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.     

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.     

Effects of oil price shocks on the stock market performance: Do nature of shocks and economies matter?

The main focus of this study is to examine how oil price fluctuations influence the performance of stock markets. This study used the causality approach developed by Toda and Yamamoto (1995) to explore the causality between oil prices and stock prices in the long-run and their short-term impact. The generalized impulse response functions were applied to the monthly data in the period from January 1997 to July 2013. In this study, to capture the different characteristics of oil refining, exporting and importing, three Asian economies were examined. The results indicate that the manner in which a market reacts to hikes in oil prices varies between different markets and periods. This depends on differences in the oil characteristics of the economy and the nature of the shock in oil prices.     

Gold and oil futures markets: Are markets efficient?

In this paper we examine the long-run relationship between gold and oil spot and futures markets. We draw on the conceptual framework that when oil price rises, it creates inflationary pressures, which instigate investments in gold as a hedge against inflation. We test for the long-run relationship between gold and oil futures prices at different maturity and unravel evidence of cointegration. This implies that: (a) investors use the gold market as a hedge against inflation and (b) the oil market can be used to predict the gold market prices and vice versa, thus these two markets are jointly inefficient, at least for the sample period considered in this study.     

Do emerging markets matter in the world oil pricing system? Evidence of imported crude by China and India

This paper provides empirical evidence on the changing structure of world oil price system by identifying an additional driver-emerging market factor. We choose China and India as a representative of emerging markets to examine if the quantity of crude oil imported by China and India is significant in the existing oil pricing system (Kaufmann et al., 2004). Our data starts from January 2002 and ends in March 2010, which includes the oil shock of 2007–2008. We utilize cointegration and error correction model framework developed by Engle–Granger (1987) and Gregory–Hansen (1996) in the analysis. Our results indicate that demand from emerging markets has become a significant factor in the world oil pricing system since 2003. This result is significant as it lends empirical support to the widely held conjecture that the oil shock of 2007–2008 is a demand-led shock (Hamilton, 2009). Our result also has significant policy implications that go beyond the oil shock. The emerging market factor is there to stay and reflects the changing power between emerging and developed economies in the world economic system as a result of decades of fast economic development in the former. It will certainly influence policy issues related to oil and beyond.     

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.     

Does speculation in the oil market drive investor herding in emerging stock markets?

This paper examines whether the time variation in the level of investor herding in the stock markets of major oil exporting nations relates to speculation and volatility in the global oil market. We find that speculative activities in the oil market, rather than oil price movements, are positively correlated with anti-herding in the stock markets of major exporters. We argue that traders take the speculative signals from the oil market as a sign of positive expectations and try to generate superior profits by going against the crowd in their local market. While this pattern largely holds during calm (low volatility) market periods, we also find that significant herd behavior takes place during high volatility (or crisis) periods. The findings suggest that policy makers who are concerned about stability in their stock markets should monitor measures of speculative activities in the energy market in order to model and monitor volatility and/or risk transmissions into their markets.     

Does the source of oil price shocks matter for South African stock returns? A structural VAR approach

In this paper, we investigate the dynamic relationship between different oil price shocks and the South African stock market using a sign restriction structural VAR approach for the period 1973:01 to 2011:07. The results show that for an oil-importing country like South Africa, stock returns only increase with oil prices when global economic activity improves. In response to oil supply shocks and speculative demand shocks, stock returns and the real price of oil move in opposite directions. The analysis of the variance decomposition shows that the oil supply shock contributes more to the variability in real stock prices. The main conclusion is that different oil price shocks affect stock returns differently and policy makers and investors should always consider the source of the shock before implementing a policy and making investment decisions.     

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.     

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.     

Do demand and supply shocks explain USA’s oil stock fluctuations?

In this paper using historical monthly data on the US oil stocks (Crude Oil and Petroleum Products Ending Stock-coppes), industrial production, energy use for transportation, oil production, and oil imports, we examine whether supply and demand shocks explain the apparent decline in the volatility of the growth of COPPES since about the mid-1980s. We find that nearly 70% of the variation in the US COPPES growth is explained by its supply and demand factors, each sharing about half of this variation. This is on account of sharp decline in the contribution of persistence to the US COPPES growth variation from about 47% in the pre-break period to about 17% in the post-break period. This reduction is taken up by increased contribution of demand and supply factors since mid 1980s, of which growth variances have declined on net since then. This in turn contributes to the stability of the US COPPES growth fluctuations.     

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.     

Volatility spillovers in China’s crude oil,corn and fuel ethanol markets

Price volatility spillovers among China’s crude oil, corn and fuel ethanol markets are analyzed based on weekly price data from September 5, 2003 to August 31, 2012, employing the univariate EGARCH model and the BEKK-MVGARCH model, respectively. The empirical results indicate a higher interaction among crude oil, corn and fuel ethanol markets after September, 2008. In the overall sample period, the results simultaneously provide strong evidence that there exist unidirectional spillover effects from the crude oil market to the corn and fuel ethanol markets, and double-directional spillovers between the corn market and the fuel ethanol market. However, the spillover effects from the corn and fuel ethanol markets to the crude oil market are not significant.     

Multifractal cross-correlations between the world oil and other financial markets in 2012–2017

Statistical and multiscaling characteristics of WTI Crude Oil futures prices expressed in US dollar in relation to the most traded currencies as well as to gold futures and to the E-mini S&P500 futures prices on 5 min intra-day recordings in the period January 2012–December 2017 are studied. It is shown that in most of the cases the tails of return distributions of the considered financial instruments follow the inverse cubic power law. The only exception is the Russian ruble for which the distribution tail is heavier and scales with the exponent close to 2. From the perspective of multiscaling the analysed time series reveal the multifractal organization with the left-sided asymmetry of the corresponding singularity spectra. Even more, all the considered financial instruments appear to be multifractally cross-correlated with oil, especially on the level of medium-size fluctuations, as the multifractal cross-correlation analysis carried out by means of the multifractal cross-correlation analysis (MFCCA) and detrended cross-correlation coefficient ρ_q show. The degree of such cross-correlations is however varying among the financial instruments. The strongest ties to the oil characterize currencies of the oil extracting countries. Strength of this multifractal coupling appears to depend also on the oil market trend. In the analysed time period the level of cross-correlations systematically increases during the bear phase on the oil market and it saturates after the trend reversal in 1st half of 2016. The same methodology is also applied to identify possible causal relations between considered observables. Searching for some related asymmetry in the information flow mediating cross-correlations indicates that it was the oil price that led the Russian ruble over the time period here considered rather than vice versa.     

Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?

This paper investigates the inter-connectedness between WTI oil price returns and the returns of listed firms in the US energy sector. Specifically, we focus on the issue of whether firm-level idiosyncratic information matters. A generalised dynamic factor model is used to separate common components from idiosyncratic components in these energy stocks. Systemic connectedness is then estimated following Diebold and Yilmaz (2014). Our empirical results demonstrate the important role of industrial-level common information in understanding the oil–stock relationship. A number of interesting points include: the US energy sector is the net contributor to WTI price changes, but the effect is mainly driven by industrial-level common information; the oil and gas industry dominates other industries in the energy sector; the dynamic analysis shows that although idiosyncratic information is mostly independent of oil shocks, individual energy stock returns do respond to WTI price movements.     

Can reserve additions in mature crude oil provinces attenuate peak oil?

Following the peak in US crude oil production 30 years ago, more and more non-OPEC producers have seen their production decline as a result of resource depletion. OPEC, on the other hand has extracted a comparatively smaller proportion of its reserve base. Given that new non-OPEC discoveries are growing ever limited, we explore the role of reserve additions and OPEC in determining future crude oil supply: we formulate a model that embodies a weak and strong OPEC for various rates of reserve additions in mature crude oil provinces. Using this geo-economic partial equilibrium model that generates a peak in crude oil production, we show that although potential conventional crude oil resources may seem abundant, OPEC strategy could cause substantial crude oil reserve depletion in non-OPEC countries by 2050 (or even earlier) given likely depletion rates. In addition, we find that reducing reserve decline rates in mature crude oil provinces not only extends the time to exhaustion substantially, but also discourages OPEC from engaging in an overly strategic extraction behavior.     

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.     

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.     

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.     

Intrastate conflict in oil producing states: A threat to global oil supply?

In this paper I investigate how often and how much outbreaks of intrastate conflict in oil producing states translates into oil supply shortfalls. The Libyan conflict that broke out in February 2011 highlighted the fear that intrastate conflict in oil producing states may imply shortfalls and ensuing volatile global oil prices. I argue, however, that it is far from certain that shortfalls following conflict outbreak will occur, since both sides in a conflict face incentives simultaneously to protect and maintain oil installations and to strike and destroy these. Based on a quantitative analysis of 39 intrastate wars in oil producing countries (1965–2007) I conclude that outbreak of conflict does not translate into production decline with any certainty. In fact, likelihoods are less than 50% for reductions to occur. In many cases growing production actually followed conflict outbreak. I conclude by investigating four characteristics of intrastate conflict that may explain when oil production is at risk during conflict: (1) proximity of oil producing fields to key battle zones, (2) duration of conflict, (3) separatism and the location of oil in separatist territory, and (4) the relative size of oil production. While the first three factors did not prove important, oil producer size could be significant. But further research is needed to establish this with greater certainty.