Analysis of policies to reduce oil consumption and greenhouse-gas emissions from the US transportation sector

Even as the US debates an economy-wide CO₂ cap-and-trade policy the transportation sector remains a significant oil security and climate change concern. Transportation alone consumes the majority of the US’s imported oil and produces a third of total US Greenhouse-Gas (GHG) emissions. This study examines different sector-specific policy scenarios for reducing GHG emissions and oil consumption in the US transportation sector under economy-wide CO₂ prices. The 2009 version of the Energy Information Administration’s (EIA) National Energy Modeling System (NEMS), a general equilibrium model of US energy markets, enables quantitative estimates of the impact of economy-wide CO₂ prices and various transportation-specific policy options. We analyze fuel taxes, continued increases in fuel economy standards, and purchase tax credits for new vehicle purchases, as well as the impacts of combining these policies. All policy scenarios modeled fail to meet the Obama administration’s goal of reducing GHG emissions 14% below 2005 levels by 2020. Purchase tax credits are expensive and ineffective at reducing emissions, while the largest reductions in GHG emissions result from increasing the cost of driving, thereby damping growth in vehicle miles traveled.     

Oil price shocks and industry stock returns

We examine the impact of changes in the oil returns and oil return volatility on excess stock returns and return volatilities of thirteen U.S. industries using the GARCH (1,1) technique. We find strong evidence in support of the view that oil price fluctuations constitute a systematic asset price risk at the industry level as nine of the thirteen sectors analyzed show statistically significant relationships between oil-futures return distribution and industry excess return. These industries are affected either by oil futures returns, oil futures return volatility or both. In general, excess returns of the oil-user industries are more likely to be affected by changes in the volatility of oil returns, than those of oil return itself. Volatilities of industry excess returns are time-varying, and return volatility for a number of sectors, appears to have long memory. Fama–French factors show universal statistical and high economic significance as risk factors influencing industry excess returns.     

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.     

Oil price volatility and stock returns in the G7 economies

This study examines the relationship between oil price volatility and stock returns in the G7 economies (Canada, France, Germany, Italy, Japan, the UK and the US) using monthly data for the period 1970 to 2014. In order to measure oil volatility we consider alternative specifications for oil prices (world, nominal and real prices). We estimate a vector autoregressive model with the following variables: interest rates, economic activity, stock returns and oil price volatility taking into account the structural break in the year 1986. We find a negative response of G7 stock markets to an increase in oil price volatility. Results also indicate that world oil price volatility is generally more significant for stock markets than the national oil price volatility.     

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.     

Analysis of the impacts of combining carbon taxation and emission trading on different industry sectors

Application of price mechanisms has been the important instrument for carbon reduction, among which the carbon tax has been frequently advocated as a cost-effective economic tool. However, blanket taxes applied to all industries in a country might not always be fair or successful. It should therefore be implemented together with other economic tools, such as emission trading, for CO₂ reduction. This study aims to analyze the impacts of combining a carbon tax and emission trading on different industry sectors. Results indicate that the “grandfathering rule (RCE2000)” is the more feasible approach in allocating the emission permit to each industry sector. Results also find that the accumulated GDP loss of the petrochemical industry by the carbon tax during the period 2011–2020 is 5.7%. However, the accumulated value of GDP will drop by only 4.7% if carbon taxation is implemented together with emission trading. Besides, among petrochemical-related industry sectors, up-stream sectors earn profit from emission trading, while down-stream sectors have to purchase additional emission permits due to failure to achieve their emission targets.     

The effect of oil and stock price volatility on firm level investment: The case of UK firms

Crude oil price behaviour has become more volatile since 1973 which has a significant impact on macroeconomic variables such as GDP, inflation and productivity. Studies considering the effects of oil price changes on decisions at the firm level are comparatively few. Oil price volatility represents a source of uncertainty for firm profitability, valuations and investment decisions. This study examines the effects of industry uncertainty and market instability on total investment expenditures in UK firms. Generalized method of moments estimation techniques are applied to a panel data set of UK firms over the period 1986–2011. Tobin′s Q theory is applied to estimate the investment model, which is augmented with measures for both macroeconomic and industry specific uncertainty. Stock price uncertainty seems to be positively related to investment. On the other hand, there is a U shaped relationship between oil price volatility and firm investment. The results will be useful to decision makers, investors, managers and policy makers who need to make investment decisions in an uncertain world.     

Futures trading with information asymmetry and OTC predominance: Another look at the volume/volatility relations in the European carbon markets

This paper constitutes the first exercise of analysing the European carbon market efficiency from a double perspective combining both nature of execution venues (screen vs. OTC trading) and their volatility/liquidity relations. Using a bivariate asymmetric GJR-GARCH model, we first document that OTC (exchange traded) trading volume shows consistent bi-(uni) directional Granger causality to our volatility estimates, consistent with greater responsiveness of the OTC (exchange traded) market to changes in market-wide (idiosyncratic) risks. Second, we report significant contemporaneous and lagged positive causality of OTC derivatives volume on spot/futures volatility confirming that the Sequential Information Arrival Hypothesis (SIAH) applies. Third, we find that the one-way causality from OTC to futures volumes is mainly driven by heterogeneous investor beliefs: trading volume provides an indication on how (private) information is dispersed and held at different levels rather than proxying information signal itself. After rejecting execution venues' substitutability, we advocate for systematic clearing and netting of OTC positions through a unique clearing house and reporting rules to identify speculation in line with Mifid (Art. 59) proposals.     

Extreme Value Theory and Value at Risk: Application to oil market

Recent increases in energy prices, especially oil prices, have become a principal concern for consumers, corporations, and governments. Most analysts believe that oil price fluctuations have considerable consequences on economic activity. Oil markets have become relatively free, resulting in a high degree of oil-price volatility and generating radical changes to world energy and oil industries. Consequently, oil markets are naturally vulnerable to significant high price shifts. An example of such a case is the oil embargo crisis of 1973. In this newly created climate, protection against market risk has become a necessity. Value at Risk (VaR) measures risk exposure at a given probability level and is very important for risk management. Appealing aspects of Extreme Value Theory (EVT) have made convincing arguments for its use in managing energy price risks. In this paper, we model VaR for long and short trading positions in oil market by applying both unconditional and conditional EVT models to forecast Value at Risk. These models are compared to the performances of other well-known modelling techniques, such as GARCH, Historical Simulation and Filtered Historical Simulation. Both conditional EVT and Filtered Historical Simulation procedures offer a major improvement over the conventional methods. Furthermore, GARCH(1, 1)-t model may provide equally good results which are comparable to two combined procedures. Finally, our results confirm the importance of filtering process for the success of standard approaches.     

The macroeconomic effects of oil price fluctuations on a small open oil-producing country: The case of Trinidad and Tobago

Using vector autoregressive (VAR) methodology, this paper empirically investigates the macroeconomic effects of oil price fluctuations on Trinidad and Tobago. Overall, we find that the price of oil is a major determinant of economic activity of the country. Our impulse response functions suggest that following a positive oil price shock, output falls within the first two years followed by positive and growing response. We also investigate the macroeconomic impact of oil price volatility. Results suggest that an unanticipated shock to oil price volatility brings about random swings in the macroeconomy; however, only government revenue and the price level exhibit significant responses. With regard to the magnitude of the responses, shocks to oil price volatility tend to yield smaller macroeconomic impacts in comparison to shocks to oil prices. Variance decompositions suggest that the price of oil is a major component of forecast variation for most macroeconomic variables. Finally, Granger-causality tests indicate causality from oil prices to output and oil prices to government revenue.     

Realized volatility and the influence of market measures on predictability: Analysis of Nord Pool forward electricity data

This is the first paper to utilize intra-daily high-frequency data and to apply known market measures for the prediction of volatility in the Nord Pool electricity forward market. The work is based on recent methods of separating realized volatility into two components: continuous and jump volatilities. In addition, the link between future price volatility and current observable economic variables is examined. The measures—trading volume, time-to-maturity, asymmetric effect from negative shocks, and intra-week seasonality—are assessed to identify improvements in day-ahead predictions. The model where the total variation is separated into its continuous and jump components is compared with the simpler heterogeneous autoregressive model of realized variation both in- and out-of-sample. The results show a strong degree of persistence in realized volatility, and significant impacts from the mentioned market measures when predicting Nord Pool forward price volatility. Hence, there is a clear preference for models accounting for the systematic impact of market measures to improve volatility assessment for tomorrow. Moreover, separating the total variation into continuous and jump components seems potentially useful when predicting day-ahead volatility.     

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.     

Forecasting petroleum futures markets volatility: The role of regimes and market conditions

In this paper we employ regime volatility models to describe time dependency in petroleum markets. Using a sample of NYMEX and ICE futures contracts, we establish the existence of a regime process and link this process to market fundamentals. This formulation results in two distinct states: a highly persistent conditional volatility process, characterised by long memory and low sensitivity to market shocks, and a relatively short-lived nonstationary process with less memory but higher sensitivity to shocks. Moreover, to investigate the relationship between disequilibrium and volatility of oil futures across high and low volatility regimes we use augmented regime GARCH models to address in a realistic way the potential diverse response of volatility to forward curve shocks. The performance of these models is compared to benchmarks, using both statistical tests and risk management loss functions. To test the robustness of the forecasting strategies, we also perform a reality check employing the stationary bootstrap approach. The findings of this paper have important implications for decision making concerning trading and risk management, as well as energy market operations, such as refining and budget planning, by providing valuable information on the oil price volatility dynamics and the ability to predict risk.     

The impact of global oil price shocks on China’s stock returns: Evidence from the ARJI(-ht)-EGARCH model

This paper investigated the impact of global oil price shocks on China’s stock market, using the ARJI(-h_t)-EGARCH model. We separated the volatilities into expected, unexpected and negatively unexpected ones to identify how oil prices influence the stock returns. The results reveal that there are jumps varying in time in China’s stock market, and that China’s stock returns are correlated only with expected volatilities in world oil prices, contrary to previous research. While world oil prices have a positive effect on China’s stock returns, results from this study suggest that this effect is minor.     

Advancing the design of a dynamic petro-dollar currency basket

This study offers Organization of Petroleum Exporting Countries (OPEC) member nations a crude oil pricing currency basket based on currency liquidity, in contrast with prior emphasis on OPEC trading patterns. Motivating the search for an alternative US dollar pricing of crude oil is the significant and inverse relationship (r=−0.82, p<0.01) between the US dollar major currencies index and crude oil price over the period January 1999–March 2009. A dynamically weighted petro-dollar currency basket is proposed based on the five currency claims (US dollar, Euro, British pound, Japanese yen and Swiss franc) and their varying proportions of foreign exchange reserves held by central banks. The major currencies US dollar index is compared against the proposed petro-dollar index to reveal an average US$8.1 billion annual gain in favor of the petro-dollar currency basket, offering OPEC members a revenue stream of diversified and highly liquid currencies to transition away from complete dependence on the US dollar crude oil pricing. The proposed petro-dollar crude oil pricing schema offers OPEC a crude oil price dynamically denominated in currencies reflecting the global use and importance of crude oil. This paper concludes with implementation issues facing a move toward the dynamically weighted petro-dollar crude oil pricing schema.     

Modeling and forecasting the volatility of petroleum futures prices

We investigate volatility models and their forecasting abilities for three types of petroleum futures contracts traded on the New York Mercantile Exchange (West Texas Intermediate crude oil, heating oil #2, and unleaded gasoline) and suggest some stylized facts about the volatility of these futures markets, particularly in regard to volatility persistence (or long-memory properties). In this context, we examine the persistence of market returns and volatility simultaneously using the following ARFIMA–GARCH-class models: ARIMA–GARCH, ARFIMA–GARCH, ARFIMA–IGARCH, and ARFIMA–FIGARCH. Although the ARFIMA–FIGARCH model better captures long-memory properties of returns and volatility, the out-of-sample analysis indicates no unique model for all three types of petroleum futures contracts, suggesting that investors should be careful when measuring and forecasting the volatility (risk) of petroleum futures markets.     

Google search keywords that best predict energy price volatility

Internet search activity data has been widely used as an instrument to approximate trader attention in different markets. This method has proven effective in predicting market indices in the short-term. However, little attention has been paid to demonstrating search activity for keywords that best grab investor attention in different markets. This study attempts to build the best practically possible proxy for attention in the market for energy commodities using Google search data. Specifically, we confirm the utility of Google search activity for energy related keywords are significant predictors of volatility by showing they have incremental predictive power beyond the conventional GARCH models in predicting volatility for energy commodities' prices. Starting with a set of ninety terms used in the energy sector, the study uses a multistage filtering process to create combinations of keywords that best predict the volatility of crude oil (Brent and West Texas Intermediate), conventional gasoline (New York Harbor and US Gulf Coast), heating oil (New York Harbor), and natural gas prices. For each commodity, combinations that enhance GARCH most effectively are established as proxies of attention. The results indicate investor attention is widely reflected in Internet search activities and demonstrate search data for what keywords best reveal the direction of concern and attention in energy markets.     

Relationship between the oil price volatility and sectoral stock markets in oil-exporting economies: Evidence from wavelet nonlinear denoised based quantile and Granger-causality analysis

This paper examines the extent of volatility between oil price and sectoral indices in the Gulf Cooperation Council (GCC) countries by using quantile regression analysis (QRA) for the return's series and denoised series over the period 2006–2017. Four sectors are found to offer diversification opportunities during a high market (i.e., 90th quantile). All the sectors are found interdependent of oil price volatility; however, the bank and insurance sectors are insusceptible to oil price volatility during the 10th, 25th and 75th quantiles. In addition, QRA results for wavelet nonlinear denoising with a soft-thresholding series indicate that all the sectors are interdependent of oil price volatility but that the aggregate market index, transport and telecommunication sectors are insensitive to oil price volatility during the 75th and 90th quantiles. This highlights the usefulness of denoising the financial returns series when applying regression tools. Moreover, the contagion and interdependence between the oil price and stock returns sectors are estimated by frequency domain causality.     

Modeling the price dynamics of CO2 emission allowances

In this paper we analyze the short-term spot price behavior of carbon dioxide (CO₂) emission allowances of the new EU-wide CO₂ emissions trading system (EU ETS). After reviewing the stylized facts of this new class of assets we investigate several approaches for modeling the returns of emission allowances. Due to different phases of price and volatility behavior in the returns, we suggest the use of Markov switching and AR–GARCH models for stochastic modeling. We examine the approaches by conducting an in-sample and out-of-sample forecasting analysis and by comparing the results to alternative approaches. Our findings strongly support the adequacy of the models capturing characteristics like skewness, excess kurtosis and in particular different phases of volatility behavior in the returns.     

Modelling asymmetric volatility in oil prices under structural breaks

This paper shows that accounting for endogenously determined structural breaks within an asymmetric GARCH model reduces volatility persistence in oil prices. More importantly, we find that both good and bad news have significantly more impact on volatility if structural breaks are accounted for in a model. Thus, previous studies have significantly underestimated the impact of news on volatility as they have inadvertently ignored these structural breaks in volatility. Our empirical results suggest that it is best to include both asymmetric effects and structural breaks in a GARCH model to accurately estimate oil price volatility dynamics. Our results have important practical implications not only for option valuation and hedging decisions but also have major consequences for broader financial markets, the energy industry, and the overall economy.