Unit root properties of crude oil spot and futures prices

In this article, we examine whether WTI and Brent crude oil spot and futures prices (at 1, 3 and 6 months to maturity) contain a unit root with one and two structural breaks, employing weekly data over the period 1991–2004. To realise this objective we employ Lagrange multiplier (LM) unit root tests with one and two endogenous structural breaks proposed by Lee and Strazicich [2003. Minimum Lagrange multiplier unit root test with two structural breaks. Review of Economics and Statistics, 85, 1082–1089; 2004. Minimum LM unit root test with one structural break. Working Paper no. 04–17, Department of Economics, Appalachian State University]. We find that each of the oil price series can be characterised as a random walk process and that the endogenous structural breaks are significant and meaningful in terms of events that have impacted on world oil markets.     

Volatility spillovers for spot,futures, and ETF prices in agriculture and energy

The agricultural and energy industries are closely related, both biologically and financially. The paper discusses the relationship and the interactions on price and volatility, and on the covolatility spillover effects for these two industries. The interaction and covolatility spillovers, or the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset, between the energy and agricultural industries is the primary emphasis of the paper. Although there has already been significant research on biofuel and biofuel-related crops, only a few published papers have been concerned with volatility spillovers. It must be emphasized that there have been numerous technical errors in the theoretical and empirical research, which need to be addressed. The paper considers futures prices as a widely-used hedging instrument, and also considers an interesting new hedging instrument, ETF, which is regarded as index futures when investors manage their portfolios. In the empirical analysis, multivariate conditional volatility diagonal BEKK models are estimated for comparing patterns of covolatility spillovers. The paper provides a new way of analyzing and describing the patterns of covolatility spillovers, which should be useful for the future empirical analysis of estimating and testing covolatility spillover effects.     

A comparison of petroleum futures versus spot prices as predictors of prices in the future

Even though studies have indicated that the futures market leads the spot market in price discovery, this paper hypothesizes that the ‘predictive’ significance of each should be the same. A number of different statistical tests are presented to test this hypothesis. When deseasonalized data is employed the predictive significance of each series is found to be the same, but when actual prices are employed, futures prices did correctly anticipate the observed seasonal pattern.     

Re-examining the movements of crude oil spot and futures prices over time

We carry out a (partial) replication exercise of Chang and Lee (2015) unit root and cointegration analyses of crude oil spot and futures prices. In doing so, we offer an updated and expanded analysis based on a much wider set of oil futures series than that considered by Chang and Lee, and we consider the impact of different temporal aggregation methods. Although we are not able to exactly replicate their findings, we nonetheless reach qualitatively similar findings to theirs when using their dataset in terms of the long-run properties and interactions of the spot and futures prices data. Likewise, qualitatively comparable results are obtained when using our expanded dataset. However, a number of important qualitative differences from Chang and Lee arise in terms of the analysis of causality between spot and futures contract prices. As part of our replication exercise we also investigate some aspects that were not originally considered by Chang and Lee. In doing this, we find that both the variability of futures prices as well as the speed of adjustment of futures/spot price differentials increase as the maturity of the contracts increase.     

Oil prices,speculation, and fundamentals: Interpreting causal relations among spot and futures prices

A consensus that the world oil market is unified begs the question, where do innovations in oil prices enter the market? Here we investigate where changes in the price of crude oil originate and how they spread by examining causal relationships among prices for crude oils from North America, Europe, Africa, and the Middle East on both spot and futures markets. Results indicate that innovations first appear in spot prices for Dubai–Fateh and spread to other spot and futures prices while other innovations first appear in the far month contract for West Texas Intermediate and spread to other exchanges and contracts. Links between spot and futures markets are relatively weak and this may have allowed the long-run relationship between spot and future prices to change after September 2004. Together, these results suggest that market fundamentals initiated a long-term increase in oil prices that was exacerbated by speculators, who recognized an increase in the probability that oil prices would rise over time.     

Revisiting the relationship between spot and futures oil prices: Evidence from quantile cointegrating regression

Since most real decisions depend upon current market states or whether it is advantageous to the participants themselves, this paper revisits the relationship between spot and futures oil prices of West Texas Intermediate covering 1986 to 2009 with an innovative approach named quantile cointegration. Different to previous perspectives, we target the issues of cointegrating relationships, causalities, and market efficiency based on different market states under different maturities of oil futures. In our empirical analysis, except for market efficiency, long-run cointegrating relationships and causalities between spot and futures oil prices have significant differentials among futures maturities and the performances of spot oil markets. Furthermore, the response of spot prices to shocks in 1-month futures oil prices is much steeper in high spot prices than in low spot prices. This phenomenon is consistent with the prospect theory (Kahneman and Tversky, 1979), in that the value function is generally steeper for losses than for gains.     

The dynamics of a nonlinear relationship between crude oil spot and futures prices: A multivariate threshold regression approach

This paper segments daily data from January of 1986 to April of 2007 into three periods based on certain important events. Both periods I and II indicate that the spot prices in general are higher than futures prices as was well-known in the literature. Only period-III (2001/9/11–2007/4/30) displays a reverse phenomenon: futures prices, in general, exceed spot prices. When the absolute value of a basis (futures-spot) is greater than the threshold value in the arbitrage area (regime 1 and 3), at least one of the error correction coefficients, representing adjustment towards equilibrium, is statistically significant. That is, there exists a tendency in the oil market in which prices move toward equilibrium. With respect to the short-run dynamic interaction between spot price change (Δs_t) and futures price change (Δf_t), our results indicate that when the spot price is higher than futures price, and the basis is less than certain threshold value (regime 3), there exists at least one causal relationship between Δs_t and Δf_t. Conversely, when the futures price is higher than spot price and the basis is higher than certain threshold value (regime 1), there exists at least one causal relationship between Δs_t and Δf_t. Finally, we use the method suggested by Diebold and Mariano [Diebold, Francis X., Mariano, Roberto S., 1995. Comparing predictive accuracy. Journal of Business and Economic Statistics 13 (3), 253–263] to compare the predictive power between the linear and nonlinear models. Our empirical results indicate that the in-sample prediction of the nonlinear model is clearly superior to that of the linear model.     

Time-varying risk premiums in petroleum futures prices

This paper uses an ARMAX-ARCH model to estimate the conditional expected returns of petroleum futures prices under time-varying risk. Empirical results suggest that macroeconomic risk factors have significant forecast power in petroleum futures markets. The conditional expected returns for petroleum futures prices are quite large. Results from a small forecasting experiment indicate that the out-of-sample forecasts from an ARMAX-ARCH model generally outperform a random walk for all forecast horizons. Regression-based tests for market timing indicate that the model captures both the correct sign and the correct magnitude. Net trading profits are positive in all cases.     

The relationship between spot and contract gas prices in Europe

Following deregulations in the European gas market, spot trading of natural gas has been established in the UK, Belgium and the Netherlands, while long-term contracts remain the dominant pricing process in continental Europe. In this paper we investigate the degree of market integration between the three spot markets, the contract gas price in Germany and the oil price. The results indicate a highly integrated market, and there is no evidence of an independent price determination process for natural gas.     

Tight oil,real WTI prices and U.S. stock returns

Following the adoption of new techniques of shale and fracking by U.S. oil companies, a structural vector autoregression model (SVAR) complements studies on why Brent and WTI started to diverge around early-2011. Using monthly data from 2000 to 2018, we decompose oil supply into: world oil (excluding U.S.), U.S. conventional (non-tight) oil and U.S. tight oil. We examine the variance decomposition of stock returns for the aggregate market (S&P 500), the S&P Energy sector and Chevron and Exxon Mobil oil companies, and we further identify differences between two subsamples from 2000 to 2010 and 2011 to 2018, respectively. We find that supply considerations (especially due to tight oil) become more important in the subsample after 2011, not only for individual oil companies but also for the aggregate market and energy sector: Supply shocks due to tight oil explain in our benchmark model between 29% (S&P 500) and 31% (S&P Energy) of the variance in stock returns after 24 months and between 28% and 29% for oil companies. None of these are statistically significant in the pre-2011 subsample. Among impulse responses, tight oil production responds positively to disruptions in world oil, and U.S. stock returns respond positively to oil price shocks and respond negatively to tight oil shocks which is a further finding while being consistent with the literature. Copula modeling uncovers stronger tail dependences in the second subsample for the interactions during downturns and upturns among global demand, crude oil prices and stock markets.     

An hour-ahead prediction model for heavy-tailed spot prices

We propose an hour-ahead prediction model for electricity prices that capture the heavy tailed behavior that we observe in the hourly spot market in the Ercot (Texas) and the PJM West hub grids. We present a model according to which we separate the price process into a thin-tailed trailing median process and a heavy-tailed residual process whose probability distribution can be approximated by a Cauchy distribution. We show empirical evidence that supports our model.     

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.     

Efficient modeling and forecasting of electricity spot prices

The increasing importance of renewable energy, especially solar and wind power, has led to new forces in the formation of electricity prices. Hence, this paper introduces an econometric model for the hourly time series of electricity prices of the European Power Exchange (EPEX) which incorporates specific features like renewable energy. The model consists of several sophisticated and established approaches and can be regarded as a periodic VAR-TARCH with wind power, solar power, and load as influences on the time series. It is able to map the distinct and well-known features of electricity prices in Germany. An efficient iteratively reweighted lasso approach is used for the estimation. Moreover, it is shown that several existing models are outperformed by the procedure developed in this paper.     

Bayesian estimation of stable CARMA spot models for electricity prices

We develop a Bayesian estimation procedure for the electricity spot price model in Benth et al. (2014). This model incorporates a trend and seasonality component, a stable CARMA process for the price spikes, and an additional Lévy process for mid-range price level changes. Our MCMC algorithm has two advantages over the existing stepwise estimation procedure presented in Benth et al. (2014): First, since our algorithm produces samples from the full posterior distribution over all parameters, we can estimate the parameters much more accurately, which is shown in simulation studies. Second, we can provide accuracy measures as credibility intervals in addition to the point estimates. The approach is quite general, so that it can be adapted also to other similar pricing models. For illustration, we analyse spot and future prices from the EEX using the new Bayesian method and provide estimates for the risk premium together with credibility regions.     

Integration of physical and futures prices in the US natural gas market

This paper examines the integration between the prices of different types of physical (upstream/end-use) and futures contracts of natural gas in the US for the period of June 1990–Dec 2014. To examine the equilibrium relationship between physical and futures prices, several cointegration tests are applied. The study finds that (a) futures prices are cointegrated with wellhead, power, industrial, and citygate prices; (b) NG1 futures prices Granger cause all physical prices; (c) upstream physical prices Granger cause futures prices; (d) shocks to wellhead prices are the only ones among physical prices with persistent long-term effects; (e) shocks to futures prices have persistent effects on all physical prices; (f) futures contracts with a longer time-to-maturity explain a larger portion of commercial gas price variations; and (g) commercial and residential prices show different behavior compared to other physical prices in multiple tests.     

Filtering and forecasting commodity futures prices under an HMM framework

We propose a model for the evolution of arbitrage-free futures prices under a regime-switching framework. The estimation of model parameters is carried out using the hidden Markov filtering algorithms. Comprehensive numerical experiments on real financial market data are provided to illustrate the effectiveness of our algorithm. In particular, the model is calibrated with data from heating oil futures and its forecasting performance as well as statistical validity is investigated. The proposed model is parsimonious, self-calibrating and can be very useful in predicting futures prices.     

Time variation in European carbon pass-through rates in electricity futures prices

The European Union Emissions Trading Scheme is a means to price emission allowances. Electricity market prices should reflect these market prices of emission allowances as they are a cost factor for power producers. The pass-through rate is the fraction of the emission allowance price that is passed through to electricity market prices. It is often measured and presented as an average or a fixed estimate over some time period. However, we expect that the pass-through rates should actually vary over time as electricity supply curves reflect the marginal costs of different producers that differ in emission intensity. We apply a Kalman Filter approach to observe pass-through rates in Germany and U.K. and find strong support for time varying instead of fixed pass-through rates. Although policy makers are interested in the impact of a policy on average, our results indicate that one needs to be careful with the time-frame over which pass-through rates are measured for policy evaluation, as an incorrect chosen evaluation period could cause an under- or overestimation of the pass-through rate. In addition, our model helps to provide policy makers with insight in the development of pass-through rates when market circumstances change with respect to power production.     

A rough multi-factor model of electricity spot prices

We introduce a new continuous-time mathematical model of electricity spot prices which accounts for the most important stylized facts of these time series: seasonality, spikes, stochastic volatility, and mean reversion. Empirical studies have found a possible fifth stylized fact, roughness, and our approach explicitly incorporates this into the model of the prices. Our setup generalizes the popular Ornstein–Uhlenbeck-based multi-factor framework of Benth et al. (2007) and allows us to perform statistical tests to distinguish between an Ornstein–Uhlenbeck-based model and a rough model. Further, through the multi-factor approach we account for seasonality and spikes before estimating – and making inference on – the degree of roughness. This is novel in the literature and we present simulation evidence showing that these precautions are crucial for accurate estimation. Lastly, we estimate our model on recent data from six European energy exchanges and find statistical evidence of roughness in five out of six markets. As an application of our model, we show how, in these five markets, a rough component improves short term forecasting of the prices.     

A combined modeling approach for wind power feed-in and electricity spot prices

Wind power generation and its impacts on electricity prices has strongly increased in the EU. Therefore, appropriate mark-to-market evaluation of new investments in wind power and energy storage plants should consider the fluctuant generation of wind power and uncertain electricity prices, which are affected by wind power feed-in (WPF). To gain the input data for WPF and electricity prices, simulation models, such as econometric models, can serve as a data basis.This paper describes a combined modeling approach for the simulation of WPF series and electricity prices considering the impacts of WPF on prices based on an autoregressive approach. Thereby WPF series are firstly simulated for each hour of the year and integrated in the electricity price model to generate an hourly resolved price series for a year. The model results demonstrate that the WPF model delivers satisfying WPF series and that the extended electricity price model considering WPF leads to a significant improvement of the electricity price simulation compared to a model version without WPF effects. As the simulated series of WPF and electricity prices also contain the correlation between both series, market evaluation of wind power technologies can be accurately done based on these series.     

A conditionally heteroskedastic model with time-varying coefficients for daily gas spot prices

This paper examines the relationship between gas spot prices at the Zeebrugge market, one-month ahead Brent prices and temperatures over the period 2000–2005. A cointegration analysis is carried out and it is discovered that a cointegration relationship exists between the three series. To take into account the influence of temperature on the gas volatility, a GARCH(1,1) model with temperature-dependent coefficients is considered. Stability and estimation properties are discussed. An empirical finding is the existence of distinct volatility regimes for the volatility of gas prices, depending on the temperature level.