver. (ii) The increase in oil prices over the last decade is mainly driven by the strength of global demand. However, speculation played a significant role in the oil price increase between 2004 and 2008 and its subsequent collapse. (iii) The co‐movement between oil prices and the prices of other commodities is mainly explained by global demand shocks. Our results support the view that the recent oil price increase is mainly driven by the strength of global demand but that the financialization process of commodity markets also played a role. Copyright © 2014 John Wiley & Sons, Ltd.     

Oil structural shocks,bank-level characteristics,and systemic risk: Evidence from dual banking systems

By performing a structural VAR analysis on oil price shocks, we provide an evidence on how the origins of oil price shocks impact the risk level of banks in oil-exporting countries and whether bank-level characteristics can influence the sensitivity of risk to oil shocks. When conducting panel regression analysis, we document the following findings. First, not all shocks have the same effect on bank risk. Due to oil supply shocks, the increase in oil price raises bank risk, whereas the similar increase in price due to economic expansion or oil-market specific demand reduces that risk. Second, the business model (whether the bank is Islamic or conventional), size, income diversification, profitability, and financial leverage influence the bank risk exposure to oil shocks differently. Third, the two major recent crises (global financial crises and COVID-19 pandemic) magnified bank risk exposure to oil supply shocks and speculative oil demand shocks. Overall, the structural oil shocks explain a large fraction of the variation in financial stability in GCC countries.     

Oil Prices and Personal Consumption Expenditures: Does the Source of the Shock Matter?

This paper studies the effect of structural oil shocks on personal consumption expenditures (PCE). First, we estimate a nonlinear simultaneous equation model, compute impulse responses by Monte Carlo integration, and conduct a test of the symmetry of the impulse response functions. We find that aggregate PCE responds asymmetrically to positive and negative oil‐specific demand shocks. Second, we find that aggregate PCE responds negatively to positive oil demand shocks, while adverse oil supply shocks are of limited effect. Third, we find important heterogeneity in the magnitude, sign and timing of the disaggregate PCE responses to structural shocks in the crude oil market. Our results clearly indicate that the response of PCE to an unexpected oil price increase depends on the source of the oil price shock. Our findings are robust to different nonlinear transformations for the real price of oil.     

House price synchronization across the US states: The role of structural oil shocks

This paper analyzes the impact of disentangled oil shocks on the synchronization in housing price movements across all the US states plus DC. Using a Bayesian dynamic factor model, the house price movements are decomposed into national, regional, and state-specific factors. We then study the impact of oil-specific supply and demand, inventory accumulation, and global demand shocks on the national factor using linear and nonlinear local projection methods. The impulse response analyses suggest that oil-specific supply and consumption demand shocks are most important in driving the national factor. Moreover, as observed from the regime-specific local projection model, these two shocks are found to have a relatively stronger impact in a bearish rather than a bullish national housing market. Our results have important policy implications.     

Oil price shocks and credit spread: Structural effect and dynamic spillover

This study analyzes the heterogeneous response of U.S. credit spread to global oil price shocks by building an extended structural vector autoregressive model (SVAR), which can distinguish among the U.S. and non-US oil supply shocks, aggregated demand shocks and oil market-specific demand shocks behind the real oil prices. Meanwhile, a spillover index model developed by Diebold and Yilmaz (2012) (hereafter D.Y. (2012)) is used to estimate the link between oil price shocks and the U.S. credit spread over time. The results show that (i) the credit spread does not respond to global oil supply shocks and non-US oil supply shocks, but has a negative reaction to the U.S. oil supply shocks, aggregate demand shocks, and oil-market-specific demand shocks. (ii) There exists a close connectedness between oil price shocks and the U.S. credit spread, and the link fluctuates cyclically and relates to the economic cycle and the U.S. shale oil revolution. (iii) The spillover from different oil price shocks to the U.S. credit spread shows significant heterogeneity over time. Our findings suggest that policymakers and investors can better track the U.S. credit spread changes using oil price information.     

THE ROLE OF TIME‐VARYING PRICE ELASTICITIES IN ACCOUNTING FOR VOLATILITY CHANGES IN THE CRUDE OIL MARKET

There has been a systematic increase in the volatility of the real price of crude oil since 1986, followed by a decline in the volatility of oil production since the early 1990s. We explore reasons for this evolution. We show that a likely explanation of this empirical fact is that both the short‐run price elasticities of oil demand and of oil supply have declined considerably since the second half of the 1980s. This implies that small disturbances on either side of the oil market can generate large price responses without large quantity movements, which helps explain the latest run‐up and subsequent collapse in the price of oil. Our analysis suggests that the variability of oil demand and supply shocks actually has decreased in the more recent past, preventing even larger oil price fluctuations than observed in the data. Copyright © 2012 John Wiley & Sons, Ltd.     

The shale oil revolution and the global oil supply curve

We focus on the implications of the shale oil boom for the global supply of oil. In order to derive testable implications, we introduce a simple stylized model with two producers, one facing low production costs and one higher production costs but potentially lower adjustment costs, competing à la Stackelberg. We find that the supply function is flatter for the high cost producer and that the supply function for shale oil producers becomes more responsive to demand shocks when adjustment costs decline. On the empirical side, we apply an instrumental variable approach using estimates of demand-driven oil price changes derived from a standard structural VAR of the oil market. A main finding is that global oil supply is rather vertical, with a short-term elasticity around 0.05. A rolling sample reveals that the shale oil boom does not appear to have fundamentally changed the contours of global oil production, but there is evidence for the oil supply curve to become more vertical in Saudi Arabia and more price responsive in the United States.     

Crude oil price dynamics with crash risk under fundamental shocks

Our paper presents a crude oil price model in which the price is confined in a wide moving band. A price crash occurs when the price breaches the lower boundary where a smooth-pasting condition is imposed. Using an asymmetric mean-reverting fundamental (supply/demand) shock, the solution derived from the oil price equation for the model shows the oil price follows a mean-reverting square-root process, which is quasi-bounded at the boundary. The oil price dynamics generates left-skewed price distributions consistent with empirical observations. A weakened mean-reverting force for the price increases the probability leakage for the price across the boundary and the risk of a price crash. The empirical results show the oil price dynamics can be calibrated according to the model, where the mean reversion of the price dynamics is positively co-integrated with the oil production reaction to negative demand shocks, and with the risk reversals of the commodity currencies, the Canadian dollar and the Australian dollar in currency option markets. The results are consistent with an increased price crash risk with negative demand shocks and negative risk reversals. The forecasting performance of the oil price model is better than the futures-spread models and random walk models during the crash periods. While the price of oil was above the lower boundary for most of the time, the conditions for breaching the boundary were met in 2008 and 2014 when the price fell sharply.     

The shale revolution and shifting crude dynamics

Oil price fluctuates in response to both demand and supply shocks. This paper proposes a new methodology that allows for timely identification of the shifting contribution from the two types of shock through a joint analysis of crude futures options and stock index options. Historical analysis shows that crude oil price movements are dominated by supply shocks from 2004 to 2008, but demand shocks have become much more dominant since then. The large demand shock following the 2008 financial crisis contributes to the start of this dynamics shift, whereas the subsequent shale revolution has fundamentally altered the crude supply behavior.     

The effects of oil price shocks on inflation in the G7 countries

This study examines the effects of three types of oil price shocks on inflation in the G7 countries with a new method of isolating oil price shocks. Based on monthly data from January 1997 to January 2019, we find that each oil price shock has the largest effect on U.S. inflation among the G7 countries and each country’s response to oil price shocks is different. Moreover, a rolling-window analysis shows that supply shocks, demand shocks and risk shocks have dynamic effects on inflation. The effect of supply shocks on inflation is strong before the financial crisis, but weakens during the crisis. However, the effect of demand shocks increases sharply in this time. The effect of risk shocks mainly occurs during the financial crisis and the European debt crisis. In addition, this study uses two ways to verify the robustness of the results. Our empirical results have important implications for policymakers and manufacturers, since the results provide a good explanation for the response of inflation in the G7 countries to the oil price shocks from different sources.     

Macroeconomic responses of emerging market economies to oil price shocks: An analysis by region and resource profile

We analyze the impact of oil price shocks on the macroeconomic fundamentals in emerging economies in three regions that have different resource endowments. The existing literature on emerging economies remains inconclusive on how regional factors and resource characteristics affect the response of macroeconomic variables to oil price shocks. We show that (1) exports in Europe and Central Asia are driven by oil more than East Asia and the Pacific and that (2) policy makers in East Asia and the Pacific should be concerned about real exchange appreciation following a positive oil shock to mitigate losses in the non-oil export market. Analysis by resource endowment further reveals that, in less-resource-intensive economies, an oil price shock causes large variations in consumption and has a negative and persistent impact on the real gross domestic product (GDP). In mineral-exporting economies, real GDP and interest rates are driven largely by oil price shocks. The response of real GDP in mineral-exporting economies is short lived. In oil-exporting economies, only real GDP has a large variation in response to oil price shocks. Our findings highlight the need for customized policy responses to oil price shocks, depending on resource endowments, as we show that a “one size fits all" policy does not exist.     

Macroeconomic Effects of Precautionary Demand for Oil

We evaluate the macroeconomic effects of shocks specific to the oil market, which mainly reflect fluctuations in precautionary demand for oil driven by uncertainty about future supplies. A two‐stage identification procedure is used. First, daily changes in the futures–spot price spread proxy for precautionary demand shocks and the path of oil prices is estimated. This information is then exploited to restrict the oil price response in a vector autoregression. Impulse responses suggest that such shocks reduce output and raise prices. Historical decomposition shows that they contributed significantly to the US recessions in the 1990s and in the early 2000s, but not to the most recent slump. Copyright © 2014 John Wiley & Sons, Ltd.     

What Drives Oil Prices? Emerging Versus Developed Economies

We explore the role of demand from emerging and developed economies as drivers of the real price of oil. Using a FAVAR model that identifies shocks from different regions of the world, we find that demand from emerging economies (most notably from Asian countries) is more than twice as important as demand from developed countries in accounting for the fluctuations in the real oil price and in oil production. Furthermore, geographical regions respond differently to adverse oil market shocks that drive up oil prices, with Europe and North America being more negatively affected than countries in Asia and South America. Copyright © 2014 John Wiley & Sons, Ltd.     

DISENTANGLING DEMAND AND SUPPLY SHOCKS IN THE CRUDE OIL MARKET: HOW TO CHECK SIGN RESTRICTIONS IN STRUCTURAL VARS

Sign restrictions have become increasingly popular for identifying shocks in structural vector autoregressive (SVAR) models. So far there are no techniques for validating the shocks identified via such restrictions. Although in an ideal setting the sign restrictions specify shocks of interest, sign restrictions may be invalidated by measurement errors, data adjustments or omitted variables. We model changes in the volatility of the shocks via a Markov switching (MS) mechanism and use this device to give the data a chance to object to sign restrictions. The approach is illustrated by considering a small model for the market of crude oil. Earlier findings that oil supply shocks explain only a very small fraction of movements in the price of oil are confirmed and it is found that the importance of aggregate demand shocks for oil price movements has declined since the mid 1980s. Copyright © 2013 John Wiley & Sons, Ltd.     

Macroeconomic Uncertainty and Oil Price Volatility

This paper shows that higher macroeconomic uncertainty causes higher oil price volatility. Regimes of low and high uncertainty are identified in a threshold VAR model in which the effects of structural oil demand and supply shocks are estimated. The results show that higher macroeconomic uncertainty, as measured by global industrial production volatility, significantly increases the sensitivity of oil prices to shocks in oil demand and supply. This occurs as uncertainty lowers the price elasticity of oil demand and supply. The difference in the estimated oil price elasticities is economically meaningful as the price impact of a similar change in oil production might double when it hits the economy in uncertain times. As such, varying uncertainty can explain why oil price volatility is typically higher during periods such as financial crises and recessions, and why oil price volatility changes over time more generally.     

Large time‐varying parameter VARs: A nonparametric approach

In this paper we introduce a nonparametric estimation method for a large Vector Autoregression (VAR) with time‐varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and Factor Augmented VARs. When applied to the problem of forecasting key macroeconomic variables, the method outperforms constant parameter benchmarks and compares well with large (parametric) Bayesian VARs with time‐varying parameters. The tool can also be used for structural analysis. As an example, we study the time‐varying effects of oil price shocks on sectoral U.S. industrial output. According to our results, the increased role of global demand in shaping oil price fluctuations largely explains the diminished recessionary effects of global energy price increases.     

Interpreting shocks to the relative price of investment with a two-sector model

Consumption and investment comove over the business cycle in response to shocks that permanently move the price of investment. The interpretation of these shocks has relied on standard one-sector models or on models with two or more sectors that can be aggregated. We show that the same interpretation can also be motivated with a model that captures key features of the US Input–Output Tables and cannot be aggregated into a standard one-sector model. Our alternative model yields a closer match to the empirical evidence of positive comovement for consumption and investment subject shocks that permanently move the price of investment.     

The Role of Sustainability‐Related Strategies on the Biofuel Industry: Trends,Prospects and Challenges

The aim of this study is to elucidate the sustainability‐related strategies on the biofuel industry. Our empirical analysis is based on a time series data set covering diesel demand in Greece over the period 1978–2014 and on the basis of these estimates we make forecasts for biodiesel consumption in the coming years (2015–2030) under three alternative scenarios. Our approach utilizes unit root testing to investigate possible co‐integrated relationships among the sample variables. The empirical findings indicate that diesel demand is income and price inelastic in both the long and the short run, while biodiesel demand seems to have an upward trend over the simulated period. We argue that the importance of biofuel in the Greek energy balance will change the form of the existing business strategies towards issues such as sustainability, green entrepreneurship and corporate social responsibility to achieve the environmental goals set by the EU Energy Roadmap 2050. Copyright © 2018 John Wiley & Sons, Ltd and ERP Environment     

The nonlinear effect of oil price shocks on financial stress: Evidence from China

This paper examines the nonlinear effects of different types of oil price shocks on China’s financial stress index (FSI). For this purpose, we use newly proposed framework by Ready (2018) to decompose oil prices into supply, demand and risk shocks. Then, we use a Markov regime-switching (MRS) model to investigate the nonlinear effects of these oil price shocks on China’s FSI. The empirical results show that the effects of three oil price shocks are nonlinear under different regimes. In particular, oil supply shocks mainly have a significantly positive effect on China’s FSI in the low-volatility state; demand shocks have negative effects on China’s FSI in different regimes, but this effect is larger in the low-volatility state; the effect of risk shocks on China’s FSI is the opposite, and it is positive in the high-volatility state but negative in the low-volatility state.     

Inference on impulse response functions in structural VAR models

Skepticism toward traditional identifying assumptions based on exclusion restrictions has led to a surge in the use of structural VAR models in which structural shocks are identified by restricting the sign of the responses of selected macroeconomic aggregates to these shocks. Researchers commonly report the vector of pointwise posterior medians of the impulse responses as a measure of central tendency of the estimated response functions, along with pointwise 68% posterior error bands. It can be shown that this approach cannot be used to characterize the central tendency of the structural impulse response functions. We propose an alternative method of summarizing the evidence from sign-identified VAR models designed to enhance their practical usefulness. Our objective is to characterize the most likely admissible model(s) within the set of structural VAR models that satisfy the sign restrictions. We show how the set of most likely structural response functions can be computed from the posterior mode of the joint distribution of admissible models both in the fully identified and in the partially identified case, and we propose a highest-posterior density credible set that characterizes the joint uncertainty about this set. Our approach can also be used to resolve the long-standing problem of how to conduct joint inference on sets of structural impulse response functions in exactly identified VAR models. We illustrate the differences between our approach and the traditional approach for the analysis of the effects of monetary policy shocks and of the effects of oil demand and oil supply shocks.