International Journal of Forecasting最新文献_第5页

The time-varying Multivariate Autoregressive Index model 时变多元自回归指数模型

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-05-15 DOI: 10.1016/j.ijforecast.2024.04.007

Gianluca Cubadda , Stefano Grassi , Barbara Guardabascio

引用次数: 0

Light-touch forecasting: A novel method to combine human judgment with statistical algorithms 轻触式预测：将人类判断与统计算法相结合的新方法

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-05-13 DOI: 10.1016/j.ijforecast.2024.04.003

B.B.J.P.J. van der Staak, R.J.I. Basten, P.P.F.M. van de Calseyde, E. Demerouti, A.G. de Kok

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引用次数: 0

Judgment in macroeconomic output growth predictions: Efficiency, accuracy and persistence

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-05-13 DOI: 10.1016/j.ijforecast.2024.04.001

Michael Pedersen

引用次数: 0

Forecasting interest rates with shifting endpoints: The role of the functional demographic age distribution 预测端点移动的利率：功能性人口年龄分布的作用

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-05-07 DOI: 10.1016/j.ijforecast.2024.04.006

Jiazi Chen , Zhiwu Hong , Linlin Niu

引用次数: 0

Do oil price forecast disagreement of survey of professional forecasters predict crude oil return volatility? 专业预测师调查中的油价预测分歧能否预测原油收益波动？

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-05-04 DOI: 10.1016/j.ijforecast.2024.04.005

Anton Hasselgren , Ai Jun Hou , Sandy Suardi , Caihong Xu , Xiaoxia Ye

{"title":"Do oil price forecast disagreement of survey of professional forecasters predict crude oil return volatility?","authors":"Anton Hasselgren , Ai Jun Hou , Sandy Suardi , Caihong Xu , Xiaoxia Ye","doi":"10.1016/j.ijforecast.2024.04.005","DOIUrl":"10.1016/j.ijforecast.2024.04.005","url":null,"abstract":"<div><div>This paper explores whether the dispersion in forecasted crude oil prices from the European Central Bank Survey of Professional Forecasters can provide insights for predicting crude oil return volatility. It is well-documented that higher disagreement among forecasters of asset price implies greater uncertainty and higher return volatility. Using several Generalized Autoregressive Conditional Heteroskedasticity with Mixed Data Sampling (GARCH-MIDAS) models, we find, based on the in-sample estimation results, the oil market experiences greater volatility when the forecasters’ disagreements increase. The model that integrates both historical realized variance and forward-looking forecaster disagreement into the conditional variance, along with the model focusing solely on pure forward-looking forecaster disagreement, exhibits a much superior fit to the data compared to the model relying solely on realized variance and the models considering forward-looking forecasted mean return. The out-of-sample forecasting results unequivocally illustrate that incorporating forecaster disagreement offers valuable insights, markedly enhancing the predictive accuracy of crude oil return volatility within the GARCH-MIDAS model. Moreover, we illustrate the economic benefit of considering forecasters’ disagreement when forecasting volatility, demonstrating its significance for VaR risk management.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 141-152"},"PeriodicalIF":6.9,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Return predictability, dividend growth, and the persistence of the price–dividend ratio 回报可预测性、股息增长和市盈率的持续性

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-04-24 DOI: 10.1016/j.ijforecast.2024.03.005

Adam Goliński , João Madeira , Dooruj Rambaccussing

引用次数: 0

Coupling LSTM neural networks and state-space models through analytically tractable inference 通过可分析推理将 LSTM 神经网络和状态空间模型耦合起来

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-04-24 DOI: 10.1016/j.ijforecast.2024.04.002

Van-Dai Vuong, Luong-Ha Nguyen, James-A. Goulet

引用次数: 0

Local and global trend Bayesian exponential smoothing models 局部和全局趋势贝叶斯指数平滑模型

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-04-18 DOI: 10.1016/j.ijforecast.2024.03.006

Slawek Smyl , Christoph Bergmeir , Alexander Dokumentov , Xueying Long , Erwin Wibowo , Daniel Schmidt

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引用次数: 0

Forecasting presidential elections: Accuracy of ANES voter intentions 预测总统选举：ANES 选民意向的准确性

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-04-16 DOI: 10.1016/j.ijforecast.2024.03.003

Hyein Ko , Natalie Jackson , Tracy Osborn , Michael S. Lewis-Beck

引用次数: 0

Locally tail-scale invariant scoring rules for evaluation of extreme value forecasts 评估极值预测的局部尾尺度不变评分规则

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-04-04 DOI: 10.1016/j.ijforecast.2024.02.007

Helga Kristin Olafsdottir , Holger Rootzén , David Bolin

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引用次数: 0