Carbon Price Forecasting with Quantile Regression and Feature Selection

• URL: http://arxiv.org/abs/2305.03224v1
• Date: Fri, 5 May 2023 01:02:08 GMT
• Title: Carbon Price Forecasting with Quantile Regression and Feature Selection
• Authors: Tianqi Pang and Kehui Tan and Chenyou Fan
• Abstract summary: We propose to improve carbon price forecasting with several novel practices. We collect various influencing factors, including commodity prices, export volumes such as oil and natural gas, and prosperity indices. We use the Sparse Quantile Group Lasso and Adaptive Sparse Quantile Group Lasso for robust price predictions.
• Score: 4.973858621819144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Carbon futures has recently emerged as a novel financial asset in the trading markets such as the European Union and China. Monitoring the trend of the carbon price has become critical for both national policy-making as well as industrial manufacturing planning. However, various geopolitical, social, and economic factors can impose substantial influence on the carbon price. Due to its volatility and non-linearity, predicting accurate carbon prices is generally a difficult task. In this study, we propose to improve carbon price forecasting with several novel practices. First, we collect various influencing factors, including commodity prices, export volumes such as oil and natural gas, and prosperity indices. Then we select the most significant factors and disclose their optimal grouping for explainability. Finally, we use the Sparse Quantile Group Lasso and Adaptive Sparse Quantile Group Lasso for robust price predictions. We demonstrate through extensive experimental studies that our proposed methods outperform existing ones. Also, our quantile predictions provide a complete profile of future prices at different levels, which better describes the distributions of the carbon market.

Related papers

• Carbon Market Simulation with Adaptive Mechanism Design [55.25103894620696]
  A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility. We propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL) Numerical results show MARL enables government agents to balance productivity, equality, and carbon emissions.
  arXiv  Detail & Related papers  (2024-06-12T05:08:51Z)
• Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
  We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
  arXiv  Detail & Related papers  (2024-01-08T15:29:23Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Forecasting, capturing and activation of carbon-dioxide (CO$_2$): Integration of Time Series Analysis, Machine Learning, and Material Design [0.0]
  This study provides a comprehensive time series analysis of daily industry-specific, country-wise CO$$ emissions from January 2019 to February 2023. The research focuses on the Power, Industry, Ground Transport, Domestic Aviation, and International Aviation sectors in European countries (EU27 & UK, Italy, Germany, Spain) and India. To identify regular emission patterns, the data from the year 2020 is excluded due to the disruptive effects caused by the COVID-19 pandemic.
  arXiv  Detail & Related papers  (2023-07-25T16:03:44Z)
• Petroleum prices prediction using data mining techniques -- A Review [0.0]
  This study provides a review of the existing data mining techniques for making predictions on petroleum prices. The data mining techniques are classified into regression models, deep neural network models, fuzzy sets and logic, and hybrid models.
  arXiv  Detail & Related papers  (2022-11-20T19:33:02Z)
• Measuring the Carbon Intensity of AI in Cloud Instances [91.28501520271972]
  We provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions. We evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform.
  arXiv  Detail & Related papers  (2022-06-10T17:04:04Z)
• (Private)-Retroactive Carbon Pricing [(P)ReCaP]: A Market-based Approach for Climate Finance and Risk Assessment [64.83786252406105]
  Retrospective Social Cost of Carbon Updating (ReSCCU) is a novel mechanism that corrects for limitations as empirically measured evidence is collected. To implement ReSCCU in the context of carbon taxation, we propose Retroactive Carbon Pricing (ReCaP) To alleviate systematic risks and minimize government involvement, we introduce the Private ReCaP (PReCaP) prediction market.
  arXiv  Detail & Related papers  (2022-05-02T06:02:13Z)
• A hybrid deep learning approach for purchasing strategy of carbon emission rights -- Based on Shanghai pilot market [0.0]
  This paper attempts to design a carbon emission purchasing strategy for enterprises, and establish a carbon emission price prediction model. We built a hybrid deep learning model by embedding Generalized Autoregressive Heteroskedastic (GARCH) into the Gate Recurrent Unit (GRU) model. In the simulation, the purchasing strategy based on the GARCH-GRU model was executed with the least cost as well.
  arXiv  Detail & Related papers  (2022-01-24T03:10:01Z)
• Forecasting Crude Oil Price Using Event Extraction [2.132096006921048]
  A novel crude oil price forecasting framework, AGESL, is proposed to deal with this problem. In our approach, an open domain event extraction algorithm is utilized to extract underlying related events. Then a deep neural network integrating the news event features, sentimental features, and historical price features is built to predict future crude oil prices.
  arXiv  Detail & Related papers  (2021-11-14T08:48:43Z)
• Stock Price Prediction Under Anomalous Circumstances [81.37657557441649]
  This paper aims to capture the movement pattern of stock prices under anomalous circumstances. We train ARIMA and LSTM models at the single-stock level, industry level, and general market level. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98%.
  arXiv  Detail & Related papers  (2021-09-14T18:50:38Z)
• Share Price Prediction of Aerospace Relevant Companies with Recurrent Neural Networks based on PCA [13.033705947070931]
  We provide a hybrid prediction model by the combination of Principal Component Analysis (PCA) and Recurrent Neural Networks. Various factors could influence the performance of prediction models, such as finance data, extracted features, algorithms, and parameters. The developed approach can be used to predict the share price of aerospace industries at post COVID-19 time.
  arXiv  Detail & Related papers  (2020-08-26T20:16:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.