State heating oil and propane price survey: A review of Winter 1995/96

El Niño, La Niña, and the Forecastability of the Realized Variance of Heating Oil Price Movements

Sustainability ◽

10.3390/su13147987 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7987

Author(s):

Mehmet Balcilar ◽

Elie Bouri ◽

Rangan Gupta ◽

Christian Pierdzioch

Keyword(s):

El Niño ◽

Predictive Value ◽

El Nino ◽

Southern Oscillation ◽

La Niña ◽

Realized Variance ◽

La Nina ◽

Out Of Sample ◽

Heating Oil ◽

Price Movements

We use the heterogenous autoregressive (HAR) model to compute out-of-sample forecasts of the monthly realized variance (RV) of movements of the spot and futures price of heating oil. We extend the HAR–RV model to include the role of El Niño and La Niña episodes, as captured by the Equatorial Southern Oscillation Index (EQSOI). Using data from June 1986 to April 2021, we show evidence for several model configurations that both El Niño and La Niña phases contain information useful for forecasting subsequent to the realized variance of price movements beyond the predictive value already captured by the HAR–RV model. The predictive value of La Niña phases, however, seems to be somewhat stronger than the predictive value of El Niño phases. Our results have important implications for investors, as well as from the perspective of sustainable decisions involving the environment.

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The Connections between COVID-19 and the Energy Commodities Prices: Evidence through the Dynamic Time Warping Method

Energies ◽

10.3390/en14134024 ◽

2021 ◽

Vol 14 (13) ◽

pp. 4024

Author(s):

Krzysztof Dmytrów ◽

Joanna Landmesser ◽

Beata Bieszk-Stolorz

Keyword(s):

Time Series ◽

Palm Oil ◽

Dynamic Time Warping ◽

Global Economy ◽

Price Change ◽

Commodity Prices ◽

The Other ◽

Time Warping ◽

Heating Oil ◽

Dynamic Time

The main objective of the study is to assess the similarity between the time series of energy commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic affects all aspects of the global economy. Although this impact is multifaceted, we assess the connections between the number of COVID-19 cases and the energy commodities sector. We analyse these connections by using the Dynamic Time Warping (DTW) method. On this basis, we calculate the similarity measure—the DTW distance between the time series—and use it to group the energy commodities according to their price change. Our analysis also includes finding the time shifts between daily COVID-19 cases and commodity prices in subperiods according to the chronology of the COVID-19 pandemic. Our findings are that commodities such as ULSD, heating oil, crude oil, and gasoline are weakly associated with COVID-19. On the other hand, natural gas, palm oil, CO2 allowances, and ethanol are strongly associated with the development of the pandemic.

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A Water Extraction, Static Headspace Sampling, Gas Chromatographic Method to Determine MTBE in Heating Oil and Diesel Fuel

Environmental Science & Technology ◽

10.1021/es001355l ◽

2001 ◽

Vol 35 (6) ◽

pp. 1202-1208 ◽

Cited By ~ 15

Author(s):

Timothy M. Cummins ◽

Gary A. Robbins ◽

Brent J. Henebry ◽

C. Ryan Goad ◽

Edward J. Gilbert ◽

...

Keyword(s):

Diesel Fuel ◽

Chromatographic Method ◽

Water Extraction ◽

Headspace Sampling ◽

Static Headspace ◽

Static Headspace Sampling ◽

Heating Oil ◽

Gas Chromatographic Method

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Can energy commodities affect energy blockchain-based cryptos?

Studies in Economics and Finance ◽

10.1108/sef-10-2018-0313 ◽

2019 ◽

Vol 36 (4) ◽

pp. 682-699 ◽

Cited By ~ 1

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.

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