Copertina

When Are Statistical Forecast Gains Economically Relevant? Evidence From Bitcoin Returns

Fuente: Wiley Open Access

Salvato in:

Dettagli Bibliografici
Autori principali:	Rehan Arain, Stephen Snudden
Natura:	Artículo Open Access
Pubblicazione:	Wiley 2025
Soggetti:	Journal of Forecasting
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Deep Learning and Machine Learning Insights Into the Global Economic Drivers of the Bitcoin Price
di: Nezir Köse, et al.
Pubblicazione: (2025)

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

Forecasting Transition of Personal Travel Behavior in a Sharing Economy: Evidence From Consumer Preferences of Travel Modes
di: Stephen Youngjun Park, et al.
Pubblicazione: (2025)

Stock Return Forecasting: A Supervised PCA With Selecting and Scaling
di: Ting Zhang, et al.
Pubblicazione: (2025)

Inflation Forecasting Post‐COVID‐19: Evidence From Germany
di: Tiphaine Wibault
Pubblicazione: (2026)

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

The Information Content of Overnight Information for Volatility Forecasting: Evidence From China's Stock Market
di: Yi Zhang, et al.
Pubblicazione: (2025)

Lost in Translation? Risk‐Adjusting RMSE for Economic Forecast Performance
di: Lukas Salcher, et al.
Pubblicazione: (2026)

Climate Change Risk and Financial Market Response: An International Evidence From Performance Forecasts by Financial Analysts
di: Cyrine Khiari, et al.
Pubblicazione: (2025)

Economic Conditions and Predictability of US Stock Returns Volatility: Local Factor Versus National Factor in a GARCH‐MIDAS Model
di: Afees A. S alisu, et al.
Pubblicazione: (2025)

A Comparative Forecasting Framework for Turkey–Germany Trade: Evidence From Time Series and Artificial Neural Networks Models
di: Seyma Nur Unal, et al.
Pubblicazione: (2025)

Forecasting in turbulent times
di: Nikolaos Giannellis, et al.
Pubblicazione: (2024)

Forecasting agricultures security indices: Evidence from transformers method
di: Ammouri Bilel
Pubblicazione: (2024)

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

The Impact of News Sentiment on the Bitcoin Price via Machine Learning and Deep Learning‐Based NLP Models
di: Yunus Emre Gür, et al.
Pubblicazione: (2025)

Forecasting stock market returns with a lottery index: Evidence from China
di: Yaojie Zhang, et al.
Pubblicazione: (2024)

Forecasting Inflation in the Presence of Structural Breaks: A Time‐Varying Parameter Approach
di: Stephen G. Hall, et al.
Pubblicazione: (2026)

Correction to “Smart Forecasting of Carbon Prices Using Machine Learning and Neural Networks: When ARIMA Meets XGBoost and LSTM”
Pubblicazione: (2026)

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

Forecasting the Quantile Connectedness: Insight From Global CSR and Sustainability Indices
di: Miklesh Prasad Yadav, et al.
Pubblicazione: (2026)

Forecasting COVID‐19 Transmission: Insights From the Logistic Smooth Transition Model
di: Yeliz Uymaz, et al.
Pubblicazione: (2026)

Stock Return Prediction Based on a Functional Capital Asset Pricing Model
di: Ufuk Beyaztas, et al.
Pubblicazione: (2025)

Extended Multivariate EGARCH Model: A Model for Zero‐Return and Negative Spillovers
di: Yongdeng Xu
Pubblicazione: (2024)

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

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

A Comparison of Realized Measures of Integrated Volatility: Price Duration‐ vs. Return‐Based Approaches
di: Björn Schulte‐Tillmann, et al.
Pubblicazione: (2026)

Whether Uncertainty Theory Can Enhance GDP Forecasting From Energy: A New Uncertain MIDAS Model
di: Yuxin Shi, et al.
Pubblicazione: (2025)

Modeling Volatility Dynamics in Emerging Markets: Novel Evidence From Large Set of Predictors
di: Maria Ghani, et al.
Pubblicazione: (2025)

Sectoral Corporate Profits and Long‐Run Stock Return Volatility in the United States: A GARCH‐MIDAS Approach
di: Afees Salisu, et al.
Pubblicazione: (2024)

Crossproduct Effect and Volatility Forecasting
di: Jiafu Xu, et al.
Pubblicazione: (2024)

Coherent Forecasting of Realized Volatility
di: Marius Puke, et al.
Pubblicazione: (2026)

Mortality Forecasting Using Variational Inference
di: Patrik Andersson, et al.
Pubblicazione: (2025)

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

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

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

Component‐Driven FX Volatility Prediction: Evidence From USDCNH via GARCH‐MIDAS Models Exploiting Leading Indicators
di: Denis Haoheng Wu, et al.
Pubblicazione: (2025)

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

Threshold MIDAS Forecasting of Canadian Inflation Rate
di: Chaoyi Chen, et al.
Pubblicazione: (2025)

Forecasting With Machine Learning Shadow‐Rate VARs
di: Michael Grammatikopoulos
Pubblicazione: (2025)

Optimal Variance Forecasting in a Trading Context
di: Nick Taylor
Pubblicazione: (2025)