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Predicting the equity premium around the globe: Comprehensive evidence from a large sample 预测全球股票溢价：来自大样本的综合证据
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-06-08 DOI: 10.1016/j.ijforecast.2024.05.002
Fabian Hollstein , Marcel Prokopczuk , Björn Tharann , Chardin Wese Simen
{"title":"Predicting the equity premium around the globe: Comprehensive evidence from a large sample","authors":"Fabian Hollstein ,&nbsp;Marcel Prokopczuk ,&nbsp;Björn Tharann ,&nbsp;Chardin Wese Simen","doi":"10.1016/j.ijforecast.2024.05.002","DOIUrl":"10.1016/j.ijforecast.2024.05.002","url":null,"abstract":"<div><div>Examining 81 countries over a period of up to 145 years and using various predictor variables and forecasting specifications, we provide a detailed analysis of equity premium predictability. We find that excess returns are more predictable in emerging and frontier markets than in developed markets. For all groups, forecast combinations perform very well out of sample. Analyzing the cross-section of countries, we find that market inefficiency is an important driver of return predictability. We also document significant cross-market return predictability. Finally, domestic inflation-adjusted returns are significantly more predictable than USD returns.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 208-228"},"PeriodicalIF":6.9,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141401101","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
Multi-view locally weighted regression for loss given default forecasting 用于给定违约损失预测的多视角局部加权回归
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-06-08 DOI: 10.1016/j.ijforecast.2024.05.006
Hui Cheng, Cuiqing Jiang, Zhao Wang, Xiaoya Ni
{"title":"Multi-view locally weighted regression for loss given default forecasting","authors":"Hui Cheng,&nbsp;Cuiqing Jiang,&nbsp;Zhao Wang,&nbsp;Xiaoya Ni","doi":"10.1016/j.ijforecast.2024.05.006","DOIUrl":"10.1016/j.ijforecast.2024.05.006","url":null,"abstract":"<div><div>Accurately forecasting loss given default (LGD) poses challenges, due to its highly skewed distributions and complex nonlinear dependencies with predictors. To this end, we propose a multi-view locally weighted regression (MVLWR) method for LGD forecasting. To address the complexity of LGD distributions, we build a specific ensemble LGD forecasting model tailored for each new sample, providing flexibility and relaxing reliance on distribution assumptions. To address complex relationships, we combine multi-view learning and ensemble learning<span> for LGD modeling. Specifically, we divide original features into multiple complementary groups, build a view-specific locally weighted model for each group, and aggregate the outputs from all view-specific models. An empirical evaluation using a real-world dataset shows that the proposed method outperforms all the benchmarked methods in terms of both out-of-sample and out-of-time performance in LGD forecasting. We also provide valuable insights and practical implications for stakeholders, particularly financial institutions, to enhance their LGD forecasting capabilities.</span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 290-306"},"PeriodicalIF":6.9,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141413761","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
Forecasting macroeconomic tail risk in real time: Do textual data add value? 实时预测宏观经济尾部风险：文本数据会带来价值吗？
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-06-06 DOI: 10.1016/j.ijforecast.2024.05.007
Philipp Adämmer , Jan Prüser , Rainer A. Schüssler
{"title":"Forecasting macroeconomic tail risk in real time: Do textual data add value?","authors":"Philipp Adämmer ,&nbsp;Jan Prüser ,&nbsp;Rainer A. Schüssler","doi":"10.1016/j.ijforecast.2024.05.007","DOIUrl":"10.1016/j.ijforecast.2024.05.007","url":null,"abstract":"<div><div>We examine the incremental value of news-based data relative to the FRED-MD economic indicators for quantile predictions of employment, output, inflation, and consumer sentiment in a high-dimensional setting. Our results suggest that news data contain valuable information not captured by a large set of economic indicators. We provide empirical evidence that this information can be exploited to improve tail risk predictions. The added value is largest when media coverage and sentiment are combined to compute text-based predictors. Methods that capture quantile-specific non-linearities produce superior forecasts to those with linear predictive relationships. The results are robust along different modeling choices.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 307-320"},"PeriodicalIF":6.9,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705210","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
Cross-temporal forecast reconciliation at digital platforms with machine learning 利用机器学习实现数字平台的跨时空预测调节
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-06-02 DOI: 10.1016/j.ijforecast.2024.05.008
Jeroen Rombouts , Marie Ternes , Ines Wilms
{"title":"Cross-temporal forecast reconciliation at digital platforms with machine learning","authors":"Jeroen Rombouts ,&nbsp;Marie Ternes ,&nbsp;Ines Wilms","doi":"10.1016/j.ijforecast.2024.05.008","DOIUrl":"10.1016/j.ijforecast.2024.05.008","url":null,"abstract":"<div><div>Platform businesses operate on a digital core, and their decision-making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to days). It also necessitates coherent forecasts across all hierarchy levels to ensure aligned decision-making across different planning units such as pricing, product, controlling, and strategy. Given that platform data streams feature complex characteristics and interdependencies, we introduce a non-linear hierarchical forecast reconciliation method that produces cross-temporal reconciled forecasts in a direct and automated way through popular machine learning methods. The method is sufficiently fast to allow forecast-based high-frequency decision-making that platforms require. We empirically test our framework on unique, large-scale streaming datasets from a leading on-demand delivery platform in Europe and a bicycle-sharing system in New York City.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 321-344"},"PeriodicalIF":6.9,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705209","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
Asymmetric uncertainty: Nowcasting using skewness in real-time data 非对称不确定性：利用实时数据的偏度进行预测
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-05-29 DOI: 10.1016/j.ijforecast.2024.05.003
Paul Labonne
{"title":"Asymmetric uncertainty: Nowcasting using skewness in real-time data","authors":"Paul Labonne","doi":"10.1016/j.ijforecast.2024.05.003","DOIUrl":"10.1016/j.ijforecast.2024.05.003","url":null,"abstract":"<div><div>This paper presents a new way to account for downside and upside risks when producing density nowcasts of GDP growth. The approach relies on modelling location, scale, and shape common factors in real-time macroeconomic data. While movements in the location generate shifts in the central part of the predictive density, the scale controls its dispersion (akin to general uncertainty) and the shape its asymmetry, or skewness (akin to downside and upside risks). The empirical application is centred on US GDP growth, and the real-time data come from FRED-MD. The results show that there is more to real-time data than their levels or means: their dispersion and asymmetry provide valuable information for nowcasting economic activity. Scale and shape common factors (i) yield more reliable measures of uncertainty and (ii) improve precision when macroeconomic uncertainty is at its peak.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 229-250"},"PeriodicalIF":6.9,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705256","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
Dynamic time series modelling and forecasting of COVID-19 in Norway 挪威 COVID-19 的动态时间序列建模和预测
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-05-25 DOI: 10.1016/j.ijforecast.2024.05.004
Gunnar Bårdsen , Ragnar Nymoen
{"title":"Dynamic time series modelling and forecasting of COVID-19 in Norway","authors":"Gunnar Bårdsen ,&nbsp;Ragnar Nymoen","doi":"10.1016/j.ijforecast.2024.05.004","DOIUrl":"10.1016/j.ijforecast.2024.05.004","url":null,"abstract":"<div><div>A framework for forecasting new COVID-19 cases jointly with hospital admissions and hospital beds with COVID-19 cases is presented. This project, dubbed CovidMod, produced 21 days ahead forecasts each working day from March 2021 to April 2022. Comparison of RMSFEs from that period, with the RMSFEs of the Norwegian Institute of Public Health (NIPH), favours the CovidMod forecasts, both for new cases and for hospital beds. Another comparison, with the short term forecasts produced by the Cardt method, shows little difference. Next, we present a new model where smooth transition regression is used as a feasible method to include forecasted effects of non-linear policy responses to the deviation between hospital beds and hospital bed capacity, on the forecasts of the original three variables. The forecasting performance of the model with endogenous policy effects is demonstrated retrospectively. It is suggested as a complementary approach to follow when the forecasted variables are generated from processes that include policy responses as realistic features.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 251-269"},"PeriodicalIF":6.9,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705212","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
Boosting domain-specific models with shrinkage: An application in mortality forecasting 利用收缩技术提升特定领域模型：死亡率预测中的应用
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-05-20 DOI: 10.1016/j.ijforecast.2024.05.001
Li Li , Han Li , Anastasios Panagiotelis
{"title":"Boosting domain-specific models with shrinkage: An application in mortality forecasting","authors":"Li Li ,&nbsp;Han Li ,&nbsp;Anastasios Panagiotelis","doi":"10.1016/j.ijforecast.2024.05.001","DOIUrl":"10.1016/j.ijforecast.2024.05.001","url":null,"abstract":"<div><div><span>This paper extends the technique of gradient boosting with a focus on using domain-specific models instead of trees. The domain of mortality forecasting is considered as an application. The two novel contributions are to use well-known stochastic mortality models as weak learners in gradient boosting rather than trees, and to include a penalty that shrinks mortality forecasts in adjacent age groups and nearby </span>geographical regions<span> closer together. The proposed method demonstrates superior forecasting performance based on US male mortality data from 1969 to 2019. The proposed approach also enables us to interpret and visualize the results. The boosted model with age-based shrinkage yields the most accurate national-level mortality forecast. For state-level forecasts, spatial shrinkage provides further improvement in accuracy in addition to the benefits of age-based shrinkage. This improvement can be attributed to data sharing across states with large and small populations in adjacent regions and states with common risk factors.</span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 191-207"},"PeriodicalIF":6.9,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141138769","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
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
{"title":"The time-varying Multivariate Autoregressive Index model","authors":"Gianluca Cubadda ,&nbsp;Stefano Grassi ,&nbsp;Barbara Guardabascio","doi":"10.1016/j.ijforecast.2024.04.007","DOIUrl":"10.1016/j.ijforecast.2024.04.007","url":null,"abstract":"<div><div>Many economic variables are characterized by changes in their conditional mean and volatility, and time-varying Vector Autoregressive Models are often used to handle such complexity. Unfortunately, as the number of series grows, they present increasing estimation and interpretation issues. This paper tries to address this problem by proposing a Multivariate Autoregressive Index model that features time-varying mean and volatility. Technically, we develop a new estimation methodology that mixes switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows one to select or weigh the number of common components, and other data features, in real-time without additional computational costs. Using US macroeconomic data, we provide a forecast exercise that shows the feasibility and usefulness of this model.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 175-190"},"PeriodicalIF":6.9,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705041","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 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
{"title":"Forecasting interest rates with shifting endpoints: The role of the functional demographic age distribution","authors":"Jiazi Chen ,&nbsp;Zhiwu Hong ,&nbsp;Linlin Niu","doi":"10.1016/j.ijforecast.2024.04.006","DOIUrl":"10.1016/j.ijforecast.2024.04.006","url":null,"abstract":"<div><div>An extended dynamic Nelson–Siegel (DNS) model is developed with an additional functional demographic (FD) factor that considers the overall demographic age distribution as a persistent end-shifting driving force. The FD factor in the extended DNS model improves the accuracy of the yield curve forecast by reducing both bias and variance compared with the random walk model, the DNS model, the DNS model with a simple demographic factor of a middle-to-young age ratio, and a benchmark end-shifting model. The model with an unspanned FD factor performs substantially better than the alternative models for most maturities at forecast horizons between one and five years.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 153-174"},"PeriodicalIF":6.9,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141031213","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
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 ,&nbsp;Ai Jun Hou ,&nbsp;Sandy Suardi ,&nbsp;Caihong Xu ,&nbsp;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
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