Fundamental News and the Behavior of Commodity Prices: Price Discovery and Jumps in U.S. Natural Gas Futures and Spot Prices

2008 ◽  
Author(s):  
Song Zan Chiou-Wei ◽  
Scott C. Linn ◽  
Zhen Zhu
Keyword(s):  
Natural Gas ◽  
Price Discovery ◽  
Commodity Prices ◽  
Spot Prices

scholarly journals Impact of Weather on Agriculture Commodity Market and the Price Discovery: An Empirical Analysis

2021 ◽  
pp. 60-70
Author(s):  
Suraj E. S ◽  
Ojasvi Gupta
Keyword(s):  
Price Discovery ◽  
Commodity Prices ◽  
Price Volatility ◽  
Future Market ◽  
Contributing Factors ◽  
Agricultural Commodity ◽  
Spot Prices ◽  
The Future ◽  
Demand Fluctuations ◽  
Derivative Market

This paper focused on studying the agricultural commodity prices in India and it's extreme volatility due to many reasons such as government interference, growth, market forces factors, regular floods and droughts, transport and warehousing problems, etc. These are contributing factors to demand fluctuations. In this case, the future market plays an important role in the economy. The demand for commodity futures has three particular economic functions: price discovery, price risk management, and price volatility. The future market plays a key role in the process of price discovery. The main aim of this system is to regulate prices to minimize uncertainty, to provide price signals to market traders for futures spot prices through the price discovery phase. So, this study emphasized the role of the derivative market in reducing the volatility of agricultural commodity prices in the Indian market. Keywords: volatility, future market, derivatives

The Economics of Commodities and Commodity Markets

2018 ◽  
Author(s):  
Florian Ielpo
Keyword(s):  
Raw Materials ◽  
Commodity Prices ◽  
Essential Element ◽  
Commodity Markets ◽  
Production Costs ◽  
Demand And Supply ◽  
Spot Prices ◽  
Supply Shocks ◽  
The Business Cycle

This chapter covers the economic fundamentals of commodity markets (i.e., what shapes the evolution of the price of raw materials) in three steps. First, it covers the theories explaining why the futures curve can be upward or downward sloping, an essential element for commodity producing companies. The evolution of inventories and hedging pressures are the two dominant sources of explanation. Second, the chapter reviews the fundamentals of commodity spot prices: technologies, supply, demand, and speculation. Production costs draw the long-term evolution of prices, but demand and supply shocks can trigger substantial variations in commodity prices. Third, the chapter presents how commodity prices interact with the business cycle. Commodities are influenced by the world activity but can also have a material impact on it.

The Relationship Between Commodity Prices and Selected Macroeconomic Variables in Turkey

2020 ◽  
pp. 510-528
Author(s):  
Mustafa Uysal ◽  
Zafer Adalı
Keyword(s):  
Natural Gas ◽  
Commodity Prices ◽  
Cointegration Test ◽  
Macroeconomic Variables ◽  
Long Run ◽  
Oil And Natural Gas ◽  
The Relationship

This chapter determines whether there is a long-run relationship among oil, copper, natural gas, export figures and import figures, and BIST 100. Within this context, the study employs monthly periods from January 2006 to June 2019. ADF, Fourier ADF, and Banerjee Cointegration Test were applied. Banerjee Cointegration Test revealed that copper, oil, and natural gas and import figures move together in the long run but the existence of the long-run relationship between the selected inputs and export figures and BIST 100 has not been found. This evidence can be interpreted as the change in oil, copper, and natural gas may influence the amount of Turkish import figures.

scholarly journals Gold Exchange Traded Fund - Price Discovery and Performance Analysis

2018 ◽  
Vol 65 (4) ◽  
pp. 477-495
Author(s):  
Mathew Mallika ◽  
M. M. Sulphey
Keyword(s):  
Price Discovery ◽  
Tracking Error ◽  
Spot Price ◽  
Exchange Traded Funds ◽  
Vector Error Correction ◽  
Spot Prices ◽  
Deviation Analysis ◽  
Vector Error ◽  
And Performance ◽  
Exchange Traded Fund

Abstract The paper aims to examine the price discovery process and the performance of Gold Exchange Traded Funds especially with respect to two Gold ETFs, namely, Goldman Sachs Gold Exchange Traded Scheme (GoldBeEs) and SBI Gold Exchange Traded Scheme (SBIGETS), for the period 2009 – 2016. The study has employed Johansen cointegration and Johansen’s Vector Error Correction Model (VECM) for the price discovery analysis. The results of VECM reveal that the spot prices lead the Gold ETFs price during the study period. Tracking Error analysis shows that Gold ETFs have neither outperformed nor underperformed the spot price. Price Deviation analysis indicates that Gold ETFs are trading on an average lower than the spot price of gold. The entire analysis reveals that although the price discovery takes place in the spot market, Gold ETFs have performed as well as physical gold and the slight difference in price with that of Gold is only because of certain fees, which are applicable in the management of Gold ETFs.

scholarly journals Concurrent variation in oil and gas methane emissions and oil price during the COVID-19 pandemic

2020 ◽  
Author(s):  
David R. Lyon ◽  
Benjamin Hmiel ◽  
Ritesh Gautam ◽  
Mark Omara ◽  
Kate Roberts ◽  
...  
Keyword(s):  
Natural Gas ◽  
Commodity Prices ◽  
Oil Prices ◽  
The United States ◽  
Methane Emissions ◽  
Gas Production ◽  
Satellite Observations ◽  
Permian Basin ◽  
Natural Gas Production ◽  
Oil And Natural Gas

Abstract. Methane emissions associated with the production, transport, and use of oil and natural gas increase the climatic impacts of energy use; however, little is known about how emissions vary temporally and with commodity prices. We present airborne and ground-based data, supported by satellite observations, to measure weekly to monthly changes in total methane emissions in the United States’ Permian Basin during a period of volatile oil prices associated with the COVID-19 pandemic. As oil prices declined from ~$ 60 to $ 20 per barrel, emissions changed concurrently from 3.4 % to 1.5 % of gas production; as prices partially recovered, emissions increased back to near initial values. Concurrently, total oil and natural gas production only declined by a maximum of ~10 % from the peak values seen in the months prior to the crash. Activity data indicate that a rapid decline in well development and subsequent effects on associated gas flaring and midstream infrastructure throughput are the likely drivers of temporary emission reductions. Our results, along with past satellite observations, suggest that under more typical price conditions, the Permian Basin is in a state of overcapacity in which rapidly growing natural gas production exceeds midstream capacity and leads to high methane emissions.

scholarly journals Forecasting Natural Gas Spot Prices with Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5782
Author(s):  
Dimitrios Mouchtaris ◽  
Emmanouil Sofianos ◽  
Periklis Gogas ◽  
Theophilos Papadimitriou
Keyword(s):  
Machine Learning ◽  
Natural Gas ◽  
Gaussian Process Regression ◽  
Time Frame ◽  
Spot Price ◽  
Support Vector ◽  
Learning Methods ◽  
Spot Prices ◽  
Explanatory Variables ◽  
Machine Learning Methods

The ability to accurately forecast the spot price of natural gas benefits stakeholders and is a valuable tool for all market participants in the competitive gas market. In this paper, we attempt to forecast the natural gas spot price 1, 3, 5, and 10 days ahead using machine learning methods: support vector machines (SVM), regression trees, linear regression, Gaussian process regression (GPR), and ensemble of trees. These models are trained with a set of 21 explanatory variables in a 5-fold cross-validation scheme with 90% of the dataset used for training and the remaining 10% used for testing the out-of-sample generalization ability. The results show that these machine learning methods all have different forecasting accuracy for every time frame when it comes to forecasting natural gas spot prices. However, the bagged trees (belonging to the ensemble of trees method) and the linear SVM models have superior forecasting performance compared to the rest of the models.

An Empirical Analysis on Change in Price Discovery of KOSPI200 Futures Through Market Maturity Process

2006 ◽  
Vol 14 (2) ◽  
pp. 51-77
Author(s):  
Woo–baik Lee
Keyword(s):  
Error Correction ◽  
Trading Volume ◽  
Price Discovery ◽  
Futures Market ◽  
Spot Price ◽  
Sample Period ◽  
Information Share ◽  
Spot Prices ◽  
Intraday Data ◽  
Arbitrage Opportunity

This paper estimates the contribution of KOSPI200 futures to spot price discovery based on methodology of ‘information share’, which is suggested by Hasbrouck (1995). Using the intraday data covering sample period from year 1997 to 2003, I estimate information share with specification of Vector Error Correction Model. Main empirical findings are summarized as followings; First. estimate of information share is above 60 percent on average through-out the entire sample period. Second. the contribution of KOSPI200 futures to error correction increased during the recent year of sample period. showing that futures price have strong tendency to lead the spot price. Third. price discovery of KOSPI200 futures have significantly positive relationship with program trading volume and seems to increase under contango. These empirical findings explain the ‘market maturity effect’ that role of futures in spot price discovery enhances as cointegration between futures and spot prices strengthens and futures market countervails the arbitrage opportunity. In general. this paper presents that mature futures market Significantly contributes to spot market efficiency and price discovery process.

The Natural Gas Futures Markets - is it Still Inefficient?

1994 ◽  
Vol 12 (5) ◽  
pp. 369-380 ◽  
Author(s):  
John H. Herbert
Keyword(s):  
Natural Gas ◽  
Futures Markets ◽  
Price Discovery ◽  
Futures Market ◽  
Informational Efficiency ◽  
Natural Gas Market ◽  
Price Series ◽  
Gas Market

The natural gas futures market is fundamental to the current natural gas market both as means of price discovery and for price hedging. Thus, the informational efficiency of the futures market is an important issue. In this article we re-examine the informational efficiency of the natural gas futures market. In this re-examination several cash price series are considered. It is found that the natural gas futures market is informationally efficient for only one of the cash markets. The characteristics of the current natural gas market that might explain the estimated results are also discussed.

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