Dynamic link between oil prices and exchange rates: A non-linear approach

We explore the non-linear relationship between crude oil prices and exchange rates of major currencies from quantitative and structural perspectives, by utilizing bivariate normal mixture model. The correlation coefficients between oil prices and exchange rates demonstrate that their dependences typically start from 2004 then dynamically change over time. Then we investigate whether business cycle and oil price shocks as two possible exogenous factors affect the dependence structure of oil-FX linkage. We find significant structural heterogeneity during economic expansion while little evidence of heterogeneity in recession. This finding provides alternative interpretations for the enhanced dependence between oil prices and exchange rates and generates implications of financialization in commodity market. In terms of oil price shocks (Kilian, 2009), the normal mixture model captures significant heterogeneity, implying that unstable oil-FX dependence structure is frequently associated with oil aggregate demand shocks and oil-specific demand shocks. With an emphasis on the dynamic weights of two underlying states, we interestingly find that structural heterogeneities coincide with a variety of geopolitical and economic events. The application of normal mixture not only provide us knowledge of non-linear relationship between oil prices and exchange rates but also guidance for investors in risk management and portfolio diversification complementary to the traditional portfolio theory based on normal distribution.     

The effects of oil price shocks on output and inflation in China

Crude oil price shocks derive from many sources, each of which may bring about different effects on macro-economy variables and require completely different designs in macro-economic policy; thus, distinguishing the sources of oil price fluctuations is crucial when evaluating these effects. This paper establishes an open-economy dynamic stochastic general equilibrium (DSGE) model with two economies: China and the rest of the world. To assess the effects of oil price shocks, the CES production function is extended by adding oil as an input. Based on the model, the effects of four types of oil price fluctuations are evaluated. The four types of oil price shocks are supply shocks driven by political events in OPEC countries, other oil supply shocks, aggregate shocks to the demand for industrial commodities, and demand shocks that are specific to the crude oil market. Simulation results indicate the following: Oil supply shocks driven by political events mainly produce short-term effects on China's output and inflation, while the other three shocks produce relatively long-term effects; in addition, demand shocks that are specific to the crude oil market contribute the most to the fluctuations in China's output and inflation.     

Assessing the impact of US ethanol on fossil fuel markets: A structural VAR approach

Despite the growing importance of biofuels, the effect of biofuels on fossil fuel markets is not fully understood. We develop a joint structural Vector Auto Regression (VAR) model of the global crude oil, US gasoline, and US ethanol markets to examine whether the US ethanol market has had any impact on global oil markets. The structural VAR approach provides a unique method for decomposing price and quantity data into demand and supply shocks, allowing us to estimate the distinct dynamic effects of ethanol demand and supply shocks on the real prices of crude oil and US gasoline. Ethanol demand in the US is driven mainly by government support in the form of tax credits and blending mandates. Shocks to ethanol demand therefore reflect changes in policy more than any other factor. In contrast, ethanol supply shocks are driven by changes in feedstock prices. A principle finding is that a policy-driven ethanol demand expansion causes a statistically significant decline in real crude oil prices, while an ethanol supply expansion does not have a statistically significant impact on real oil prices. This suggests that even though US ethanol market is small, the influence of US biofuels policy on the crude oil market is pervasive. We also show that ethanol demand shocks are more important than ethanol supply shocks in explaining the fluctuation of real prices of crude oil and US gasoline.     

Oil shocks and financial systemic stress: International evidence

By conducting a structural VAR analysis on the financial systemic stress in 20 countries, this paper provides international evidence that oil structural shocks impact not only stress in individual financial markets but also their connectedness. The oil structural shocks explain a large fraction of the variation in the connectedness among various financial markets. The effect of oil structural shocks on financial systemic stress is largely dependent on the origins of oil price changes and a country's net oil export position. In most oil importing economies, financial systemic stress is negatively impacted by supply and aggregate demand shocks and positively impacted by oil-market specific demand shocks. Opposite patterns are detected in oil exporting economies. The effects of the spillovers are asymmetrically related with market conditions: During normal periods, more risks are spilled over from the oil market to financial systems, but during financial crises, the opposite occurs. In periods of financial crises and oil price collapses, there is noticeable contagion between the oil market and financial systems.     

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.     

Oil price shocks and agricultural commodity prices

While the impacts of oil price changes on agricultural commodity markets are of great interest to economists, previous studies do not differentiate oil-specific shocks from aggregate demand shocks. In this paper, we address this issue using a structural VAR analysis. Our findings indicate that the responses of agricultural commodity prices to oil price changes depend greatly on whether they are caused oil supply shocks, aggregate demand shocks or other oil-specific shocks mainly driven by precautionary demand. Oil shocks can explain a minor friction of agricultural commodity price variations before the food crisis in 2006–2008, whereas in post-crisis period their explanatory abilities become much higher. After crisis, the contributions of oil-specific factors to variations in agricultural commodity prices are greater than those of aggregate demand shocks. The results from an alternative SVAR confirm the robustness of our main findings.     

Which oil shocks really matter in equity markets?

This paper examines the relationship between structural oil shocks and US equity markets. The recent oil shock decomposition of Ready (2018) is reconsidered and refined, providing a clearer delineation between shocks to equity market discount rates and aggregate demand, leading to an oil shock specification which attributes substantially more explanatory power to the latter in explaining equity market variation. Providing links with the literature dating back to Kilian and Park (2009), an explicit role is given to precautionary demand shocks using an independent measure constructed from oil futures data, reducing the role of the supply shocks obtained as the final residual in the recursive identification scheme. In an extended sample that allows an analysis of the oil/equity market relationship since the global financial crisis, the modified aggregate demand shocks have approximately twice as much explanatory power for stock return variation than the demand shocks of Ready (2018). The importance of these shocks in driving oil price changes and equity market volatility has only increased since the financial crisis, with the role of supply shocks diminishing. Once these demand effects are accounted for, there is little relationship between precautionary demand shocks and equity returns, in contrast to the existing literature.     

What drives commodity price booms and busts?

We provide evidence on the dynamic effects of aggregate commodity demand shocks, commodity supply shocks, and storage demand or other commodity-specific demand shocks on real commodity prices. We analyze a new data set of price and production levels for 12 agricultural goods, metals, and soft commodities from 1870 to 2013. We establish that commodity demand shocks strongly dominate commodity supply shocks in driving prices over a broad set of commodities and over a long period of time. While commodity demand shocks have gained importance over time, commodity supply shocks have become less relevant.     

The relationship between global oil price shocks and China's output: A time-varying analysis

We employ a class of time-varying Bayesian vector autoregressive (VAR) models on new standard dataset of China's GDP constructed by Chang et al. (2015) to examine the relationship between China's economic growth and global oil market fluctuations between 1992Q1 and 2015Q3. We find that: (1) the time varying parameter VAR with stochastic volatility provides a better fit as compared to it's constant counterparts; (2) the impacts of intertemporal global oil price shocks on China's output are often small and temporary in nature; (3) oil supply and specific oil demand shocks generally produce negative movements in China's GDP growth whilst oil demand shocks tend to have positive effects; (4) domestic output shocks have no significant impact on price or quantity movements within the global oil market. The results are generally robust to three commonly employed indicators of global economic activity: Kilian's global real economic activity index, the metal price index and the global industrial production index, and two alternative oil price metrics: the US refiners' acquisition cost for imported crude oil and the West Texas Intermediate price of crude oil.     

How does the U.S. natural gas market react to demand and supply shocks in the crude oil market?

In this paper we use monthly data (over the period from January 1976 to December 2012) and a structural VAR model to disentangle demand and supply shocks in the global crude oil market and investigate their effects on the real price of natural gas in the United States. We identify the model by assuming that innovations to the real price of crude oil are predetermined with respect to the natural gas market and show that close to 45% of the variation in the real price of natural gas can be attributed to structural supply and demand shocks in the global crude oil market.     

Historical energy price shocks and their changing effects on the economy

The purpose of this paper is to identify the changes in the impact of energy shocks on economic activity — with an interest in assessing if an economy's vulnerability and resilience to shocks improved with economic development. Using data on the United Kingdom over the last three hundred years, the paper identifies supply, aggregate demand and residual shocks to energy prices and estimates their changing influence on energy prices and GDP. The results suggest that the impacts of supply shocks rose with its increasing dependence on coal, and declined with its partial transition to oil. However, the transition from exporting coal to importing oil increased the negative impacts of demand shocks. More generally, the results indicate that improvements in vulnerability and resilience to shocks did not progress systematically as the economy developed. Instead, the changes in impacts depended greatly on the circumstances related to the demand for and supply of energy sources. If these experiences are transferable to future markets, a transition to a diversified mix of renewable energy is likely to reduce vulnerability and increase resilience to energy price shocks.     

Do exchange rates respond asymmetrically to shocks in the crude oil market?

The paper argues that exchange rates respond asymmetrically to different shocks to the crude oil market. We apply Kilian's (2009) methodology to disentangle shocks to the crude oil market into distinct demand and supply shocks, and examine the response of the U.S. real and nominal trade-weighted U.S. dollar exchange rate indexes, as well as six other bilateral exchange rates to these shocks. Our analysis indicates that oil supply shocks have no significant effects on exchange rates, while global aggregate demand and oil-specific demand shocks lead to depreciations. We further show that exchange rates respond asymmetrically to shocks in the crude market depending on whether the shocks are large versus small, or positive versus negative.     

Dynamic fuel price pass-through: Evidence from a new global retail fuel price database

We estimate the dynamic effects of crude oil price shocks on retail fuel prices, the pass-through, using the local projection approach of Jordà (2005). Using a novel monthly dataset of retail fuel prices in 162 countries over the period from 2000:1 to 2014:12, we find that: (i) retail gasoline prices respond positively to crude oil price shocks, but the responses vary across regions and income groups; (ii) there is also some variation across country groups in the persistence of the effects of crude oil price shocks on retail gasoline prices; and (iii) declines in crude oil prices lead to smaller effects on retail gasoline prices than increases in crude oil prices, pointing to an asymmetry in the fuel price pass-through.     

Oil prices,fundamentals and expectations

This paper empirically investigates the relationship between oil prices, traditional fundamentals and expectations. Informational frictions may force a wedge between oil prices and supply and/or demand shocks, especially during periods of elevated risk aversion and uncertainty. In such a context, expectations can be a key driver of oil price movements and their impact can vary over time. Overall, we find that both traditional oil fundamentals and forward-looking expectations matter for oil prices. Our findings show that the real price of oil responds differently to expectations shocks of business leaders, consumers and aggregate markets. Our TVP-VAR approach provides evidence that business leaders' expectations play an important role in terms of oil price fluctuations and the impact is stronger in periods of elevated global oil demand. In terms of traditional oil market fundamentals, we find that oil prices have been significantly affected by the recent US shale oil boom. Moreover, global oil demand had a positive impact upon oil prices, especially from the mid-2000s. Several alternative model specifications prove the robustness of our analysis.     

Another perspective on gasoline price responses to crude oil price changes

The effects of oil price volatility on the responses of gasoline prices to oil price shocks have received little attention in discussions on the relationship between the prices of crude oil and gasoline. In this paper we consider such effects by using a bivariate structural vector autoregression which is modified to accommodate GARCH-in-mean errors. Our measure of oil price volatility is the conditional variance of the oil price–change forecast error. We isolate the effects of volatility in the price of oil on the price of gasoline and employ simulation methods to calculate nonlinear impulse response functions (NIRFs) to trace any asymmetric effects of independent oil price shocks on the conditional means of gasoline prices. We test whether the relationship between the prices of crude oil and gasoline is symmetric using tests of the null hypothesis of symmetric impulse responses. Based on monthly U.S. data over the period from 1978:1 to 2014:11, our empirical results show that gasoline prices respond asymmetrically to positive and negative oil price shocks. We also find that oil price volatility has a positive effect on the price of gasoline and it contributes to the asymmetries in the transmission of oil price shocks.     

Macroeconomic impacts of oil price shocks in Asian economies

This paper analyzes the macroeconomic impact of structural oil shocks in four of the top oil-consuming Asian economies, using a VAR model. We identify three different structural oil shocks via sign restrictions: an oil supply shock, an oil demand shock driven by global economic activity and an oil-specific demand shock. The main results suggest that economic activity and prices respond very differently to oil price shocks depending on their types. In particular, an oil supply shock has a limited impact, while a demand shock driven by global economic activity has a significant positive effect in all four Asian countries examined. Our finding also includes that policy tools such as interest rates and exchange rates help mitigating the effects of supply shocks in Japan and Korea; however, they can be more actively used in response to demands shocks.     

Is the recent low oil price attributable to the shale revolution?

The U.S. Energy Information Administration estimates that approximately 52% of total U.S. crude oil was produced from shale oil resources in 2015. We examine whether the recent low crude oil price is attributable to this shale revolution in the U.S., using a SVAR model with structural breaks. Our results reveal that U.S. supply shocks are important drivers of real oil price and, for example, explain approximately a quarter of the 73% decline between June 2014 and February 2016. Failure to consider statistically significant structural changes results in underestimating the role played by global supply shocks, while overestimating the role of the demand shocks.     

Tight oil,real WTI prices and U.S. stock returns

Following the adoption of new techniques of shale and fracking by U.S. oil companies, a structural vector autoregression model (SVAR) complements studies on why Brent and WTI started to diverge around early-2011. Using monthly data from 2000 to 2018, we decompose oil supply into: world oil (excluding U.S.), U.S. conventional (non-tight) oil and U.S. tight oil. We examine the variance decomposition of stock returns for the aggregate market (S&P 500), the S&P Energy sector and Chevron and Exxon Mobil oil companies, and we further identify differences between two subsamples from 2000 to 2010 and 2011 to 2018, respectively. We find that supply considerations (especially due to tight oil) become more important in the subsample after 2011, not only for individual oil companies but also for the aggregate market and energy sector: Supply shocks due to tight oil explain in our benchmark model between 29% (S&P 500) and 31% (S&P Energy) of the variance in stock returns after 24 months and between 28% and 29% for oil companies. None of these are statistically significant in the pre-2011 subsample. Among impulse responses, tight oil production responds positively to disruptions in world oil, and U.S. stock returns respond positively to oil price shocks and respond negatively to tight oil shocks which is a further finding while being consistent with the literature. Copula modeling uncovers stronger tail dependences in the second subsample for the interactions during downturns and upturns among global demand, crude oil prices and stock markets.     

Expectations, learning, and the changing relationship between oil prices and the macroeconomy

This paper estimates a structural general equilibrium model to investigate the changing relationship between the oil price and macroeconomic variables. The oil price, through the role of oil in production and consumption, affects aggregate demand and supply in the model. The assumption of rational expectations is relaxed in favor of learning. Oil prices, therefore, affect the economy through an additional channel, i.e. through their effect on the formation of agents' beliefs.The estimated learning dynamics indicates that economic agents' perceptions about the effects of oil prices on the economy have changed over time: oil prices were perceived to have large effects on output and inflation in the 1970s, but only milder effects after the mid-1980s. Since expectations play a large role in the determination of output and inflation, the effects of oil price increases on expectations can magnify the response of macroeconomic variables to oil price shocks. In the estimated model, in fact, the implied responses of output and inflation to oil price shocks were much more pronounced in the 1970s than in 2008. Therefore, through the time variation in the impact of oil prices on beliefs, the paper can successfully explain the observed weakening of the effects of oil price shocks on real activity and inflation.     

What drives volatility in natural gas prices?

We examine the primary drivers of U.S. natural gas price volatility. To do so, we apply a structural heterogeneous autoregressive VAR (SHVAR) model, accommodating structural breaks in both the coefficient and volatility, to monthly time series data on natural gas supply, demand and price between January 1978 and July 2018. We detect several structural changes in coefficients and shock volatility in the natural gas market. Our findings indicate that the response of natural gas prices differs significantly depending on the regime and type of shock in the natural gas market. While demand shocks specific to the natural gas market are the primary drivers of natural gas price volatility, structural supply shocks also play a significant role in explaining movements in natural gas prices. Our results suggest that failure to consider structural breaks in the coefficient and volatility may result in biased estimates and distorted impulse responses of the impact of shocks on natural gas price volatility.