Do energy prices stimulate food price volatility? Examining volatility transmission between US oil,ethanol and corn markets

This paper examines volatility transmission in oil, ethanol and corn prices in the United States between 1997 and 2011. We follow a multivariate GARCH approach to evaluate the level of interdependence and the dynamics of volatility across these markets. The estimation results indicate a higher interaction between ethanol and corn markets in recent years, particularly after 2006 when ethanol became the sole alternative oxygenate for gasoline. We only observe, however, significant volatility spillovers from corn to ethanol prices but not the converse. We also do not find major cross-volatility effects from oil to corn markets. The results do not provide evidence of volatility in energy markets stimulating price volatility in the US corn market.     

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.     

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.     

Does the volatility of commodity prices reflect macroeconomic uncertainty?

While there exists numerous studies on the macroeconomic effects of oil and commodity shocks, the literature is quite silent on the impact of macroeconomic uncertainty on oil and commodity prices and, especially, on their volatility. This paper tackles this issue through the estimation of a structural threshold vector autoregressive (TVAR) model on a sample of 19 commodity markets. We aim at (i) assessing whether the effect of macroeconomic uncertainty shocks on commodity price returns depends on the degree of uncertainty, and (ii) investigating the transfer from macroeconomic uncertainty to price uncertainty using a newly developed measure of commodity price uncertainty. Our findings show that both agricultural and industrial markets are highly sensitive to the variability and the level of macroeconomic uncertainty, while the impact on precious metals is more parsimonious given their well-identified safe-haven role in time of economic turmoil. In addition, we find evidence that the recent 2007–09 recession has generated an unprecedented episode of high uncertainty in numerous commodity prices. Interestingly, our analysis further reveals that volatility and uncertainty in prices can be disconnected. This is especially true for the oil market as most important shocks in the 1990s and the beginning of the 2000s that lead to price volatility do not generate price uncertainty, highlighting the relevance of our uncertainty measure in linking uncertainty to predictability rather than to volatility.     

Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany

Although variable renewable energy (VRE) technologies with zero marginal costs decrease electricity prices, the literature is inconclusive about how the resulting shift in the supply curves impacts price volatility. Because the flexibility to respond to high peak and low off-peak prices is crucial for demand-response applications and may compensate for the losses of conventional generators caused by lower average prices, there is a need to understand how the penetration of VRE affects volatility. In this paper, we build distributed lag models with Danish and German data to estimate the impact of VRE generation on electricity price volatility. We find that in Denmark wind power decreases the daily volatility of prices by flattening the hourly price profile, but in Germany it increases the volatility because it has a stronger impact on off-peak prices. Our analysis suggests that access to flexible generation capacity and wind power generation patterns contribute to these differing impacts. Meanwhile, solar power decreases price volatility in Germany. By contrast, the weekly volatility of prices increases in both areas due to the intermittency of VRE. Thus, policy measures for facilitating the integration of VRE should be tailored to such region-specific patterns.     

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.     

How does petroleum price and corn yield volatility affect ethanol markets with and without an ethanol use mandate?

The recent increase in ethanol use in the US strengthens and changes the nature of links between agricultural and energy markets. Here, we explore the interaction of market volatility and the scope for policy to affect this interaction, with a focus on how corn yields and petroleum prices affect ethanol prices. Mandates associated with new US energy legislation may intervene in these links in the medium-term future. We simulate stochastically a structural model that represents these markets, and that includes mandates, in order to assess how shocks to corn or oil markets can affect ethanol price and use. We estimate that the mandate makes ethanol producer prices more sensitive to corn yields and less sensitive to changes in petroleum prices overall. We note a discontinuity in these links that is caused by the mandate. Ethanol use can exceed the mandate if petroleum prices and corn yields are high enough, but the mandate limits downside adjustments in ethanol use to low petroleum prices or corn yields.     

Comprehensive evaluation of ARMA–GARCH(-M) approaches for modeling the mean and volatility of wind speed

Accurately modeling the mean and volatility of wind speed can be beneficial to effective wind energy utilization. For this purpose, this paper evaluates the effectiveness of autoregressive moving average–generalized autoregressive conditional heteroscedasticity (ARMA–GARCH) approaches for modeling the mean and volatility of wind speed. Five different GARCH approaches are included, and each consists of an original form and a modified form, GARCH-in-mean (GARCH-M). As a result, 10 different model structures are evaluated, based on the 7-year hourly wind speed data collected at four different heights from an observation site in Colorado, USA. Multiple evaluation methods of modeling sufficiency are used. The results show that the ARMA–GARCH(-M) approaches can effectively catch the trend change of the mean and volatility of wind speed. Also, the volatility of wind speed has the nonlinear and asymmetric time-varying feature, and the ARMA–GARCH-M structures can consistently improve the modeling sufficiency of mean wind speed. As the height increases, the explanatory power of all ARMA–GARCH(-M) models slightly deteriorates. On the other hand, no single model structure outperforms the others at all heights, and this confirms that for any wind speed dataset, the potential models should be evaluated to find the most appropriate one for the highest modeling sufficiency.     

Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence

This study examines whether high-frequency crude oil futures data contain useful information to forecast the realized volatility (RV) of the US stock market from both in- and out-of-sample perspectives. There are several significant findings. First, from the in-sample analysis, crude oil futures RV exhibits a significant positive impact on the future S&P 500 volatility. Second, the out-of-sample results reveal that the prediction models, including crude oil futures RV, outperform the related competing models, implying that crude oil RV is an important predictive factor for the US stock market. Third, we further find that the primary forecasting ability of crude oil RV is reflected in high-frequency information, negative crude oil RV, and high volatility level. Finally, the out-of-sample empirical results based on different forecasting windows, alternative forecast evaluation approaches, subsample analysis, different prediction models, alternative MIDAS lags, and controlling the leverage effect are robust to our conclusions.     

How do OPEC news and structural breaks impact returns and volatility in crude oil markets? Further evidence from a long memory process

Since its formation, OPEC through its conference decisions has been a major player in the world oil markets. The purpose of this paper is to examine the impacts of OPEC's different news announcements on the conditional expectations and volatility of crude oil markets in the presence of long memory and structural changes. To do so, we first discern OPEC's oil production behavior in response to its “cut”, “maintain”, and “increase” decisions. Then by applying the ARMA–GARCH class models to the two global benchmarks WTI and Brent over the period May 1987 through December 2012, we find strong evidence of long memory. The empirical evidence also shows that OPEC's announcements especially the “cut” and the “maintain” decisions have a significant effect on both returns and volatility of the crude oil markets, particularly that of the WTI. Moreover, we explore the possibility of structural breaks in the crude oil prices and detect five (six) breakpoints for the WTI (Brent) oil markets. The presence of structural breaks reduces the persistence of volatility. Accounting for OPEC's scheduled news announcements in the presence of structural changes reduces the degree of volatility persistence and enhances the understanding of this volatility in the oil markets. These results have several implications for policy makers, oil traders and other participants in the crude oil markets.     

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.