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Global Oil Export Destination Prediction: A Machine Learning Approach

Haiying Jia, Roar Adland, and Yuchen Wang

Year: 2021
Volume: Volume 42
Number: Number 4
DOI: 10.5547/01956574.42.4.hjia
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Abstract:
We use classification methods from machine learning to predict the destination of global crude oil exports by utilising micro-level crude oil shipment data that incorporates attributes related to the contract, cargo specifications, vessel specifications and macroeconomic conditions. The results show that micro-level information about the oil shipment such as quality and cargo size dominates in the destination prediction. We contribute to the academic literature by providing the first machine learning application to oil shipment data, and by providing new knowledge on the determinants of global crude oil flows. The machine-learning models used to predict the importing country can reach an accuracy of above 71% for the major oil exporting countries based on out-of-sample tests and outperform both naïve models and discrete regression models.



Auctions for Renewables: Does the Choice of the Remuneration Scheme Matter?

Ali Darudi

Year: 2023
Volume: Volume 44
Number: Number 6
DOI: 10.5547/01956574.44.6.adar
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Abstract:
Auctions are increasingly used to support renewable energy sources (RES). The choice of the remuneration scheme is one of the major design challenges policymakers face. This paper analyzes the effects of remuneration schemes on RES auctions’ success in markets with imperfect competition. I develop a game-theoretical auction/operation framework to model the feedback effects between the spot market’s strategic behavior and the auction stage’s bidding behavior. The analysis indicates that policymakers concerned about true-cost bidding, allocative efficiency, spot price, total payments to RES, and non-realization risk may prefer feed-in-tariff (FIT) remuneration. However, feed-in-premium (FIP) remunerations may outperform FIT ones from a social welfare perspective, particularly in markets with dirty technologies at the margin. A machine-learning-based simulation strategy is also presented, indicating that, for an auction for 14 GW of onshore wind in France, FIP auction with a winning incumbent leads to 1.40% higher prices than FIT ones.



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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