Cover Image

Forecasting realized volatility of crude oil futures prices based on machine learning

Fuente: Wiley Open Access

Saved in:

Bibliographic Details
Main Authors:	Jiawen Luo, Tony Klein, Thomas Walther, Qiang Ji
Format:	Artículo Open Access
Published:	Wiley 2024
Subjects:	Journal of Forecasting
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter?
by: Matteo Bonato, et al.
Published: (2024)

Forecasting interval‐valued returns of crude oil: A novel kernel‐based approach
by: Kun Yang, et al.
Published: (2024)

High frequency volatility of oil futures in China: Components, modeling, and prediction
by: Yi Hong, et al.
Published: (2024)

A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data
by: Huosong Xia, et al.
Published: (2024)

Forecasting the high‐frequency volatility based on the LSTM‐HIT model
by: Guangying Liu, et al.
Published: (2024)

Forecasting healthcare service volumes with machine learning algorithms
by: Dong‐Hui Yang, et al.
Published: (2024)

Forecasting Bitcoin returns: Econometric time series analysis vs. machine learning
by: Theo Berger, et al.
Published: (2024)

Interpretable corn future price forecasting with multivariate time series
by: Binrong Wu, et al.
Published: (2024)

Harnessing volatility cascades with ensemble learning
by: Mingmian Cheng
Published: (2024)

Forecasting food price inflation during global crises
by: Patricia Toledo, et al.
Published: (2024)

Forecasting house price index with social media sentiment: A decomposition–ensemble approach
by: Jin Shao, et al.
Published: (2024)

Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation
by: Jiaming Liu, et al.
Published: (2024)

Functional volatility forecasting
by: Yingwen Tan, et al.
Published: (2024)

Probabilistic electricity price forecasting based on penalized temporal fusion transformer
by: He Jiang, et al.
Published: (2024)

Using deep (machine) learning to forecast US inflation in the COVID‐19 era
by: David Stoneman, et al.
Published: (2024)

An infinite hidden Markov model with stochastic volatility
by: Chenxing Li, et al.
Published: (2024)

Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model‐agnostic explanations for multivariate wind speed forecasting
by: Lu Peng, et al.
Published: (2024)

Forecasting multi‐frequency intraday exchange rates using deep learning models
by: Muhammad Arslan, et al.
Published: (2024)

Structured multifractal scaling of the principal cryptocurrencies: Examination using a self‐explainable machine learning
by: Foued Saâdaoui, et al.
Published: (2024)

Forecasting Gold Volatility in an Uncertain Environment: The Roles of Large and Small Shock Sizes
by: Li Zhang, et al.
Published: (2025)

Design of a precise ensemble expert system for crop yield prediction using machine learning analytics
by: Deeksha Tripathi, et al.
Published: (2024)

Forecasting corporate financial performance with deep learning and interpretable ALE method: Evidence from China
by: Longyue Liang, et al.
Published: (2024)

Fiscal Forecasting Rationality Among Expert Forecasters
by: Belen Chocobar, et al.
Published: (2024)

Can intraday data improve the joint estimation and prediction of risk measures? Evidence from a variety of realized measures
by: Zhimin Wu, et al.
Published: (2024)

A Novel Approach to Forecasting After Large Forecast Errors
by: Jennifer L. Castle, et al.
Published: (2025)

Portfolio management based on a reinforcement learning framework
by: Wu Junfeng, et al.
Published: (2024)

Forecasting in turbulent times
by: Nikolaos Giannellis, et al.
Published: (2024)

Macroeconomic and Financial Forecasting in the Aftermath of the Covid Shock: Inflation Forecasting With Large Language Models
by: Nikolaos Giannellis, et al.
Published: (2026)

European Union Allowance price forecasting with Multidimensional Uncertainties: A TCN‐iTransformer Approach for Interval Estimation
by: Ran Wu, et al.
Published: (2025)

Crossproduct Effect and Volatility Forecasting
by: Jiafu Xu, et al.
Published: (2024)

Coherent Forecasting of Realized Volatility
by: Marius Puke, et al.
Published: (2026)

Nonstationary Functional Time Series Forecasting
by: Han Lin Shang, et al.
Published: (2024)

Forecasting and Pricing in the Carbon Credits Market
by: Yiyang Chen, et al.
Published: (2026)

Mortality Forecasting Using Variational Inference
by: Patrik Andersson, et al.
Published: (2025)

Forecasting and Modeling Macroeconomic Vulnerabilities in CESEE
by: Florian Huber, et al.
Published: (2025)

Forecasting the containerized freight index with AIS data: A novel information combination method based on gray incidence analysis
by: Yanhui Chen, et al.
Published: (2024)

Research on occupant injury severity prediction of autonomous vehicles based on transfer learning
by: Na Yang, et al.
Published: (2024)

Visualizing Uncertainty in Time Series Forecasts: The Impact of Uncertainty Visualization on Users' Confidence, Algorithmic Advice Utilization, and Forecasting Performance
by: Dirk Leffrang, et al.
Published: (2024)

Forecasting carbon emissions using asymmetric grouping
by: Didier Nibbering, et al.
Published: (2024)

Trade and Economic Activity: Nonlinear Modeling and Forecasting
by: Alessandro Borin, et al.
Published: (2024)