发布求助
文献互助 智能选刊 最新文献

International Journal of Forecasting最新文献

筛选
英文 中文
Real-time monitoring procedures for early detection of bubbles 实时监测程序，尽早发现气泡
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-01-27 DOI: 10.1016/j.ijforecast.2024.12.005
E.J. Whitehouse , D.I. Harvey , S.J. Leybourne
{"title":"Real-time monitoring procedures for early detection of bubbles","authors":"E.J. Whitehouse ,&nbsp;D.I. Harvey ,&nbsp;S.J. Leybourne","doi":"10.1016/j.ijforecast.2024.12.005","DOIUrl":"10.1016/j.ijforecast.2024.12.005","url":null,"abstract":"<div><div>Asset price bubbles and crashes can have severe consequences for the stability of financial and economic systems. Policymakers require timely identification of such bubbles in order to respond to their emergence. In this paper we propose new econometric procedures that improve the speed of detection for an emerging asset price bubble in real time. Our new monitoring procedures make use of alternative variance standardisations that are better able to capture the behaviour of the underlying process during a bubble phase. We derive asymptotic results to show that using these alternative variance standardisations does not increase the probability of false detection under the no-bubble (unit root) null hypothesis relative to existing procedures. However, Monte Carlo simulations demonstrate that much earlier detection becomes possible with our new procedures under the bubble (explosive autoregressive) alternative. Empirical applications to OECD housing markets and bitcoin prices show the value in terms of earlier detection of bubbles that our new procedures can achieve. In particular, we show that the United States housing bubble that preceded the global financial crisis could have been detected as early as 1999:Q1 by our new procedures.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1260-1277"},"PeriodicalIF":6.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211943","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
Time-varying parameters as ridge regressions 时变参数作为脊回归
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-01-27 DOI: 10.1016/j.ijforecast.2024.08.006
Philippe Goulet Coulombe
{"title":"Time-varying parameters as ridge regressions","authors":"Philippe Goulet Coulombe","doi":"10.1016/j.ijforecast.2024.08.006","DOIUrl":"10.1016/j.ijforecast.2024.08.006","url":null,"abstract":"<div><div>Time-varying parameter (TVP) models are frequently used in economics to capture structural change. I highlight a rather underutilized fact—that these are actually ridge regressions. Instantly, this makes computations, tuning, and implementation much easier than in the state-space paradigm. Among other things, solving the equivalent dual ridge problem is computationally very fast even in high dimensions, and the crucial ‘amount of time variation’ is tuned by cross-validation. Evolving volatility is dealt with using a two-step ridge regression. I consider extensions that incorporate sparsity (the algorithm selects which parameters vary and which do not) and reduced-rank restrictions (variation is tied to a factor model). To demonstrate the usefulness of the approach, I use it to study the evolution of monetary policy in Canada using large time-varying local projections and TVP-VARs with demanding lag lengths. The applications require the estimation of up to 4600 TVPs, a task within the reach of the new method.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 982-1002"},"PeriodicalIF":6.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211933","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
Predicting the relative performance among financial assets: A comparative analysis of different approaches 预测金融资产的相对表现：不同方法的比较分析
IF 7.1 2区 经济学
International Journal of Forecasting Pub Date : 2025-01-24 DOI: 10.1016/j.ijforecast.2024.12.008
Panagiotis Samartzis
{"title":"Predicting the relative performance among financial assets: A comparative analysis of different approaches","authors":"Panagiotis Samartzis","doi":"10.1016/j.ijforecast.2024.12.008","DOIUrl":"10.1016/j.ijforecast.2024.12.008","url":null,"abstract":"<div><div>We perform a comparative analysis of a wide array of approaches for the problem of forecasting the relative performance<span> among different tradable assets in the framework of the M6 competition. To produce the forecasts, we employ various models spanning probabilistic, classification, and time-series methods, each approaching the problem from a different perspective. We demonstrate that in the case of financial forecasting, simple machine learning approaches<span> have better performance compared to more complex deep-learning models. Furthermore, approaching the problem as a classification task appears to be beneficial. We also confirm findings from existing literature that using simple ensemble techniques can improve performance, and that forecasting performance is better for exchange-traded funds and assets that have lower idiosyncratic volatility. Finally, we benchmark our results against the performance of teams that participated in the M6 competition.</span></span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1428-1449"},"PeriodicalIF":7.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020562","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
Predicting value at risk for cryptocurrencies with generalized random forests 用广义随机森林预测加密货币的风险值
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-01-24 DOI: 10.1016/j.ijforecast.2024.12.002
Rebekka Buse , Konstantin Görgen , Melanie Schienle
{"title":"Predicting value at risk for cryptocurrencies with generalized random forests","authors":"Rebekka Buse ,&nbsp;Konstantin Görgen ,&nbsp;Melanie Schienle","doi":"10.1016/j.ijforecast.2024.12.002","DOIUrl":"10.1016/j.ijforecast.2024.12.002","url":null,"abstract":"<div><div>We study the prediction of value at risk (VaR) for cryptocurrencies. In contrast to classic assets, returns of cryptocurrencies are often highly volatile and characterized by large fluctuations around single events. Analyzing a comprehensive set of 105 major cryptocurrencies, we show that generalized random forests (GRF) adapted to quantile prediction have superior performance over other established methods such as quantile regression, GARCH-type models, and CAViaR models. This advantage is especially pronounced in unstable times and for classes of highly volatile cryptocurrencies. Furthermore, we identify important predictors during such times and show their influence on forecasting over time. Moreover, a comprehensive simulation study indicates that the GRF methodology is at least on par with existing methods in VaR predictions for standard types of financial returns, and clearly superior in the cryptocurrency setup.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1199-1222"},"PeriodicalIF":6.9,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211940","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
How does training improve individual forecasts? Modeling differences in compensatory and non-compensatory biases in geopolitical forecasts 培训如何提高个人预测？地缘政治预测中补偿性和非补偿性偏差的建模差异
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-01-22 DOI: 10.1016/j.ijforecast.2024.12.001
Vahid Karimi Motahhar , Thomas S. Gruca
{"title":"How does training improve individual forecasts? Modeling differences in compensatory and non-compensatory biases in geopolitical forecasts","authors":"Vahid Karimi Motahhar ,&nbsp;Thomas S. Gruca","doi":"10.1016/j.ijforecast.2024.12.001","DOIUrl":"10.1016/j.ijforecast.2024.12.001","url":null,"abstract":"<div><div>Biases in human forecasters lead to poor calibration. We assess how formal training affects two types of bias in probabilistic forecasts of binary outcomes. Compensatory bias occurs when underestimation in one range of probabilities (e.g., less than 50%) is accompanied by overestimation in the opposite range. Non-compensatory bias occurs when the direction of misestimation is consistent throughout the entire range of probabilities. We present a new approach to modeling probabilistic forecasts to determine the extent and direction of compensatory and non-compensatory biases. Using data from the Good Judgment Project, we model the effects of training (randomly assigned) on the calibration of 39,481 initial forecasts from 851 forecasters across two years of the contest. The forecasts exhibit significant indications of both compensatory and non-compensatory biases across all forecasters. Training significantly reduces the compensatory bias in both years. It reduces the non-compensatory bias only in the second year of the contest.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 487-498"},"PeriodicalIF":6.9,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579234","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
Quasi-average predictions and regression to the trend: An application to the M6 financial forecasting competition 准平均预测与趋势回归：在M6财务预测竞赛中的应用
IF 7.1 2区 经济学
International Journal of Forecasting Pub Date : 2025-01-22 DOI: 10.1016/j.ijforecast.2024.12.006
Jose M.G. Vilar
{"title":"Quasi-average predictions and regression to the trend: An application to the M6 financial forecasting competition","authors":"Jose M.G. Vilar","doi":"10.1016/j.ijforecast.2024.12.006","DOIUrl":"10.1016/j.ijforecast.2024.12.006","url":null,"abstract":"<div><div>This paper presents the winning method that achieved fifth place overall in the M6 financial forecasting competition. The method is based on the idea that, under the efficient market hypothesis, it is often more effective to predict values close to the expected averages of categories and trends than to try to make precise predictions. By leveraging low-variability prediction methods, we forecast both the relative performance of multiple assets and their optimal investment positions. We demonstrate that combining asset-class and temporal averages yields modest but consistent advantages over reference indices. The results highlight the challenges of achieving above-average returns in efficient markets and the potential benefits of low-variability prediction methods in such contexts.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1505-1513"},"PeriodicalIF":7.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020566","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 stock market return with anomalies: Evidence from China 用异常预测股市回报：来自中国的证据
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-01-21 DOI: 10.1016/j.ijforecast.2024.12.007
Jianqiu Wang , Zhuo Wang , Ke Wu
{"title":"Forecasting stock market return with anomalies: Evidence from China","authors":"Jianqiu Wang ,&nbsp;Zhuo Wang ,&nbsp;Ke Wu","doi":"10.1016/j.ijforecast.2024.12.007","DOIUrl":"10.1016/j.ijforecast.2024.12.007","url":null,"abstract":"<div><div>We empirically investigate the relation between anomaly portfolio returns and market return predictability in the Chinese stock market. Using 132 long-leg, short-leg, and long-short anomaly portfolio returns, we employ various shrinkage-based statistical learning methods to capture predictive signals of the anomalies in a high-dimensional setting. Our analysis reveals statistically and economically significant return predictability using long- and short-leg anomaly portfolio returns. Moreover, high arbitrage risk enhances forecasting performance, supporting that the predictability stems from mispricing correction persistence. Contrary to findings in the US stock market, we find little evidence that the long-short anomaly portfolios contribute to market return predictability in China, due to the low persistence of asymmetric mispricing corrections. We provide simulation evidence to justify the distinct prediction patterns for the US and Chinese stock markets.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1278-1295"},"PeriodicalIF":6.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211944","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
Subjective-probability forecasts of existential risk: Initial results from a hybrid persuasion-forecasting tournament 存在风险的主观概率预测：混合劝说-预测比赛的初步结果
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-01-17 DOI: 10.1016/j.ijforecast.2024.11.008
Ezra Karger , Josh Rosenberg , Zachary Jacobs , Molly Hickman , Phillip E. Tetlock
{"title":"Subjective-probability forecasts of existential risk: Initial results from a hybrid persuasion-forecasting tournament","authors":"Ezra Karger ,&nbsp;Josh Rosenberg ,&nbsp;Zachary Jacobs ,&nbsp;Molly Hickman ,&nbsp;Phillip E. Tetlock","doi":"10.1016/j.ijforecast.2024.11.008","DOIUrl":"10.1016/j.ijforecast.2024.11.008","url":null,"abstract":"<div><div>A multi-stage persuasion-forecasting tournament asked specialists and generalists (“superforecasters”) to explain their probability judgments of short- and long-run existential threats to humanity. Specialists were more pessimistic, especially on long-run threats posed by artificial intelligence (AI). Despite incentives to share their best arguments during four months of discussion, neither side materially moved the other’s views. This would be puzzling if participants were Bayesian agents methodically sifting through elusive clues about distant futures but it is less puzzling if participants were boundedly rational agents searching for confirmatory evidence as the risks of embarrassing accuracy feedback receded. Consistent with the latter mechanism, strong AI-risk proponents made particularly extreme long- but not short-range forecasts and over-estimated the long-range AI-risk forecasts of others. We stress the potential of these methods to inform high-stakes debates, but we acknowledge limits on what even skilled forecasters can achieve in anticipating rare or unprecedented events.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 499-516"},"PeriodicalIF":6.9,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579420","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
Stock return predictability in the frequency domain 股票收益在频域的可预测性
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-01-16 DOI: 10.1016/j.ijforecast.2024.11.007
Zhifeng Dai , Fuwei Jiang , Jie Kang , Bowen Xue
{"title":"Stock return predictability in the frequency domain","authors":"Zhifeng Dai ,&nbsp;Fuwei Jiang ,&nbsp;Jie Kang ,&nbsp;Bowen Xue","doi":"10.1016/j.ijforecast.2024.11.007","DOIUrl":"10.1016/j.ijforecast.2024.11.007","url":null,"abstract":"<div><div>This paper investigates the role of time–frequency information in dimension reduction prediction of stock returns. Using the long-term wavelet component of monthly S&amp;P500 excess returns as supervision, we employ a machine learning method to extract the common predictive factor from prevalent macroeconomic variables and construct a new macroeconomic index aligned with stock return prediction. The macroeconomic index exhibits significant predictive power, both in and out of sample, at the market and portfolio levels. It outperforms all individual macroeconomic predictors and the factors based on higher frequency information of realized returns. Our findings demonstrate substantial economic value of the new index in asset allocation. Moreover, we also observe a complementary relation between macroeconomic index and investor sentiment. The predictive power is most pronounced during high-economic-uncertainty periods when investors are likely to underreact to fundamental signals and stems from cash flow predictability channel.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1126-1147"},"PeriodicalIF":6.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211936","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
Modeling and predicting failure in US credit unions 对美国信用合作社破产进行建模和预测
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-01-16 DOI: 10.1016/j.ijforecast.2024.12.004
Qiao Peng , Donal McKillop , Barry Quinn , Kailong Liu
{"title":"Modeling and predicting failure in US credit unions","authors":"Qiao Peng ,&nbsp;Donal McKillop ,&nbsp;Barry Quinn ,&nbsp;Kailong Liu","doi":"10.1016/j.ijforecast.2024.12.004","DOIUrl":"10.1016/j.ijforecast.2024.12.004","url":null,"abstract":"<div><div>This study presents a random forest (RF)-based machine learning model to predict the liquidation of US credit unions one year in advance. The model demonstrates impressive accuracy on the test set (97.9% accuracy, with 2.0% false negatives and 8.8% false positives) when utilizing all 44 factors. Simplifying the model to only the top five factors based on feature importance analysis results in a slightly lower, but still significant, accuracy on the test set (92.2% accuracy, with 7.8% false negatives and 17.6% false positives). Comparisons with seven other classification methods verify the superiority of the RF model. This study also uses the Cox proportional-hazards model and Shapley value-based approaches to interpret key feature significance and interactions. The model provides regulators and credit unions with a valuable early warning system for potential failures, enabling corrective measures or strategic mergers to ultimately protect the National Credit Union Share Insurance Fund.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1237-1259"},"PeriodicalIF":6.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211942","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信