The impact of crude oil price fluctuations on unemployment in the United States

This study begins by asking whether fluctuations in the price of crude oil have affected employment in the United States. After reviewing previous assessments of the issue, the existence of an empirical relationship between unemployment and crude oil price volatility is established using Granger causality. Subsequently, the nature of the relationship is estimated with the results suggesting that at least three full years are required before measurable impacts of a percentage change in the real price of crude oil on the change in unemployment are exhausted. Finally, the structural stability of the functional relationship between the change in unemployment, the volatility of the price of crude oil and the percentage change in gross national product is examined.     

Impact of oil price change on airline's stock price and volatility: Evidence from China and South Korea

Generally, the influence of crude oil price on the industries (enterprises) varies because they have different levels of reliance on crude oil. For airlines, the expenditure on fuel accounts for a considerable proportion of their gross costs; thus, airlines are unusually sensitive to changes in the crude oil price. The discussion on the relationship between crude oil price and airlines will help the airlines improve their ability to cope with the crude oil price risk. In addition, the responses of South Korean and Chinese airlines in the event of a price shock, that take, are also very important as the airplane is a basic form of transportation in many countries. This study investigates the impact of three crude oil price (WTI, Brent, Dubai) change on the stock price and volatility of four airlines (Korean Air, Asiana Airlines, Air China, and China Eastern Airlines) using VAR-GARCH-BEKK model. The main findings are as follows. There is return and volatility spillover effect between crude oil price and the stock prices of airlines. The volatility spillover effect between the crude oil price and airlines' stock price is more significant than the return spillover effect. Compared with the transportation industry, the stock prices of smaller airlines of South Korea and China are relatively more sensitive to the change in oil price. In addition, compared with Korea's airlines, China's airlines are influenced more by the oil price change, implying that spillover effects owing to oil price are closely related to the different characteristics of the air transport markets of the two countries.     

Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis

This paper assesses factors that potentially influence the volatility of crude oil prices and the possible linkage between this volatility and agricultural commodity markets. Stochastic volatility models are applied to weekly crude oil, corn, and wheat futures prices from November 1998 to January 2009. Model parameters are estimated using Bayesian Markov Chain Monte Carlo methods. Speculation, scalping, and petroleum inventories are found to be important in explaining the volatility of crude oil prices. Several properties of crude oil price dynamics are established, including mean-reversion, an asymmetry between returns and volatility, volatility clustering, and infrequent compound jumps. We find evidence of volatility spillover among crude oil, corn, and wheat markets after the fall of 2006. This can be largely explained by tightened interdependence between crude oil and these commodity markets induced by ethanol production.     

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.     

Crude oil moments and PNG stock returns

We examine the risk-neutral moments of crude oil and their relationship to stock returns in the Petroleum and Natural Gas (PNG) industry. We find substantial overlaps in the association between returns and S&P 500- and crude oil higher moments. Net of these overlaps, PNG stocks share a significant negative relationship with crude volatility and positive relationships with crude skewness and kurtosis. Large cap stocks and those with a history of hedging exhibit negative loadings on crude volatility. However, after controlling for S&P 500- and crude oil returns and their risk-neutral moments, there is little evidence that PNG stocks systematically and significantly price either S&P 500- or crude oil volatility. We document a weak pricing of crude skewness, but find no evidence for the pricing of the implied higher moments of market returns.     

Oil price risk evaluation using a novel hybrid model based on time-varying long memory

The volatility of crude oil price has a great influence on the world economy. In order to measure the crude oil price risk (VaR) and explain the dynamic relationship between investment income and risk in the oil market more clearly, this paper uses a variety of fractional GARCH models to describe typical volatility characteristics like long memory, volatility clustering, asymmetry and thick tail. The autoregressive conditional heteroscedasticity in the mean model (ARCH-M) and peaks-over-threshold model of extreme value theory (EVT-POT) are taken into account to develop a hybrid time-varying long memory GARCH-M-EVT model for calculation of static and dynamic VaR. Empirical results show that the WTI crude oil has a significantly long memory feature. All the fractional integration GARCH models can describe the long memory appropriately and the FIAPARCH model is the best in regression and out of sample one-step-ahead VaR forecasting. Back-testing results show that the FIAPARCH-M-EVT model is superior to other GARCH-type models which only consider oil price fluctuation characteristics partially and traditional methods including Variance-Covariance and Monte Carlo in price risk measurement. Our conclusions confirm that considering long memory, asymmetry and fat tails in the behavior of energy commodity return combined with effectively dynamic time-varying risk reflection such as the ARCH-M model and reliable tail extreme filter processes such as EVT can improve the accuracy of crude oil price risk measurement, provide an effective tool for analyzing the extreme risk of the tail of the oil market and facilitate the risk management for oil market investors.     

Is there co-movement of agricultural commodities futures prices and crude oil?

Even though significant attempts have appeared in literature, the current perception of co-movement of commodity prices appear inadequate and static. In particular we focus on price movements between crude oil futures and a series of agricultural commodities and gold futures. A comparative framework is applied to identify changes in relationships through time and various cointegration methodologies and causality tests are employed. Our results indicate that co-movement is a dynamic concept and that some economic and policy development may change the relationship between commodities. Furthermore we show that biofuel policy buffers the co-movement of crude oil and corn futures until the crude oil prices surpass a certain threshold.     

Oil price volatility and stock price fluctuations in an emerging market: Evidence from South Korea

How important are oil price fluctuations and oil price volatility on equity market performance? What are the policy implications if volatility turns out to be significant? We assess this issue in an economics/finance nexus for Korea using a VEC model including interest rates, economic activity, real stock returns, real oil prices and oil price volatility. Our main aim is to capture the effects of crude oil prices on the Korean economy thoroughly covering the period of the Asian Financial Crisis of 1997, which heavily affected the country, and the oil price hikes in the early 1990s after the Gulf War. South Korea was the country most hit by the financial crisis together with Indonesia and Thailand. Results indicate the dominance of oil price volatility on real stock returns and emphasize how this has increased over time. Oil price volatility can have profound effect on the time horizon of investment and firms need adjust their risk management procedures accordingly. This increase in dependency has been found in other net oil importing emerging equity markets. We test the relationship between oil price movements and economic activity by using modern time series techniques in a cointegrating framework. We expand the standard error correction model by examining the dynamics of out of sample causality through the generalized variance decomposition and impulse response function techniques. The evidence from persistence profiles also gives important guidelines based on how fast the entire system adjusts back to equilibrium. In addition, we find the cointegrating relationship to be stable and find that the linear error correction model to be more favorable than an asymmetric 2 period Markov switching model.     

Feedback spillover dynamics of crude oil and global assets indicators: A system-wide network perspective

Crude oil, the critical driver of the world economy, is quick to be blamed for causing volatility in all classes of assets, viz. equity, commodities, bonds, and currencies. The feedback mechanism between crude oil and these four asset classes makes the volatility spillover connectedness dynamics more complex and an important area to explore. Henceforth, we quantify and analyze the time-varying system-wide volatility spillover connectedness dynamics of crude oil and global asset indicators (GAIs) covering four major asset classes for the period from 2000 to 2016. Additionally, we have used the confluence of generalized error variance decomposition (GEVD) and network diagrams. According to the findings of this paper, crude oil is affected more by the GAIs than vice versa, thus making the crude oil price more vulnerable to volatility fluctuations in GAIs. On a pairwise basis, the study finds that commodities and currency are more tightly knit to crude oil, with CADUSD and gold being more sensitive to crude oil price fluctuations. In addition, the equity indices of the US and the UK have become more sensitive to crude oil price volatility following the GFC. The study opens future discourse for portfolio managers and policymakers to explore the spillover pattern and act accordingly by mitigating risk via the compensatory mechanism of positive/negative spillover pairs.     

国际原油价格的主控因素分析

现代国际油价有美国西得克萨斯中质原油（WTI）价格、欧洲布伦特（BRENT）油价及OPEC一揽子价格三大价格体系。根据国际原油价格变化的主控因素差异,可以将其划分为结构性转变、基本面趋势和波动3个部分。油价结构性转变与全球主要资源区和市场区的结构性调整有关,其直接诱因是世界原油供需体系的结构性紊乱．根源在于生产或市场的无序及对定价权的争夺,而结果均导致定价体系的嬗变。原油名义价格由供需矛盾、石油货币价格、边际成本和替代能源共同决定,这四者构成决定国际原油价格基本面趋势的主要因素。石油供需矛盾的日益突出和美元贬值必然导致油价上涨;而大型油公司高成本区的边际成本决定了未来国际油价基本面将围绕80美元／bbl波动：基础能源的垄断属性将可能最终使替代能源与目标能源的竞争关系转变为价格继承关系。波动情况主要受石油巨头或金融投机者操纵,他们通过原油供应链中段的多种实体原油仓储以及虚拟经济所衍生的多种投机括动达到操纵油价波动的目标,而WTI油价最容易受到地区市场因素的影响,从而为各种投机炒作和油价操纵提供了有利条件。一旦油价定价体系存在某种操纵,就可以通过分析操纵者的战略动机实现对油价趋势的合理判断。     

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.     

Volatility spillover between oil and agricultural commodity markets

This study examines volatility transmission between oil and selected agricultural commodity prices (wheat, corn, soybeans, and sugar). We apply the newly developed causality in variance test and impulse response functions to daily data from 01 January 1986 to 21 March 2011. In order to identify the impact of the food price crisis, the data are divided into two sub-periods: the pre-crisis period (01 January 1986 to 31 December 2005) and the post-crisis period (01 January 2006–21 March 2011). The variance causality test shows that while there is no risk transmission between oil and agricultural commodity markets in the pre-crisis period, oil market volatility spills on the agricultural markets —with the exception of sugar —in the post-crisis period. The impulse response analysis also indicates that a shock to oil price volatility is transmitted to agricultural markets only in the post-crisis period. This paper thereby shows that the dynamics of volatility transmission changes significantly following the food price crisis. After the crisis, risk transmission emerges as another dimension of the dynamic interrelationships between energy and agricultural markets.     

Impact of the price of energy on the use of conservation tillage in agriculture in the USA

The relationship between energy expenditure and the use of conservation tillage is of special importance in addressing concerns about the impact of agricultural production on the environment in the US. After establishing that a relationship exists between the price of energy and the use of conservation tillage via the Granger causality, the relationship is quantified. It is shown that while the real price of crude oil, the proxy used for the price of energy, does not affect the rate of adoption of conservation tillage, it does impact upon the extent to which it is used. Finally, there is no structural instability in the relationship between the relative use of conservation tillage and the real price of crude oil over the period 1963–1997.     

Modeling the relationship between the oil price and global food prices

The growth of corn-based ethanol production and soybean-based bio-diesel production following the increase in the oil prices have significantly affect the world agricultural grain productions and its prices. The main purpose of this paper is to investigate the relationships between the crude oil price and the global grain prices for corn, soybean, and wheat. The empirical results show that the change in each grain price is significantly influenced by the changes in the crude oil price and other grain prices during the period extending from the 3rd week in 2005 to the 20th week in 2008 which implies that grain commodities are competing with the derived demand for bio-fuels by using soybean or corn to produce ethanol or bio-diesel during the period of higher crude oil prices in these recent years. The subsidy policies in relation to the bio-fuel industries in some nations engaging in bio-fuel production should be considered to avoid the consequences resulting from high oil prices.     

An event study analysis of oil and gas firm acreage and reserve acquisitions

We examine the impact of the announcement of acquisition of oil and gas acreage and reserves on the share price of US listed oil and gas firms. While there is evidence of information asymmetry related differences in the share market reaction on announcement of acquisition of acreage or reserves, we also identify greater sensitivity to crude oil price volatility for acreage acquisitions, consistent with the creation of valuable real options on acquisition of acreage. This is not evident to the same extent with acquisition of reserves. For example, acreage investment announcements reveal a statistically significant 1.22% premium (3-day CAR) in periods of high crude oil volatility compared with periods of low volatility. The premium on reserve acquisitions across these periods is a statistically insignificant 0.12%. This is supported in a multiple regression setting, with share price sensitivity to crude oil price volatility being higher for acreage acquisitions than for reserve acquisitions. Our sample consists of 1391 separate acreage or reserve acquisition announcements made by oil and gas firms listed on the U.S. equity market over the period from 1992 to 2011.     

Analyzing time-varying volatility spillovers between the crude oil markets using a new method

The spillover effect is an important factor affecting the volatility of crude oil price. Basing on the study of Diebold and Yilmaz (2009, 2012, 2014), we propose a new method that calculates the time-varying volatility spillover indexes by the generalized forecast error variance decomposition of TVP-VAR-SV model. Then, using the new method, we study the time-varying volatility spillovers between four major crude oil markets (WTI, Brent, Oman, Tapis) from November 29, 2002 to July 13, 2018. By comparing the results of our new method and traditional rolling window method, we verify the superiority of our new method. The results show that the volatility spillovers calculated by the new method are clearer, more stable and not outlier sensitive. From the estimated results of time-varying volatility spillovers, we find that the volatility spillover between crude oil markets is slowly increasing, but there are obvious cyclical changes. And from the correlation analysis and the Granger causality test, we find that the volatility and volatility spillovers are positively correlated and are two-way Granger causality, which supported for the market infection hypothesis of King and Wadhwani (1990).     

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.     

Crude oil price shocks and hedging performance: A comparison of volatility models

From a practical perspective, it is crucial to hedge the crude oil price risk in periods of dramatic price change. In this study, we directly investigate the performance of crude oil hedge portfolios in the five periods in which the largest oil price shocks in history occurred. We use stochastic volatility (SV), GARCH, and the diagonal BEKK model to estimate the minimum variance hedge ratio of hedge portfolios. Our empirical results provide evidence that hedging strategies based on the SV model are able to outperform the GARCH and BEKK models in terms of variance reduction. Our results are also consistently valid for various hedge horizons. Interestingly, although it is important to estimate variance and covariance accurately when constructing minimum variance portfolios, we find that reducing the mean squared and mean absolute errors does not guarantee superior hedge performance.     

On the intraday dynamics of oil price and exchange rate: What can we learn from China and India?

The main aim of this paper is to investigate the volatility determinants of crude oil and foreign exchange markets and jump spillover between them. We consider currencies of two major oil-importing countries (India and China) over the sample period of January 1, 2013 to October 31, 2019. We find evidence of positive return spillover from the oil to the foreign exchange market; however, there is a lack of return spillover in the other direction. Oil jumps appear to have a negative impact on exchange rate conditional volatility, and the latter responds asymmetrically to disentangled (positive and negative) oil price jumps. We also report disentangled exchange rate jumps' significant impact on conditional oil price volatility. These results, however, are asymmetric based on the nature of jumps and alternative oil price series. Finally, we do not find evidence of co-jump between the oil and foreign exchange markets. These results have important implications for investors and policymakers.     

The effect of global oil price shocks on China's agricultural commodities

This paper studied the effect of global oil price shocks on agricultural commodities in China, including strong wheat, corn, soybean, bean pulp, cotton and natural rubber. We regarded oil price volatility process as a combination of continuous process and jump process. We not only separated oil price shocks into positive and negative categories to identify different effects on agricultural commodities in continuous process, but also investigated how jump behavior influenced these agricultural commodities. We found that the oil price was characterized by volatility clustering and jump behavior. At the same time, oil price shocks had different effects on agricultural commodities. In addition, the shocks on most agricultural commodities were asymmetric. Only natural rubber was under influence of the jump intensity of the oil price, in contrast to strong wheat, corn, soybean, bean pulp and cotton.