The Predictive Characteristics of Energy Futures: Recent Evidence for Crude Oil, Natural Gas, Gasoline and Heating Oil

2001 ◽  
Author(s):  
Menzie David Chinn ◽  
Michael LeBlanc ◽  
Olivier Coibion
Keyword(s):  
Natural Gas ◽  
Crude Oil ◽  
Energy Futures ◽  
Heating Oil

Can energy commodities affect energy blockchain-based cryptos?

2019 ◽  
Vol 36 (4) ◽  
pp. 682-699 ◽  
Author(s):  
Ikhlaas Gurrib
Keyword(s):  
Natural Gas ◽  
Crude Oil ◽  
Autoregressive Model ◽  
Structural Breaks ◽  
Price Index ◽  
Principal Component ◽  
Commodity Price ◽  
Var Model ◽  
Content Type ◽  
Heating Oil

Purpose The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market. Design/methodology/approach Using principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices. Findings Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model. Research limitations/implications Significant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil. Originality/value As per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets.

Dynamic co-movements and directional spillovers among energy futures

2020 ◽  
Vol 37 (4) ◽  
pp. 673-696 ◽  
Author(s):  
Sercan Demiralay ◽  
Nikolaos Hourvouliades ◽  
Athanasios Fassas
Keyword(s):  
Crude Oil ◽  
Futures Markets ◽  
Spillover Effects ◽  
Time Varying ◽  
Content Type ◽  
Energy Futures ◽  
Risk Transfer ◽  
Volatility Transmission ◽  
Heating Oil ◽  
Practical Implications

Purpose This paper aims to examine dynamic equicorrelations (DECO) and directional volatility spillover effects among four energy futures markets, namely, West Texas Intermediate crude oil, heating oil, natural gas and reformulated blendstock for oxygenate blending gasoline, by using a multivariate fractionally integrated asymmetric power ARCH–DECO–generalized autoregressive conditional heteroskedasticity (GARCH) model and the spillover index technique. Design/methodology/approach The empirical analysis uses the dynamic equicorrelation model of Engle and Kelly (2012) to examine time-varying correlations at equilibrium. The authors further analyze dynamic volatility transmission among energy futures by using Diebold and Yilmaz (2012) dynamic spillover index based on generalized value-at-risk framework. Findings The empirical results provide evidence of heightened equicorrelations at times of financial turmoil. More specifically, the dynamic spillover analysis shows that volatility is transmitted predominantly from crude oil to the other markets and risk transfer among four markets exhibits asymmetries. Spillovers are found to be highly responsive to dramatic events such as the 9/11 terror attack, 2008–2009 global financial crisis and 2014–2016 oil glut. Practical implications The results of this study have important practical implications for investors, portfolio managers and energy policymakers as the presence of time-varying co-movements and spillovers suggests the need for dynamic trading strategies. There are also implications regarding risk management practices, as there is evidence of increased volatility transmission at times of financial turmoil and uncertainty. Finally, the results provide insights to policymakers in a better understanding of the spillover dynamics. Originality/value This paper investigates the DECOs and spillover effects among crude oil, natural gas, heating oil and gasoline futures markets. To the best of the knowledge, this is one of a few studies that examine co-movements and risk transfer in energy futures in a comprehensive framework.

scholarly journals Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models

2018 ◽  
Vol 11 (2) ◽  
pp. 30 ◽  
Author(s):  
Samet Gunay ◽  
Audil Khaki
Keyword(s):  
Natural Gas ◽  
Normal Distribution ◽  
Stable Distributions ◽  
Energy Markets ◽  
Trading Strategies ◽  
Fat Tails ◽  
Oil Futures ◽  
Energy Futures ◽  
Heating Oil ◽  
Risk Managers

Precise modeling and forecasting of the volatility of energy futures is vital to structuring trading strategies in spot markets for risk managers. Capturing conditional distribution, fat tails and price spikes properly is crucial to the correct measurement of risk. This paper is an attempt to model volatility of energy futures under different distributions. In empirical analysis, we estimate the volatility of Natural Gas Futures, Brent Oil Futures and Heating Oil Futures through GARCH and APARCH models under gev, gat and alpha-stable distributions. We also applied various VaR analyses, Gaussian, Historical and Modified (Cornish-Fisher) VaR, for each variable. Results suggest that the APARCH model largely outperforms the GARCH model, and gat distribution performs better in modeling fat tails in returns. Our results also indicate that the correct volatility level, in gat distribution, is higher than those suggested under normal distribution with rates of 56%, 45% and 67% for Natural Gas Futures, Brent Oil Futures and Heating Oil Futures, respectively. Implemented VaR analyses also support this conclusion. Additionally, VaR test results demonstrate that energy futures display riskier behavior than S&P 500 returns. Our findings suggest that for optimum risk management and trading strategies, risk managers should consider alternative distributions in their models. According to our results, prices in energy markets are wilder than the perception of normal distribution. In this regard, regulators and policy makers should enhance transparency and competitiveness in the energy markets to protect consumers.

scholarly journals Volatility Spillovers: Evidence On U.S. Oil Product Markets

2012 ◽  
Vol 28 (6) ◽  
pp. 1237 ◽  
Author(s):  
Bernard Ben Sita ◽  
Salah Abosedra
Keyword(s):  
Natural Gas ◽  
Crude Oil ◽  
Transmission Mechanism ◽  
Font Size ◽  
Product Markets ◽  
Volatility Spillovers ◽  
Oil And Natural Gas ◽  
Heating Oil ◽  
Energy Products ◽  
The Relationship

<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; mso-pagination: none;" class="MsoNormal"><span lang="EN-CA" style="color: black; font-size: 10pt; mso-themecolor: text1; mso-ansi-language: EN-CA;"><span style="font-family: Times New Roman;">This paper provides evidence on the lead, the contemporaneous and the lagged transmission mechanism of extreme shocks across energy products. Our findings reveal a weak leadership of crude oil in guiding hedgers against risk that is specific to natural gas whose changes show a weak reliance on changes in crude oil. Moreover, our findings are consistent with the competitive use of energy products. It follows that substitutability characterizes the relationship between heating oil and natural gas when extreme standardized shocks are considered.<span style="mso-spacerun: yes;"> </span></span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span>

scholarly journals A Proposal to Fix the Number of Factors on Modeling the Dynamics of Futures Contracts on Commodity Prices

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 973
Author(s):  
Andrés García-Mirantes ◽  
Beatriz Larraz ◽  
Javier Población
Keyword(s):  
Natural Gas ◽  
Crude Oil ◽  
Commodity Prices ◽  
Spot Price ◽  
Futures Prices ◽  
Unleaded Gasoline ◽  
Energy Derivatives ◽  
Number Of Factors ◽  
Two Factors ◽  
Heating Oil

In the literature on modeling commodity futures prices, we find that the stochastic behavior of the spot price is a response to between one and four factors, including both short- and long-term components. The more factors considered in modeling a spot price process, the better the fit to observed futures prices—but the more complex the procedure can be. With a view to contributing to the knowledge of how many factors should be considered, this study presents a new way of computing the best number of factors to be accounted for when modeling risk-management of energy derivatives. The new method identifies the number of factors one should consider in the model and the type of stochastic process to be followed. This study aims to add value to previous studies which consider principal components by assuming that the spot price can be modeled as a sum of several factors. When applied to four different commodities (weekly observations corresponding to futures prices traded at the NYMEX for WTI light sweet crude oil, heating oil, unleaded gasoline and Henry Hub natural gas) we find that, while crude oil and heating oil are satisfactorily well-modeled with two factors, unleaded gasoline and natural gas need a third factor to capture seasonality.

Analyzing the financial and economic momentum as a result of changes in the interest rate

2020 ◽  
Vol 16 (9) ◽  
pp. 1656-1673
Author(s):  
V.V. Smirnov
Keyword(s):  
Interest Rate ◽  
Natural Gas ◽  
Central Bank ◽  
Crude Oil ◽  
Oil Prices ◽  
Global Capitalism ◽  
Positive Effects ◽  
Russian Economy ◽  
Oil And Natural Gas ◽  
The Interest Rate

Subject. The article discusses financial and economic momenta. Objectives. I determine financial and economic momenta as the interest rate changes in Russia. Methods. The study is based on a systems approach and the method of statistical analysis. Results. The Russian economy was found to strongly depend on prices for crude oil and natural gas, thus throwing Russia to the outskirts of the global capitalism, though keeping the status of an energy superpower, which ensures a sustainable growth in the global economy by increasing the external consumption and decreasing the domestic one. The devaluation of the national currency, a drop in tax revenue, etc. result from the decreased interest rate. They all require to increase M2 and the devalued retail loan in RUB, thus rising the GDP deflator. As for positive effects, the Central Bank operates sustainably, replenishes gold reserves and keeps the trade balance (positive balance), thus strengthening its resilience during a global drop in crude oil prices and the COVID-19 pandemic. The positive effects were discovered to result from a decreased in the interest rate, rather than keeping it low all the time. Conclusions and Relevance. As the interest rate may be, the financial and economic momentum in Russia depends on the volatility of the price for crude oil and natural gas. Lowering the interest rate and devaluing the national currency, the Central Bank preserves the resource structure of the Russian economy, strengthens its positions within the global capitalism and keeps its status of an energy superpower, thus reinforcing its resilience against a global drop in oil prices.

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