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Financial Speculation in Energy and Agriculture Futures Markets: A Multivariate GARCH Approach

Matteo Manera, Marcella Nicolini, and Ilaria Vignati

Year: 2013
Volume: Volume 34
Number: Number 3
DOI: 10.5547/01956574.34.3.4
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Abstract:
This paper analyses futures prices of four energy commodities (crude oil, heating oil, gasoline and natural gas) and five agricultural commodities (corn, oats, soybean oil, soybeans and wheat), over the period 1986�2010. Using DCC multivariate GARCH models, it provides new evidence on four research questions: 1) Are macroeconomic factors relevant in explaining returns of energy and nonenergy commodities? 2) Is financial speculation significantly related to returns in futures markets? 3) Are there significant relationships among returns, either in their mean or variance, across different markets? 4) Is speculation in one market affecting returns in other markets? Results suggest that the S&P 500 index and the exchange rate significantly affect returns. Financial speculation, proxied by Working�s T index, is poorly significant in modelling returns of commodities. Moreover, spillovers between commodities are present and the conditional correlations among energy and agricultural commodities display a spike around 2008.



Introduction to Topics on “Uncertainty and Recent Challenges in Oil and Commodity Markets

Fredj Jawadi and Apostolos Serletis

Year: 2019
Volume: Volume 40
Number: Special Issue
DOI: 10.5547/01956574.40.SI2.aser
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Abstract:
This ISCEF special issue of The Energy Journal presents new results in the area of energy economics to provide new insights on commodity markets, which will be helpful for investors, policymakers and analysts. In particular, this issue focuses on studies that use recent modeling techniques and empirical design. It introduces seven studies, presented at the fifth International Symposium in Computational Economics and Finance organized in Paris on April 12�14th, 2018 (www.iscef.com). These studies focus on the investigation of the dynamics of commodity markets, discuss the consequences of uncertainty on energy prices and their effects on the real economy and financial markets, and use high frequency data and recent econometric methods to empirically investigate the interactions between commodity markets and financial markets.



Time-Varying Term Structure of Oil Risk Premia

Gonzalo Cortazar, Philip Liedtke, Hector Ortega, and Eduardo S. Schwartz

Year: 2022
Volume: Volume 43
Number: Number 5
DOI: 10.5547/01956574.43.5.gcor
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Abstract:
We develop a framework to estimate time-varying commodity risk premia from multi-factor models using futures prices and analysts' forecasts of future prices. The model is calibrated for oil using a 3-factor stochastic commodity-pricing model with an affine risk premia specification. The WTI oil futures price data is from the New York Mercantile Exchange (NYMEX) and analysts' forecasts are from Bloomberg and the U.S Energy Information Administration. Weekly estimations for short, medium, and long-term risk premia between 2010 and 2017 are obtained. Results from the model calibration show that the term structure of oil risk premia moves stochastically through time, that short-term risk premia tend to be higher than long-term ones and that risk premia volatility is much higher for short maturities. An empirical analysis is performed to explore the macroeconomic and oil market variables that may explain the stochastic behavior of oil risk premia, showing that inventories, hedging pressure, term premium, default premium and the level of interest rates all play a significant role in explaining the risk premia.



Drilling Deeper: Non-Linear, Non-Parametric Natural Gas Price and Volatility Forecasting

Dusan Bajatovic, Deniz Erdemlioglu, and Nikola Gradojevic

Year: 2024
Volume: Volume 45
Number: Number 4
DOI: 10.5547/01956574.45.4.dbaj
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Abstract:
This paper studies the forecast accuracy and explainability of a battery of day-ahead (Henry Hub and Title Transfer Facility (TTF)) natural gas price and volatility models. The results demonstrate the dominance of non-linear, non-parametric models with deep structure relative to various competing model specifications. By employing the explainable artificial intelligence (XAI) approach, we document that the price of natural gas is formed strategically based on crude oil and electricity prices. While the conditional volatility of natural gas returns is driven by long-memory dynamics and crude oil volatility, the informativeness of the electricity predictor has improved over the most recent volatile time period. Although we reveal that predictive non-linear relationships are inherently complex and time-varying, our findings in general support the notion that natural gas, crude oil and electricity are interconnected. Focusing on the periods when markets experienced sharp structural breaks and extreme volatility (e.g., the COVID-19 pandemic and the Russia-Ukraine conflict), we show that deep learning models provide better adaptability and lead to significantly more accurate forecast performance.





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