Macroeconomic uncertainty, speculation, and energy futures returns: Evidence from a quantile regression

Xiao, Jihong; Wang, Yudong

doi:10.1016/j.energy.2021.122517

Cited by 21 publications

(8 citation statements)

References 71 publications

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“…If product prices do not correspond to their fundamental value, this ultimately leads to bubbles in markets. For example, during periods of crisis, macroeconomic uncertainty may increase the speculative nature of trading markets (Apergis et al, 2020;Xiao & Wang, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is crucial when analyzing time series that they exhibit stable statistical features and distributions such as autocorrelation, mean, and variance. The unit root Dickey-Fuller (ADF) test, which is common in this sort of research (Wang et al, 2022), will be used to determine whether the time series of futures variables are stationary. We use the Augmented Dickey-Fuller (ADF) test if the data in our study is stationary (h0: unit root is present; h1: alternate hypothesis).…”

Section: H2 Financial Speculation Has An Impact On the Volatility Of ...mentioning

confidence: 99%

“…Futures return time series, similar to other financial assets, typically rely on previous squared observations and variances to model present variation. Therefore, GARCH modeling is widely employed in studies of financial markets (Wang et al, 2022). However, GARCH models can be unstable and inaccurate if the underlying data is excessively volatile or lacks observation.…”

Section: H2 Financial Speculation Has An Impact On the Volatility Of ...mentioning

confidence: 99%

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The impact of financial speculation on futures contracts price movements: A study of the US markets for dairy commodities

Staugaitis,

Christauskas

2023

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Research background: The study analyzes whether financial speculation destabilizes commodity prices in light of recent price volatility and spikes in agricultural commodities. The study delves deeper into the US dairy futures markets, which are less studied by other authors in their research and relatively new in comparison to other agricultural commodity markets. These dairy commodity futures contracts provide dairy businesses and farmers the chance to hedge against price risks, which are particularly crucial in uncertain economic times such as the post-2020 COVID-19 pandemic timeframe. The analysis makes use of the weekly returns on futures contracts for nonfat milk powder, butter, milk class III, and cheese that are obtained from the Chicago Mercantile Exchange (CME). Purpose of the article: Conduct an empirical study to evaluate the effect of financial speculation on dairy product prices on US commodity markets, including the post-2020 timeframe. Methods: Time series analysis is used in the investigation: the generalized auto-regressive conditional heteroskedasticity (GARCH) method, the Granger causality test, and the Augmented Dickey-Fuller (ADF) test. Findings & value added: Our analysis's findings show that, even though most commodities experienced an increase in return volatility during the post-2020 period, there is no evidence for financial speculation being the cause of increased returns from dairy futures contracts. The research also suggests that financial speculation, in some cases, even lowers the volatility of dairy futures prices. Therefore, non-commercial market participants may help to distribute price risks, making these markets more liquid.

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Section: Literature Reviewmentioning

confidence: 99%

Section: H2 Financial Speculation Has An Impact On the Volatility Of ...mentioning

confidence: 99%

Section: H2 Financial Speculation Has An Impact On the Volatility Of ...mentioning

confidence: 99%

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The impact of financial speculation on futures contracts price movements: A study of the US markets for dairy commodities

Staugaitis,

Christauskas

2023

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“…Therefore, the natural gas price reform needs to straighten out the original price, formulate reasonable prices for transportation, storage and gas transmission and distribution and accelerate the construction of a market trading center to form an influential market benchmark price [45]. Other scholars have discussed the role of speculation in natural gas pricing [46,47]. Manera et al used the DCC multivariate GARCH model to analyze the future prices of energy commodities (including natural gas) and agricultural commodities; the results showed a spillover effect between commodities and a surge in the correlation between energy and agricultural commodities.…”

Section: Research On the Pricing Mechanism Of Stakeholders In The Nat...mentioning

confidence: 99%

The Pricing Mechanism Analysis of China’s Natural Gas Supply Chain under the “Dual Carbon” Target Based on the Perspective of Game Theory

et al. 2022

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China is currently the world’s largest energy consumer and carbon emitter. In order to reduce the harm of carbon dioxide to the global ecological environment, the use of natural gas instead of coal is a realistic choice for China to achieve the “dual carbon” goal. Opportunities also bring new challenges, and the price of natural gas is an important method of promoting the upstream and downstream industrial chains of natural gas, so it is of great practical significance to study the price of natural gas. This paper builds a three-level supply chain model consisting of suppliers in the natural gas market, city gas companies and consumers in the market and uses the Stackelberg game to study the decision-making models of different subjects under their own dominance and centralized decision-making; it also considers the pricing mechanism and profit situation of stakeholders in the natural gas market under the low-carbon preference of consumers and the level of corporate carbon emission reduction. The research results show that when considering consumers’ low-carbon preferences, the sales prices of various stakeholders in the market have increased, which is beneficial for all entities in the natural gas industry chain. At the same time, with the low-carbon transformation of energy companies, the production method drives the price of raw materials to rise in the process of low-carbon innovation, which, in turn, makes the price of various stakeholders in the natural gas market and the level of carbon emission reduction per unit show a positive relationship; in order to maximize the overall profit of the supply chain, the natural gas market should adopt a centralized decision-making method to further promote the reform of China’s natural gas marketization.

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“…Speculation within the oil market tends to play a role in influencing oil price movements as participants in the oil market are divided into hedgers who seek to minimize their exposure to risk and speculators who seek to make a profit as a result of the movement of oil prices (Huntington et al, 2012). Xiao and Wang (2022) implemented a study in which they aimed to determine the relationship between uncertainty and oil price fluctuations by utilizing the macroeconomic uncertainty (MU) index as an indicator. They employed a quantile regression model where their results showed that the MU index negatively the future returns of crude oil.…”

Section: Speculationmentioning

confidence: 99%

Forecasting Oil Prices: A Comparative Analysis between Neural Network and Regression Models

Hokayem¹,

Gemayel²,

Mezher³

et al. 2022

R. Econ. and Finan.

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After the war between Russia and Ukraine and its implications on various economies, energy security became a trending subject at the international level in 2022. Crude oil is an essential resource that plays a strategic role and its fluctuation has a major impact not only on the firms' profitability but also the stability of several countries. This research examines the possibility of forecasting oil prices where artificial neural network methods in addition to the multiple linear regressions were used in order to attempt to come out with a decent model that can help in forecasting oil prices. A comparison between these two models took place in order to choose the best one. The uniqueness of this research relied on the fact that 26 different variables were used all together, some of them were used for the first time in order to build the forecasting model. The period for constructing and testing both models extended from August 2006 to the beginning of 2019. The neural network model that was built showed to be more promising than the model that used multiple linear regression.

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Macroeconomic uncertainty, speculation, and energy futures returns: Evidence from a quantile regression

Cited by 21 publications

References 71 publications

The impact of financial speculation on futures contracts price movements: A study of the US markets for dairy commodities

The impact of financial speculation on futures contracts price movements: A study of the US markets for dairy commodities

The Pricing Mechanism Analysis of China’s Natural Gas Supply Chain under the “Dual Carbon” Target Based on the Perspective of Game Theory

Forecasting Oil Prices: A Comparative Analysis between Neural Network and Regression Models

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