International Journal of Forecasting最新文献_第10页

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

Robust returns ranking prediction and portfolio optimization for M6 M6 的稳健收益排名预测和投资组合优化

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2024-04-25 DOI: 10.1016/j.ijforecast.2024.04.004

Hongfeng Ai , Chenning Liu , Peng Lin

{"title":"Robust returns ranking prediction and portfolio optimization for M6","authors":"Hongfeng Ai , Chenning Liu , Peng Lin","doi":"10.1016/j.ijforecast.2024.04.004","DOIUrl":"10.1016/j.ijforecast.2024.04.004","url":null,"abstract":"<div><div><span>The M6 competition aims to address challenging problems in stock returns ranking prediction and portfolio optimization. To tackle the volatility and low signal-to-noise ratio in the stock market, our team designs the overall solution from the robustness perspective. Regarding returns ranking prediction, we present the MultiTask </span>Deep Neural Network<span><span> with Denoising </span>Autoencoder<span><span> Enhancement (MT-DNN-DAE), which incorporates the self-supervised learning of DAE and jointly optimizes the multi-task loss. We propose Robust Feature Selection (RFS) to identify features with a high signal-to-noise ratio for DAE’s representation learning. We construct a separate branch for important ID features to prevent information loss. Results show our solution can accurately predict returns ranking while maintaining generalization. On the task of portfolio optimization, a </span>Differential Evolution algorithm is presented to optimize asset allocation and maximize returns under risk constraints, demonstrating improved performance over traditional techniques. These methods led to a 4th place global ranking in the M6 competition.</span></span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1494-1504"},"PeriodicalIF":7.1,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140794038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","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

{"title":"Local and global trend Bayesian exponential smoothing models","authors":"Slawek Smyl , Christoph Bergmeir , Alexander Dokumentov , Xueying Long , Erwin Wibowo , Daniel Schmidt","doi":"10.1016/j.ijforecast.2024.03.006","DOIUrl":"10.1016/j.ijforecast.2024.03.006","url":null,"abstract":"<div><div>This paper describes a family of seasonal and non-seasonal time series models that can be viewed as generalisations of additive and multiplicative exponential smoothing models to model series that grow faster than linear but slower than exponential. Their development is motivated by fast-growing, volatile time series. In particular, our models have a global trend that can smoothly change from additive to multiplicative and is combined with a linear local trend. Seasonality, when used, is multiplicative in our models, and the error is always additive but heteroscedastic and can grow through a parameter sigma. We leverage state-of-the-art Bayesian fitting techniques to fit these models accurately, which are more complex and flexible than standard exponential smoothing models. When applied to the M3 competition data set, our models outperform the best algorithms in the competition and other benchmarks, thus achieving, to the best of our knowledge, the best results of per-series univariate methods on this dataset in the literature. An open-source software package of our method is available.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 111-127"},"PeriodicalIF":6.9,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704582","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

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

{"title":"Locally tail-scale invariant scoring rules for evaluation of extreme value forecasts","authors":"Helga Kristin Olafsdottir , Holger Rootzén , David Bolin","doi":"10.1016/j.ijforecast.2024.02.007","DOIUrl":"10.1016/j.ijforecast.2024.02.007","url":null,"abstract":"<div><p>Statistical analysis of extremes can be used to predict the probability of future extreme events, such as large rainfalls or devastating windstorms. The quality of these forecasts can be measured through scoring rules. Locally scale invariant scoring rules give equal importance to the forecasts at different locations regardless of differences in the prediction uncertainty. This is a useful feature when computing average scores but can be an unnecessarily strict requirement when one is mostly concerned with extremes. We propose the concept of local weight-scale invariance, describing scoring rules fulfilling local scale invariance in a certain region of interest, and as a special case, local tail-scale invariance for large events. Moreover, a new version of the weighted continuous ranked probability score (wCRPS) called the scaled wCRPS (swCRPS) that possesses this property is developed and studied. The score is a suitable alternative for scoring extreme value models over areas with a varying scale of extreme events, and we derive explicit formulas of the score for the generalised extreme value distribution. The scoring rules are compared through simulations, and their usage is illustrated by modelling extreme water levels and annual maximum rainfall, and in an application to non-extreme forecasts for the prediction of air pollution.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1701-1720"},"PeriodicalIF":6.9,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000128/pdfft?md5=0dc152533b3e57e00ccdf1336680c44d&pid=1-s2.0-S0169207024000128-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098321","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

Forecasting adversarial actions using judgment decomposition-recomposition 利用判断分解-重组预测对抗行动

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-04-04 DOI: 10.1016/j.ijforecast.2024.03.004

Yolanda Gomez , Jesus Rios , David Rios Insua , Jose Vila

引用次数: 0

Conditionally optimal weights and forward-looking approaches to combining forecasts 结合预测的条件最优权重和前瞻性方法

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-04-04 DOI: 10.1016/j.ijforecast.2024.03.002

Christopher G. Gibbs, Andrey L. Vasnev

引用次数: 0