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This paper proposes a novel futures market efficiency index which aggregates the efficiencies of futures contracts across their term structure spanning from one month to 12 months. The index measures the ability for price discovery of a long-term futures contract on its nearby short-term contract consistently across its terms. The index uses a recently developed futures market efficiency test which accounts for heteroscedastic prices and time-varying risk premiums. It simultaneously estimates the term premiums of futures, providing valuable information for the investor. The proposed index is employed to investigate the efficiencies of four major energy commodities, namely, crude oil, natural gas, heating oil, and gasoil during the sample period 1990–2016. Numerical results indicate that the market efficiencies vary across terms and across energy commodities. Gasoil futures traded on the Intercontinental Exchange (ICE) are the most efficient and natural gas futures are the least efficient in prices. The spillover dynamics of market risk information across futures markets are investigated using conditionally heteroscedastic common factors (CHCFs) extracted for each commodity using the estimated term premiums. There exist significant delayed, contemporaneous, and potential information spillovers among term premiums of the energy commodities in our sample. Our findings are robust and they can help investors to optimally diversify their portfolios.

International audience; In a first step, we model the multivariate tail dependence structure and spillover effects across energy commodities such as crude oil, natural gas, ethanol, heating oil, coal and gasoline using canonical vine (C-vine) copula and c-vine conditional Value-at-Risk (CoVaR). In the second step, we formulate portfolio strategies based on different performance measures to analyze the risk reduction and diversification potential of carbon assets for energy commodities. We identify greater exposure to losses arising from investments in heating oil and ethanol markets. We also find evidence of carbon asset providing diversification benefits to energy commodity investments. These findings motivate for regulatory adjustments in the trading and emission permits for the energy markets most strongly diversified by carbon assets.

In this study, we estimate the minimum variance (MV) and quantile hedge ratios for three energy-related commodities: crude oil, heating oil and natural gas. For crude oil and heating oil, we find the quantile hedge ratios to have inverted U shape using daily data. However, for natural gas, the quantile hedge ratios are mostly below the MV hedge ratio which is significantly lower compared to naive hedge ratio. Such behavior of hedge ratios for daily data is consistent with our empirical results which suggest that price discovery mostly takes place in the futures market for natural gas. We also estimate the hedge ratios for weekly and four-weekly hedging horizons using non-overlapping data. For the longer horizon, we use wavelet analysis to decompose the return time series into different components with respect to different time-scales. We find that, eventually for longer hedging horizons, the quantile hedge ratios converges to MV hedge ratio. The crude oil takes the shortest time-scale to achieve the convergence and the natural gas takes the longest time-scale. Finally, consistent with other studies, we find the hedging effectiveness to increase with hedging horizon.

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.

Oil markets profoundly influence world economies through determination of prices of energy and transports. Using novel methodology devised in frequency domain, we study the information transmission mechanisms in oil-based commodity markets. Taking crude oil as a supply-side benchmark and heating oil and gasoline as demand-side benchmarks, we document new stylized facts about cyclical properties of the transmission mechanism generated by volatility shocks with heterogeneous frequency responses. Our first key finding is that shocks to volatility with response shorter than one week are increasingly important to the transmission mechanism over the studied period. Second, demand-side shocks to volatility are becoming increasingly important in creating short-run connectedness. Third, the supply-side shocks to volatility resonating in both the long run and short run are important sources of connectedness.

In this study, we empirically analyze to see if the pure martingale hypothesis holds for three energy-related commodities: crude oil, heating oil and natural gas. We also test this hypothesis for five different hedging horizons: 1-day, 1-week, 4-week, 8-week and 12-week. Our empirical results show that the pure martingale hypothesis holds for all three commodities and all five horizons. This implies that the expected return on futures contract can be ignored in determining the optimal hedge ratio. We also test to see if the joint normality between futures and spot returns holds for the same three commodities and five hedging horizons. We reject the joint normality hypothesis for all three commodities and five hedging horizons. This implies that hedgers with different utility function have different optimal hedge ratios. Thus, in general, one needs to take into account of hedger's utility function when deriving optimal hedge ratio. Our results are robust to pre- and post-financial crisis as well as some other specifications considered in the paper.

The traditional approach to hedging the crude oil refining margin (crack spread) adopts a fixed 3:2:1 ratio between the futures positions of crude oil, gasoline, and heating oil. However, hedging the latter in arbitrary proportions might be more effective under some conditions. The paper constructs optimal hedging strategies for both scenarios during the periods of relatively stable and volatile oil prices observed in recent years. Minimization of downside risk (LPM 2 ) and variance are used as alternative hedging objectives. The joint distribution of spot and futures price log returns is modeled using a kernel copula method. The hedging performance of the constructed strategies is compared using hedging effectiveness, expected profit, and expected shortfall. The results show that allowing for arbitrary proportions in the sizes of futures positions generally achieves a better hedging performance. The advantage becomes particularly important during periods characterized by greater variation of the cross-dependence between the price log returns of individual commodities. In addition, using LPM 2 as a hedging criterion can help hedgers to better track downside risk as well as lead to higher expected profit and lower expected shortfall.

We apply an encompassing framework to assess the viability of hedging spot biodiesel price risk for four U.S. markets with a conventionally used heating oil futures contract and a soybean oil futures contract based on the logic that supply shifts (i.e., price of soybean oil as an input) drive biodiesel prices when binding blending mandates are in place. Results indicate that soybean oil futures should in fact be part of a composite hedge, and that in some instances greater hedging weight should be placed on the soybean oil futures contract than the conventionally used heating oil futures contract.

We present an empirical examination of the relation between real spot Brent oil prices and real spot petroleum product prices, specifically gasoline and heating oil prices. Based upon weekly price data spanning a 30-year period ending in April 2019, we provide consistent evidence of Granger causality running from oil prices to product prices accounting for both non-stationarity of the series, and separately conditional heteroskedasticity. No evidence of Granger causality from product prices to oil prices is found for the full sample period nor the period up to the end of 2005, but evidence that gasoline prices Granger-caused oil prices is found for post-2005. Similar results are found for an extended model that also includes potentially endogenous real market variables related to supply and demand in the oil, heating oil and gasoline markets. Given the reduced form model results that oil prices Granger-cause gasoline and heating oil prices, we specify and estimate recursively identified structural vector autoregressive models that specifically account for structural supply and demand shocks in the oil market as well as the petroleum product markets. The oil market block follows Kilian (2009) . We find that real spot gasoline prices and real heating oil prices do not respond to structural oil supply shocks, respond transiently to global commodity demand shocks, but do respond to oil-specific demand shocks. We conclude the result that spot oil prices Granger-cause spot gasoline and heating oil prices largely occurs through the channel of oil-specific demand shocks.

Potential scenarios for the forest bioeconomy are heavily reliant on price assumptions; in particular, any abrupt changes in prices have a profound impact on the relevancy of any sector analysis. The objective of this paper was to demonstrate a new forest sector approach for incorporating price uncertainties in order to improve our assessment of investment decision making alternatives. Methodologically, we linked a multivariate generalized autoregressive conditional heteroscedasticity model (mGARCH (1,1)) with three global land use scenarios that are of strategic importance to the forest bioeconomy. The three scenarios were formulated as i) a business as usual scenario, ii) a high biomass usage scenario and iii) a no-growth scenario. Our results indicate an upward trend in prices over time for all three scenarios and for most woody biomass commodities. Under all scenarios, price volatility in the forest sector would be smaller than that for the fossil fuel energy (i.e. oil and natural gas). Price volatilities from fossil fuel markets are positively influencing woody biomass price volatility and positively influencing pulp volatility. These results are discussed in the context of a case study describing investment alternatives for a district heating facility with options for: woody biomass, natural gas, or heating oil.

Taking the complex property of nonlinear feedback connectivity into consideration, the goal of this paper is to apprehend the interdependences between the financial and energy sectors. Our contribution is both theoretical and methodological. We conduct a multivariate analysis employing nonlinear tools, namely the Partial Transfer Entropy and the Asymmetric Mackey-Glass causality test. In particular, we build a system comprising the petroleum complex (crude oil, gasoline and heating oil), the S&P500 index and the 1-month futures-spot spread for crude oil. By adopting a rolling-window approach, we observe a persistent lead-lag relationship between the S&P500 index and the market participants' expectations for crude oil, from 2004 to 2009. Depending on the bubble period in the stock market, it appears that the resulting coupling becomes subject to the deterioration of global economic activity, induced by large common shocks.

There is not one primary energy market and the price of oil is not a good proxy for many alternative energy prices. Therefore, this paper explores the effects of alternative energy price shocks on economic activity, and examines the relative performance of these alternative measures in predicting state level economic activity using the Davidson and MacKinnon J-test. The alternative energy price series considered are as follows: gasoline, diesel, natural gas, heating oil, and electricity. Alternative measures of energy price shocks produce different patterns of impulse responses than oil price shocks. Additionally, overwhelming evidence indicates that alternative energy price models, excluding models containing the price of gasoline, outperform the baseline model containing the price of oil for many states, particularly at short-to-mid forecast horizons. Using alternative energy prices should lead to a more accurate modeling of the energy price–macroeconomy relationship.

Based on daily data from 28 June 1990 to 31 October 2018, we investigate the asymmetric effects of geopolitical risks on energy (crude oil, gas and heating oil) returns and volatility under different market conditions by using a quantile regression model. The results show that geopolitical risks have significantly negative effects on crude oil returns in the bearish market and on heating oil returns in the normal and bullish markets but have no effects on gas returns. Meanwhile, the impacts of geopolitical risks on crude oil volatility are significantly positive in different market conditions, and the impacts on gas and heating oil volatility are mainly negative but not significant. In addition, we further divide geopolitical risks into geopolitical threats and geopolitical acts and find that geopolitical threats and acts have heterogeneous effects on energy returns and volatility.

This study examines the trend properties in energy price series using weekly spot price data for crude oil, heating oil, and regular gasoline. In particular, the procedures proposed by Perron and Yabu (2009b) are employed to test for a one-time break in the trend function of each price series with no prior knowledge of whether the noise component is stationary or has an autoregressive unit root. Based on the results of the break estimate, the unit root test developed by Kim and Perron (2009) is performed to examine the stationarity of the prices. Finally, we extend the one-break analysis to the case with multiple breaks by employing the break test proposed by Kejriwal and Perron (2010) and the unit root test of Carrion-i-Silvestre et al. (2009). The results consistently demonstrate evidence of structural breaks and reject the unit root null hypothesis for all the price series, suggesting that energy prices are persistently influenced by long-term economic fundamentals instead of temporal policy changes.

To understand the crude oil volatility has been a challenge. The non-linear behavior, the skewed and leptokurtic returns, the presence of structural breaks and the constant political instability in suppliers' countries evidence the necessity of complex models to capture the market volatility. At the same time, crude oil is the raw material for several fuels such as jet fuel, gasoline, diesel and others, having a strong influence over their prices. Thus, this study aims to verify the presence of structural breaks in the volatility series and in the correlations between WTI return and the returns of Gasoline, Kerosene Jet Fuel, Diesel, Heating Oil, Propane and Natural Gas. To reach this objective, we identified which model presents the best fit to estimate the conditional mean between WTI and each fuel and we used a Copula–DCC–GARCH model to estimate the conditional volatility avoiding the frequently unrealistic presumptions of normality. Our main results indicate the necessity of a different model for each analyzed pair and the presence of at least one structural break in the conditional volatility and in the correlation between WTI and each fuel, usually preceded by a structural break in WTI return series.

This paper applies nonparametric methods to identify jumps in daily futures prices and intraday jumps surrounding inventory announcements of crude oil, heating oil and natural gas contracts traded on the New York Mercantile Exchange. The sample period of our intraday data covers January 1990 to January 2008. We have obtained several interesting empirical results. (1) Large volatility days are often associated with large jump components, and large jump components are often associated with the Energy Information Administration's inventory announcement dates. (2) The volatility jump component is less persistent than the continuous sample path component. (3) Volatility and trading volume are higher on days with a jump at the inventory announcement than on days without a jump at the announcement. Furthermore, the intraday volatility returns to normal faster following inventory announcements with jumps than after announcements without jumps.

We analyze the time-varying volatility and spillover effects in crude oil, heating oil, and natural gas futures markets by incorporating changes in important macroeconomic variables and major political and weather-related events into the conditional variance equations. We allow asymmetric responses to random disturbances in each market as well as to good and bad economic news related to the overall economy. Results show the presence of asymmetric effects in both random disturbances and macroeconomic variables. A bidirectional volatility spillover effect is found between natural gas and crude oil and between the natural gas and heating oil markets. Crude oil volatility is found to increase following major political, financial, and natural events. Seasonality and day-of-the-week effects are found in the crude oil and heating oil markets.

This study aims to investigate the presence of long-range dependence in energy futures markets. Using a daily dataset covering from 1990 to 2013 (which includes crucial events for energy markets such as invasion of Iraq and global financial crisis of 2008), we estimate time-varying generalized Hurst exponents of several energy futures contracts with different times to maturity using a rolling window approach. Results reveal that efficiency of energy futures markets is clearly time-varying and changes drastically over the sample period. For futures contracts with 1–4 months to maturities, crude oil and gasoline are found to be more efficient compared to others. On the other hand, for contracts with 5–9 months to maturities, crude oil and natural gas futures are more efficient. For almost every different month to maturity, heating oil and gas oil futures are found to be the least efficient markets. Moreover in general, the efficiency of energy futures markets is found to be decreasing dramatically when time to maturity is increasing. Several implications are discussed.

In this study, we empirically analyze the price discovery process in the futures and spot markets for crude oil, heating oil and natural gas using daily closing prices. We use two different information share measures that are based on the methods proposed by Gonzalo and Granger (1995) and Lien and Shrestha (2014). Both measures indicate that almost all the price discovery takes place in the futures markets for the heating oil and natural gas. However, for the crude oil, the price discovery takes place both in the futures and spot markets. As a whole, our study indicates that futures markets play an important role in the price discovery process.

In this paper we investigate two types of asymmetries, i.e., the asymmetry in the lower and upper tail dependences and the asymmetry in the propagation of crisis (bubble), between crude oil market and refined petroleum markets based on copula models. Thirteen copula models with different types of dependence structures and time-varying dependence parameters are considered. We find that in general the MALM copula fits our sample data best based on AIC criterion. We find that lower and upper tail dependences are both positive indicating that crude oil and refined product markets tend to move together. Using the asymmetric copulas, we find asymmetry in tail dependence between crude oil and heating oil returns and that between crude oil and jet fuel returns. Interestingly, upper tail dependence is significantly greater than the lower tail dependence for the pre-crisis period, while the result is reversed for the post-crisis period. Finally, although our data prefers the nonexchangeable MALM copula, the asymmetry in the propagation of crisis (bubble) between crude oil and refined product returns is very weak.

This paper investigates the cross hedging performance of several oil forwards contracts using WTI, Brent, gasoil and heating oil to manage jet-fuel spot price exposure. We apply three econometric techniques that have been widely tested and applied in the cross hedging literature on foreign exchange and stock index futures markets. Using quotes from the financial industry on forward contracts, we can show that the optimal cross hedging instrument depends on the maturity of the instrument's forwards contract. The results highlight that the standard approach in the literature to use crude oil as a cross hedge is not optimal for time horizons of three months or less. By contrast, for short hedging horizons our results indicate that gasoil forwards contracts represent the highest cross hedging efficiency for jet-fuel spot price exposure, while for maturities of more than three months, the predominance of gasoil diminishes in comparison to WTI and Brent.

Using high frequency data, this paper first time comprehensively examines the intraday efficiency of four major energy (crude oil, heating oil, gasoline, natural gas) futures markets. In contrast to earlier studies which focus on in-sample evidence and assume linearity, the paper employs various nonlinear models and several model evaluation criteria to examine market efficiency in an out-of-sample forecasting context. Overall, there is evidence for intraday market inefficiency of two of the four energy future markets (heating oil and natural gas), which exists particularly during the bull market condition but not during the bear market condition. The evidence is also robust against the data-snooping bias and the model overfitting problem, and its economic significance can be very substantial.

The goal of this paper is to present a model for the joint evolution of correlated commodity forward curves. Each forward curve is directed by two state variables, namely slope and level, and the model is meant to capture both the local and global dependence structures between slopes and levels. Our framework can be interpreted as an extension of the concept of cointegration to forward curves . The model is applied to a US database of heating oil and natural gas futures prices over the period February 2000–February 2009. We find the long-run slope and level relationships between natural gas and heating oil markets, analyze the lead and lag properties between the two energy commodities, the volatilities and correlations between their daily co-movements and evaluate the robustness of these observations to the turmoil experienced by energy markets since 2003.

Crude and refined oil prices have been relatively high and volatile on a sustained basis since 1999. This paper considers the pass through of oil prices into consumer liquid (i.e. petrol, diesel and heating) fuel prices in such an environment. The pass through of oil prices into consumer liquid fuel prices has already been addressed extensively in the literature. Nonetheless much of this literature has either focused on the United States or on a time period when oil prices were relatively stable, or has used monthly data. The main contribution of this paper is a comprehensive combination of many features that have been considered before but rarely jointly. These features include: (1) the analysis of the euro area as an aggregate and a large number of countries (the initial 12 member states); (2) the consideration of different time periods; (3) the modelling of the data in raw levels rather than in log levels. This turns out to have important implications for our findings; (4) the use of high frequency (weekly) data, which, as results will suggest, are the lowest frequency one should consider; (5) the investigation of the different stages of the production chain from crude oil prices to retail distribution — refining costs and margins, distribution and retailing costs and margins; (6) the examination of prices including and excluding taxes — excise and value-added; (7) the modelling of prices for three fuel types — passenger car petrol and diesel separately and home heating fuel oil; (8) lastly we also address the issue of possible asymmetries, allowing for the pass through to vary according to (a) whether price are increasing or decreasing and (b) whether price levels are above or below their equilibrium level. The main findings are as follows: First, as distribution and retailing costs and margins have been broadly stable on average, the modelling of the relationship between consumer prices excluding taxes and upstream prices in raw levels rather than in logarithms has important implications for the stability of estimates of pass through when oil price levels rise significantly. Second, considering spot prices for refined prices improves significantly the fit of the estimated models relative to using crude oil prices. It also results in more economically meaningful results concerning the extent of pass through. Third, oil price pass through occurs quickly, with 90% occurring within three to five weeks. Fourth, using a relatively broad specification allowing for asymmetry in the pass through from upstream to downstream prices, there is little evidence of statistically significant asymmetries. Furthermore, even where asymmetry is found to be statistically significant, it is generally not economically significant. Lastly, these results generally hold across most euro area countries with few exceptions.

We model the joint movements of daily returns on one-month futures for crude oil, heating oil and natural gas through the multivariate GARCH with dynamic conditional correlations and elliptical distributions introduced by Pelagatti and Rondena [Pelagatti, M.M., Rondena, S., 2007. “Dynamic Conditional Correlation with Elliptical Distributions”, unpublished manuscript. Universita di Milano — Bicocca, August]. Futures prices of crude and heating oil covary strongly. The conditional correlation between the futures prices of natural gas and crude oil has been rising over the last 5 years. However, this correlation has been low on average over two thirds of the sample, suggesting that future markets have no established tradition of pricing natural gas as a function of developments on oil markets.

The choice of an appropriate distribution for return innovations is important in VaR applications owing to its ability to directly affect the estimation quality of the required quantiles. This study investigates the influence of fat-tailed innovation process on the performance of one-day-ahead VaR estimates using three GARCH models (GARCH-N, GARCH-t and GARCH-HT). Daily spot prices of five energy commodities (WTI crude oil, Brent crude oil, heating oil #2, propane and New York Harbor Conventional Gasoline Regular) are used to compare the accuracy and efficiency of the VaR models. Empirical results suggest that for asset returns that exhibit leptokurtic and fat-tailed features, the VaR estimates generated by the GARCH-HT models have good accuracy at both low and high confidence levels. Additionally, MRSB indicates that the GARCH-HT model is more efficient than alternatives for most cases at high confidence levels. These findings suggest that the heavy-tailed distribution is more suitable for energy commodities, particularly VaR calculation.

We investigate whether the daily evolution of the term structure of petroleum futures can be forecasted. To this end, the principal components analysis is employed. The retained principal components describe the dynamics of the term structure of futures prices parsimoniously and are used to forecast the subsequent daily changes of futures prices. Data on the New York Mercantile Exchange (NYMEX) crude oil, heating oil, gasoline, and the International Petroleum Exchange (IPE) crude oil futures are used. We find that the retained principal components have small forecasting power both in-sample and out-of-sample. Similar results are obtained from standard univariate and vector autoregression models. Spillover effects between the four petroleum futures markets are also detected.

The Commodity Futures Trading Commission (CFTC)'s Commitments of Traders (COT) data are examined for crude oil, unleaded gasoline, heating oil, and natural gas futures contracts. The collection procedures for the COT data are first examined, followed by Granger causality tests to determine if relationships between trader positions and market prices exist. A positive correlation between returns and positions held by noncommercial traders, and a negative correlation between commercial positions and market returns, are found. Furthermore, positive returns result in an increase in noncommercial net positions in the following week, whereas the net long positions held by commercial hedgers decline following price increases. However, traders' net positions do not lead market returns in general.

The strong long-run relationships among the petroleum spot and futures prices have weakened after the Asian crisis as manifested in less co-integration among these prices. In the post-crisis period, the directional causal relationships have either changed the direction as in the case of WTI crude or weakened somewhat as in the case of NYMEX gasoline and heating oil. These changes should complicate the job of those who try to benefit from predicting the movements of petroleum prices after the Asian crisis. In the international gasoline spot markets, movements in the NYMEX gasoline price are found to precede movements in the Gulf Coast and Rotterdam gasoline prices in the pre-crisis period, and the movements of these prices and those of the Singapore price in the post-crisis period, implying that the NYMEX price is the gasoline leader in both periods. The Monday/Friday day-of-the-week effect is only significant for the NYMEX gasoline 1-month and 3-month futures prices and the Gulf Coast gasoline spot. Economic and political shocks related to the spot prices have greater impacts on all prices than shocks related to the futures prices, regardless of the petroleum type and the time period.

We test for the presence of low-dimensional chaotic structure in crude oil, heating oil, and unleaded gasoline futures prices from the early 1980s. Evidence on chaos will have important implications for regulators and short-term trading strategies. While we find strong evidence of non-linear dependencies, the evidence is not consistent with chaos. Our test results indicate that ARCH-type processes, with controls for seasonal variation in prices, generally explain the non-linearities in the data. We also demonstrate that employing seasonally adjusted price series contributes to obtaining robust results via the existing tests for chaotic structure. Maximum likelihood methodologies, that are robust to the non-linear dynamics, lend support for Samuelson's hypothesis on contract-maturity effects in futures price-changes. However, the tests for chaos are not found to be sensitive to the maturity effects in the futures contracts. The results are robust to controls for the oil shocks of 1986 and 1991.

This paper concerns the German and French heating oil market and attempts to establish long- and short-term relationships between German and French monthly heating oil prices in dollars, the Rotterdam spot price for the same product and the DM/US$ and FF/US$ exchange rates during the period from January 1987 to December 1997. To model the market over the period under consideration, incorporating the Gulf War, we have used conventional unit root tests and sequential tests allowing structural changes. Long-term relationships, with shifts in regime detected by cointegration tests taking structural breaks into consideration, are estimated. The short-term dynamics defined by a vector error correction (VEC) mechanism is derived in a classic manner when in presence of a cointegrated VAR system. The econometric results obtained are commented on from an economic point of view. Weak exogeneity tests are performed and the conditional VEC model is deduced, enabling measurement of the instantaneous impact of variations in weakly exogenous exchange rates on variations in heating oil prices in Germany and France. Lastly, a study is made of the asymmetric reaction of domestic prices to positive and negative variations in exchange rates and the Rotterdam spot quotation.

This paper investigates the interaction between energy futures prices and exchange rates. Results are presented to show that futures prices for crude oil, heating oil and unleaded gasoline are co-integrated with a trade-weighted index of exchange rates. This is important because it means that there exists a long-run equilibrium relationship between these four variables. Granger causality results for both the long- and short-run are presented. Evidence is also presented that suggests exchange rates transmit exogenous shocks to energy futures prices.

This paper presents evidence concerning the number of common stochastic trends in a system of three petroleum futures prices (crude oil, heating oil and unleaded gasoline) using daily data from 3 December 1984 to 30 April 1993. Johansen's maximum likelihood approach for estimating long-run relations in multivariate vector autoregressive models is used. The results indicate the presence of only one common trend.

This paper investigates how German consumer and wholesale prices for gasoline and light heating oil react to changes in the corresponding Rotterdam prices. Monthly data from 1972 to 1989 are employed, and the subperiods of the 1970s (1972 to 1979) and 1980s (1980–1989) are distinguished. Error correction models are estimated to seperate long-run equilibrium relations and short-run adjustment processes. For the 1980s, we find rapid, symmetric and full adjustment of the German prices to the Rotterdam prices, whereas during the 1970s we find some asymmetries, and at least for gasoline the adjustment processes took considerably more time.

House heating consumes a significant quantity of fuel. But, the author argues. studies in this area have so far been unsatisfactory because they have lacked a sound basis in economic theory. Here the author develops a house heating fuel consumption model which incorporates fuel price, consumers' income and utility functions, and a heat production function which treats the house as a firm producing heat levels. The concept of an attainable heat function is introduced. This links heat levels with the amount consumers can spend on fuel and its omission has been a major failing of previous studies. The model is investigated empirically and hypotheses about fuel price, real incomes, temperature, and insulation are tested.

The Energy Information Administration recently published data they collected from the National Interim Energy Consumption Survey (NIECS). NIECS includes detailed information on 4081 individual households: demographic characteristics, energy-related features of the structure, heating equipment and appliances therein, recent conservation actions taken by the household, and fuel consumption and costs for the April 1978–March 1979 one-year period. This data set provides a new and valuable resource for analysis. We summarized and analysed the NIECS data on household energy consumption — total energy use, electricity use, and use of the primary space heating fuel. The regression equations constructed explain roughly half the variation in energy use among households. These equations contain 10 or fewer independent variables, the most important of which are fuel price, year house was built, floor area, and heating degree days.

The structural stability of energy demand relationships is an important question in light of the events of the 1970s. This paper looks at this issue for two energy types, motor gasoline and distillate fuel oil (home heating oil). From the results of a statistical test developed by Brown, Durbin and Evans, it appears that the demand for motor gasoline destabilized following the 1979 Iranian revolution while the demand for distillate fuel oil destabilized following the very severe winter of 1976–1977. Once destabilized, neither of the demand relationships returned to their prior form.