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This paper examines the relationship between spot and futures prices for energy commodities (crude oil, gasoline, heating oil markets and natural gas). In particular, we examine whether futures prices are (1) an unbiased and/or (2) accurate predictor of subsequent spot prices. We find that while futures prices are unbiased predictors of future spot...
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Citations
... The importance of future prices in energy markets has been studied in the literature by [13,14]. Using ARMA, ARIMA, and AIC, the authors of the latter study explore the energy commodities and more specifically the potential connections between the spot and the futures prices. ...
... Finally, the optimal models employ mostly energy-related variables. Thus, we find evidence that macroeconomic indicators, fixed income securities, exchange rates, stock indices, derivative prices, and weather and technical variables do not provide significant information in terms of gasoline forecasting; this is also the finding of [6,14]. ...
This study aims to forecast New York and Los Angeles gasoline spot prices on a daily frequency. The dataset includes gasoline prices and a big set of 128 other relevant variables spanning the period from 17 February 2004 to 26 March 2022. These variables were fed to three tree-based machine learning algorithms: decision trees, random forest, and XGBoost. Furthermore, a variable importance measure (VIM) technique was applied to identify and rank the most important explanatory variables. The optimal model, a trained random forest, achieves a mean absolute percent error (MAPE) in the out-of-sample of 3.23% for the New York and 3.78% for the Los Angeles gasoline spot prices. The first lag, AR (1), of gasoline is the most important variable in both markets; the top five variables are all energy-related. This paper can strengthen the understanding of price determinants and has the potential to inform strategic decisions and policy directions within the energy sector, making it a valuable asset for both industry practitioners and policymakers.
... Multivariate Time Series Models -Multiple linear regression models (LM) with multivariate time series can include trend and seasonality in addition to predictor variables (Hyndman and Athanasopoulos, 2021). Since regression analysis is able to explore the interconnections between gasoline prices and other independent variables such as GDP and CPI, it was used to help interpret models, especially for managerial and policy implications (Chinn, LeBlanc, and Coibion, 2005). Breiman et al (1984) proposed Classification and Regression Tree (CART) in their book Classification and Regression Tree. ...
... Chinn et al. [8] investigated the link between energy commodity spot and futures prices (crude oil, gasoline, heating oil markets and natural gas). They looked at whether futures prices are (1) impartial and/or (2) accurate predictors of spot prices in the future. ...
Oil production forecasting is an important step in controlling the cost-effect and monitoring the functioning of petroleum reservoirs. As a result, oil production forecasting makes it easier for reservoir engineers to develop feasible projects, which helps to avoid risky investments and achieve long-term growth. As a result, reliable petroleum reservoir forecasting is critical for controlling and managing the effective cost of oil reservoirs. Oil production is influenced by reservoir qualities such as porosity, permeability, compressibility, fluid saturation, and other well operational parameters. Three-time series algorithms i.e., Seasonal Naive method, Exponential Smoothening and ARIMA to forecast the Distillate Fuel Oil Refinery and Propane Blender net production for the next two years.
... Other forecasting studies include additional series such as the natural gas futures or oil prices in forecasting natural gas prices (Chinn et al. 2005;García-Martos et al. 2013;Ergen and Rizvanoghlub 2016;Batten et al. 2017). No forecasting study, however, was found that include more than one spot market. ...
... No forecasting study, however, was found that include more than one spot market. Chinn et al. (2005) find future prices do not predict subsequent natural gas price movements at three-month horizon, but future prices model outperforms time-series models (regressing spot prices on lagged own prices and estimated errors) in terms of RMSE. García-Martos et al. (2013) indicate multivariate models for oil, coal, and gas prices forecast more accurate than univariate model. ...
The natural gas sector has undergone major regulatory and technological changes. These changes may induce structural changes in price relationships among natural gas markets. Tests for structural breaks suggest two potential structural breaks, around 2000 and 2009. Previous forecasting studies on natural gas prices/returns largely are point forecasts and focus on a single spot market; unlike those, this study undertakes simultaneous probabilistic forecasts of eight spot markets. Prequential forecasting analysis examines: (1) whether differences exist in the ability to probabilistically forecast returns among various natural gas markets and (2) how the presence of structural breaks in the natural gas sector influences the probability forecasts. The ability to forecast natural gas markets differs based on the different criteria. Disparities may be explained by each market’s role in price discovery, the alteration of the market’s participation, and whether the market is located in an excess supply or demand region. Irrespective of the models, Henry Hub and AECO returns appear to be easier to forecast, as they generally have the smaller root-mean-squared error, Brier score, and ranked probability score, while Dominion South and Chicago returns appear to be more difficult to forecast. Models using longer periods of data appear to forecast returns better than models using data starting after the breaks; the latter always produces the largest root-mean-squared error, Brier score, and ranked probability score.
... Traditional time series models such as SES and MA are the most commonly used forecasting methods for time series data, including crude oil prices, U.S. government statistics, and Wall Street stock prices (Huntington, 1994;Abramson and Finizza, 1995). Since regression analysis requires a set of independent variables (Chinn, LeBlanc, and Coibion, 2005;Yang, Han, Cai, and Wang, 2012) and since such explanatory variables relevant to crude oil prices as gross domestic product (GDP) and consumer price index (CPI) are only available on monthly basis, we exclude regression analysis in this research because there are no weekly government statistics. More advanced ARIMA are the most prominent time series methods, in which autocorrelation function (ACF) and partial autocorrelation function (PACF) are used to help select data driven model parameters (Ord, Fildes, and Kourentzes, 2017). ...
... For instance, the US-led sanctions imposed against Iran in mid-2012 reduced their oil production from 2.4 million barrels a day to 1.4 million barrels a day, which trivially impacted the overall oil price. Traditional approaches for oil price prediction can be categorized into quantitative and qualitative models [1][2][3]. Quantitative models, which include time-series, financial and structural models of historical data are more suited for short to medium term oil price forecasting. For instance, Pindyck [14] used an autoregressive model to forecast crude oil prices from 1887 to 1996, although with quite a poor accuracy level. ...
... Silva et al. [18] implemented a hidden Markov model (HMM) to forecast medium term crude oil price movements and achieved a mean forecasting accuracy of 57%. Chin et al. [3] examined energy futures prices to accurately forecast future spot prices. The findings suggested that future prices are unbiased predictors of spot prices and outperform time-series models. ...
... Dées et al. [18] also, used cause and effect model for the forecasting of crude oil prices. Other researchers such as Liu [19], Chinn et al. [20], Agnolucci [21] and Ahmad [22] used a well-known Box-Jenkin's methodology for forecasting the crude oil prices. Furthermore, some studied have been found which used the GARCH type of models for forecasting the Crude oil prices namely Sadorsky [23], Hou and Suardi [24], Ahmed and Shabri [25]. ...
This paper used complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) based hybrid model for the forecasting of world crude oil prices. For this purpose, the crude oil prices original time series are decomposed into sub small finite series called intrinsic mode functions (IMFs). Then ARIMA model was applied to each extracted IMF to estimate the parameters. Next, using these estimated parameters of each ARIMA model, the Kalman Filter was run for each IMF, so that these extracted IMFs can be predicted more accurately. Finally, all IMFs are combined to get the result. For testing and verification of the proposed method, two crude oil prices were used as a sample i.e. Brent and WTI (West Texas Intermediate) crude oil monthly prices series. The D-statistic values of the proposed model were 93.33% for Brent and 89.29% for WTI which reveals the importance of the CEEMDAN based hybrid model.
... This means that your portfolio will be better positioned to weather stock market declines, will be safeguarded against huge fluctuations in the total portfolio value and will have greater opportunities for higher performance over time. Table I summarizes the reasons why investors should invest in commodities gathered in this research paper from many researchers (Asche and Oglend, 2016;Chinn, 2005;Frush, 2008;Hamilton and Wu, 2014;Huchet and Fam, 2016;Miffre, 2016;Papp et al., 2008;Taylor, 2016). ...
... This means that the portfolio is protected against the negative effect of inflation and the loss of purchasing power. Only a few investments have this benefit (Asche and Oglend, 2016;Chinn, 2005;Frush, 2008;Huchet and Fam, 2016;Miffre, 2016;Papp et al., 2008) Inelastic pricing There are commodities in the marketplace that must be purchased irrespective of their price levels. These commodities are measured as inelastic commodities. ...
... Commodities give investors an opportunity to include assets that bring into line with their risk profile in their asset allocation. By including a commodities element to portfolio, it effectively forms an optimal and diversified portfolio (Chinn, 2005;Etienne et al., 2014;Frush, 2008;Hamilton and Wu, 2014;Huchet and Fam, 2016;Miffre, 2016;Papp et al., 2008;Taylor, 2016) Reduced risk Volatility and bring smoother returns: ...
Purpose: The argument whether gold is a hedge or haven is a debatable issue. Mainly, hedge is a class of asset that is negatively correlated with another asset or portfolio on average. On the other hand, a safe haven is an asset or portfolio which is negatively correlated with another asset or portfolio at the time of market turmoil. Therefore, in this research, we are taking Saudi Arabia as example to examine the relationship of gold price in Saudi Arabia with key determinants such as the stock market index, oil prices, exchange rate, interest rate, and consumer price index (CPI).
Design/methodology/approach: We employ the Autoregressive Distributed Lag model (ARDL) analysis by using six variables based on by application of monthly time series data was collected from 2011 to 2015.
Findings: From our analysis we discover that gold is useful as a portfolio hedge. Also we found that it is a hedge against inflation as because it is not affected by the CPI. External factors, for example financial crisis may be harmful to CPI, thus, adding some percentage of gold in the investment portfolio may assist to decrease the level of risk at the time of financial turmoil.
Originality/value: Since gold seems to be a useful portfolio hedge as well as inflation hedge, government policies to curb the import of gold may be futile. Our research suggests that policies that directly address the causes of inflation and provide alternative investment opportunities for retail investors may better serve the objective of bringing down gold imports.
... In this study, we also test α 0 = 0 and β 0 = 1 restrictions separately, as ensuring β 0 = 1 restriction is crucial in terms of market efficiency. This is because α 0 = 0 restriction may not hold if there exists a constant or time-varying risk premium or transportation costs even when futures markets are efficient (Chin et al., 2005;Kawamoto and Hamori, 2011;McKenzi and Holt, 2002;Wang and Ke, 2005). Therefore, as pointed out by Kawamoto and Hamori (2011), among others, β 0 = 1 restriction represents the null hypothesis of market efficiency, whereas α 0 = 0 and β 0 = 1 joint restriction represents the null hypothesis of market efficiency and unbiasedness. ...
This study examines the West Texas Intermediate crude oil (WTI), Europe Brent crude oil (Brent), heating oil no. 2, and Henry Hub natural gas (NG) futures markets’ efficiency following Fama’s (1970) weak-form efficiency hypothesis, using spot and futures prices at 1, 2, 3, and 4 months maturity based on the tests with unknown multiple regime shifts. The results show that it is important to consider the multiple regime shifts when determining whether energy futures markets are efficient. We find that WTI and Brent futures markets are not efficient, whereas NG and heating oil futures markets are efficient. Additionally, the findings also shed light on discussions about the stationary properties of energy commodities and whether spot and futures prices are cointegrated. In particular, this study presents new evidence based on the unit root and cointegration tests with multiple structural breaks.
... inspires a cash-and-carry strategy, i.e. long the spot and short the forward. The cointegration between spot and forward prices has been studied with the conclusion that the efficiency of crude oil market, as characterized by the no-arbitrage rule, is influenced by the existence of structural breaks (Chen et al. 2014;Chinn et al. 2005;Maslyuk and Smyth 2009). For instance, Chen et al. (2014) found that there is evidence against market efficiency for subsamples 1986-2012 and 1986-2004 within WTI data. ...
The goal is to explain and improve the off-shore oil storage trade observed in a contango market using a forward dynamic optimization strategy. The strategy formulation is developed in terms of trades in forward contracts and contrasted with the literature. By simulating forward prices based on realistic May 2009 market conditions, the NPV of the strategy compared to selling the oil on the spot in a base case scenario is $8.99/barrel, which is comprised of $6.19 generated by the initial forward maximization, and $2.80 achieved by the subsequent trades.
The impact of the forward curve dynamics is studied by examining the trading decisions based on the realized slope and mean level of forward curve. The path of realized slope and step-wise changes in the slope is found to be able to explain most of the trading decisions. The effect of initial conditions, frequency of readjusting the position, and storage cost are also examined. The optimal frequency depends on the storage cost. Friction is introduced in the problem by penalizing the refund of the storage cost when a decision to advance the short position in time is made, where its influence, which depends on the storage cost, is at most $1.40.
