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Nowcasting World Trade With Machine Learning: A Three-Step Approach 用机器学习预测世界贸易：三步法
IF 2.6 3区 经济学
Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-05-05 DOI: 10.1002/for.70165
Menzie Chinn, Baptiste Meunier, Sebastian Stumpner
{"title":"Nowcasting World Trade With Machine Learning: A Three-Step Approach","authors":"Menzie Chinn,&nbsp;Baptiste Meunier,&nbsp;Sebastian Stumpner","doi":"10.1002/for.70165","DOIUrl":"https://doi.org/10.1002/for.70165","url":null,"abstract":"<p>We nowcast world trade using machine learning, distinguishing between <i>tree</i>-based methods (random forest and gradient boosting) and their <i>linear</i>-<i>regression</i>-based counterparts (macroeconomic random forest and gradient boosting—linear). While much less used in the literature, the latter are found to outperform not only the <i>tree</i>-based techniques but also more “traditional” linear and nonlinear techniques (OLS, Markov-switching, and quantile regression). They do so significantly and consistently across different horizons and real-time datasets. When doing so, we find that using preselection and factor extraction significantly improves the accuracy of machine learning predictions. This approach also outperforms two workhorse nowcasting methods: PCA-OLS and dynamic factor models. Replication code can be found at https://github.com/baptiste-meunier/NowcastingML_3step.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"2954-2968"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70165","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Carbon Price Trends With Image-Based Convolutional Neural Networks 基于图像的卷积神经网络预测碳价格趋势
IF 2.6 3区 经济学
Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-05-19 DOI: 10.1002/for.70175
Xiaohang Ren, Zihui Sima, Farhad Taghizadeh-Hesary, Kun Duan
{"title":"Forecasting Carbon Price Trends With Image-Based Convolutional Neural Networks","authors":"Xiaohang Ren,&nbsp;Zihui Sima,&nbsp;Farhad Taghizadeh-Hesary,&nbsp;Kun Duan","doi":"10.1002/for.70175","DOIUrl":"https://doi.org/10.1002/for.70175","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a convolutional neural network (CNN) model that utilizes EUA chart images to predict carbon price trends. An imaging approach is adopted to convert EUA price and trading volume data into pixel images across four different time horizons as model inputs, enabling predictions for both the next-day price direction and the <i>n</i>-day cumulative trend. Results demonstrate that the image-based CNN model achieves superior performance across various prediction metrics and time horizons, outperforming all traditional machine learning models reliant on time-series data. Furthermore, our forecasting approach exhibits robustness within China's carbon market. This methodology provides carbon market participants with an effective predictive tool, contributing to the market's healthy operation.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"3058-3076"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Volatility of Commodity, Currency, and Stock Markets: Evidence From Markov-Switching Multifractal Models 预测商品、货币和股票市场的波动性：来自马尔可夫转换多重分形模型的证据
IF 2.6 3区 经济学
Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-04-28 DOI: 10.1002/for.70145
Ruipeng Liu, Mawuli Segnon, Oguzhan Cepni, Rangan Gupta
{"title":"Forecasting Volatility of Commodity, Currency, and Stock Markets: Evidence From Markov-Switching Multifractal Models","authors":"Ruipeng Liu,&nbsp;Mawuli Segnon,&nbsp;Oguzhan Cepni,&nbsp;Rangan Gupta","doi":"10.1002/for.70145","DOIUrl":"https://doi.org/10.1002/for.70145","url":null,"abstract":"<p>This paper adopts a bivariate Markov-switching multifractal (BMSM) model to reexamine comovement in SV between commodity, foreign exchange (FX), and stock markets. After the 2007–2008 global financial crisis understanding volatility linkages and the correlation structure between these markets becomes very important for risk analysts, portfolio managers, traders, and governments. Using daily data on stock indices and FX rates from developed and emerging countries and a range of commodities such crude oil, natural gas, aluminum, copper, gold, silver, platinum, wheat, corn, soybean, and soybean oil, we find evidence of (re)correlation between commodity, FX, and stock markets. The BMSM model is very competitive to the DCC-GARCH and the MSM models at short forecasting horizons (1 up to 10 days ahead) but outperforms them at long forecasting horizons (20 days ahead and beyond). Furthermore, we show that an investor with mean-variance preferences gains in most cases the highest utility benefits based on the BMSM model.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"2905-2941"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
A New Multivariate Decomposition–Ensemble Approach With Multisource Heterogeneous Data for Crude Oil Price Forecasting 原油价格预测中一种新的多源非均质数据多元分解集成方法
IF 2.6 3区 经济学
Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-04-20 DOI: 10.1002/for.70160
Zhengling Zhao, Jingyun Sun, Shaolong Sun
{"title":"A New Multivariate Decomposition–Ensemble Approach With Multisource Heterogeneous Data for Crude Oil Price Forecasting","authors":"Zhengling Zhao,&nbsp;Jingyun Sun,&nbsp;Shaolong Sun","doi":"10.1002/for.70160","DOIUrl":"https://doi.org/10.1002/for.70160","url":null,"abstract":"<div>\u0000 \u0000 <p>Forecasting crude oil prices is pivotal for every individual and even the entire country. Previous studies have encountered difficulties in forecasting highly volatile crude oil prices, especially when conflicts, wars, and other irregular events occur. In light of this, this study introduces an innovative hybrid multifactor decomposition–ensemble approach with heterogeneous data from diverse sources. First, the multivariate forecasters including unstructured news text based on keywords and structured financial variables are processed. Second, multivariate empirical mode decomposition (MEMD) is used to decompose the crude oil price and its predictors, and sample entropy (SE) is employed to reconstruct the subcomponents obtained from the decomposition. Thereafter, some effective forecasters are screened from the reconstructed subcomponents of forecasters through statistically testing approaches. Finally, the crude oil prices are forecasted using a hybrid forecasting technique, and the performance of the proposed model is assessed from various viewpoints. The empirical conclusions indicate that the proposed model performs well in forecasting the weekly spot price of West Texas Intermediate crude oil.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"2807-2823"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Media and Business Cycle Predictability 媒体和商业周期的可预测性
IF 2.6 3区 经济学
Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-05-07 DOI: 10.1002/for.70167
Salim Baz, Lara Cathcart, Alexander Michaelides
{"title":"Media and Business Cycle Predictability","authors":"Salim Baz,&nbsp;Lara Cathcart,&nbsp;Alexander Michaelides","doi":"10.1002/for.70167","DOIUrl":"https://doi.org/10.1002/for.70167","url":null,"abstract":"<div>\u0000 \u0000 <p>We construct empirical measures of US business-cycle activity based on media mentions of the word “recession” in financial newspapers. The MRIs (media recession indicators) are useful predictors of US economic activity, both in-sample and out-of-sample. Moreover, they compare favorably with existing business-cycle predictors (term premium, purchasing managers' index, consumer sentiment index and real stock market returns). Furthermore, we show that the MRIs are useful predictors of the probability of a US recession 6 months in advance. Our findings also suggest that constructing and using a more sophisticated sentiment-weighted index does not lead to economically significant improvements in business cycle predictability.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"2969-2990"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Leveraging Structural Insights: Enhancing Inflation Forecasting With Dynamic Stochastic General Equilibrium-Informed Long Short-Term Memory Models 利用结构洞察力：用动态随机一般均衡信息长短期记忆模型增强通货膨胀预测
IF 2.6 3区 经济学
Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-05-11 DOI: 10.1002/for.70174
Langfeng Zhou, Huaguan Li
{"title":"Leveraging Structural Insights: Enhancing Inflation Forecasting With Dynamic Stochastic General Equilibrium-Informed Long Short-Term Memory Models","authors":"Langfeng Zhou,&nbsp;Huaguan Li","doi":"10.1002/for.70174","DOIUrl":"https://doi.org/10.1002/for.70174","url":null,"abstract":"<div>\u0000 \u0000 <p>This study investigates the use of long short-term memory (LSTM) models for forecasting US inflation, integrating structural insights from the Federal Reserve Bank of New York dynamic stochastic general equilibrium (FRBNY DSGE) model to enhance both accuracy and theoretical grounding. Traditional models often struggle with the complexities of macroeconomic dynamics, while machine learning models like LSTM excel at capturing nonlinear relationships but lack interpretability. By using the DSGE model to guide variable selection, this research incorporates key economic sectors into the LSTM framework. An incremental analysis reveals that financial market variables, particularly credit spreads, significantly improve forecasting performance, while government-related variables contribute minimally and may reduce model reliability. The out-of-sample forecasting exercise demonstrates that the LSTM model produces projections more aligned with historical inflation patterns compared with the DSGE model, which often oversimplifies macroeconomic fluctuations. These findings highlight the potential of combining machine learning techniques with economic theory to create robust and reliable forecasting tools. The proposed approach not only enhances the accuracy of inflation forecasts but also provides a practical framework for central banks and financial institutions aiming to refine their models by leveraging the strengths of machine learning within a theoretically consistent structure. This integration represents a valuable advancement in macroeconomic forecasting methodologies.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"3011-3029"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Realized Volatility With Tree-Based HAR-Type Models Incorporating Macroeconomic Uncertainty 结合宏观经济不确定性的树型har模型预测已实现波动率
IF 2.6 3区 经济学
Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-04-22 DOI: 10.1002/for.70164
Wei Liu, Wen Xu, Ian Garrett
{"title":"Forecasting Realized Volatility With Tree-Based HAR-Type Models Incorporating Macroeconomic Uncertainty","authors":"Wei Liu,&nbsp;Wen Xu,&nbsp;Ian Garrett","doi":"10.1002/for.70164","DOIUrl":"https://doi.org/10.1002/for.70164","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper extends heterogenous autoregressive (HAR) models of realized volatility to allow for regime dependence for the purpose of volatility forecasting. We construct two series representing “good” and “bad” macroeconomic uncertainties and allow them to shape the regime structure. The coefficients are estimated with the CART method and can be updated during the forecast horizon. An empirical application to 20 liquid US securities shows that our approach, which imposes economically motivated structure on realized volatility models, can deliver a significant improvement in forecasting realized volatility, especially at long forecast horizons. This improvement is quite robust to alternative realized volatility measures and some variations in the tuning parameters. The documented improvement in volatility forecasting translates into economic gains when we forecast Value-at-Risk.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"2824-2860"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Role of Coincident Information in Real-Time Business Cycle Forecasting 同步信息在经济周期实时预测中的作用
IF 2.6 3区 经济学
Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-04-28 DOI: 10.1002/for.70166
Visa Kuntze
{"title":"The Role of Coincident Information in Real-Time Business Cycle Forecasting","authors":"Visa Kuntze","doi":"10.1002/for.70166","DOIUrl":"https://doi.org/10.1002/for.70166","url":null,"abstract":"<p>Official NBER recession dates are announced with substantial delay. Therefore, real-time forecasters cannot condition on the most recent business cycle states even though recessions and expansions are highly persistent. I study whether real-time coincident releases can substitute for this missing information. At each monthly forecast origin, I construct a recession nowcast, using four coincident indicators and several supervised classifiers, and add this nowcast probability to standard probit forecasting models. In an out-of-sample evaluation for US monthly data from 1986 to 2021, nowcast augmentation improves forecast accuracy at short horizons and at the 1-year horizon relative to a term spread benchmark, while including the raw coincident indicators directly is less effective. The gains are incremental once strong leading indicators are included, and model rankings are sensitive to resampling variation.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"2890-2904"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Regime-Dependent Tail Risk in Digital Asset Portfolios With a Mixture of Hidden Markov Factor Analyzers 混合隐马尔可夫因子分析预测数字资产组合的制度相关尾部风险
IF 2.6 3区 经济学
Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-04-16 DOI: 10.1002/for.70159
Mohamed Saidane, Ezzeddine Ben Mohamed
{"title":"Forecasting Regime-Dependent Tail Risk in Digital Asset Portfolios With a Mixture of Hidden Markov Factor Analyzers","authors":"Mohamed Saidane,&nbsp;Ezzeddine Ben Mohamed","doi":"10.1002/for.70159","DOIUrl":"https://doi.org/10.1002/for.70159","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes the mixture of hidden Markov factor analyzers (MHMFA), a unified framework for jointly forecasting value-at-risk (VaR) and expected shortfall (ES) in digital asset portfolios. The model integrates regime-switching dynamics via a hidden Markov chain, a latent factor structure capturing systematic co-movements, and regime-specific Gaussian mixtures to flexibly accommodate non-Gaussian features in both common and idiosyncratic components. Parameters are estimated using an expectation–maximization algorithm, and joint VaR–ES forecasts are generated through Monte Carlo simulation under a probabilistic soft-assignment scheme. In a large-scale out-of-sample evaluation on a six-cryptocurrency portfolio, the MHMFA consistently outperforms all competing models across multiple portfolio strategies and confidence levels according to the Patton–Ziegel–Chen joint loss criterion. The model achieves strong Basel III compliance at \u0000<span></span><math>\u0000 <mi>α</mi>\u0000 <mo>=</mo>\u0000 <mn>2</mn>\u0000 <mo>.</mo>\u0000 <mn>5</mn>\u0000 <mo>%</mo></math> and generates a time-varying ES/VaR ratio that adapts to market conditions, reflecting increased tail risk during turbulent periods. From an economic perspective, the risk-averse strategy based on MHMFA delivers robust performance after transaction costs, highlighting the practical relevance of the proposed approach for risk management and portfolio allocation.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"2735-2759"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Impact of Uncertainty on Forecasting the US Economy 不确定性对美国经济预测的影响
IF 2.6 3区 经济学
Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-04-16 DOI: 10.1002/for.70143
Angelica Ghiselli
{"title":"The Impact of Uncertainty on Forecasting the US Economy","authors":"Angelica Ghiselli","doi":"10.1002/for.70143","DOIUrl":"https://doi.org/10.1002/for.70143","url":null,"abstract":"<p>This paper examines the predictive value of uncertainty measures for key macroeconomic indicators across multiple forecast horizons. We evaluate how different uncertainty proxies—economic policy uncertainty (EPU), VIX, geopolitical risk, and measures of macroeconomic and financial uncertainty—enhance forecast accuracy for industrial production, consumer price index, and the federal funds rate using both traditional vector autoregressive models and machine learning approaches. Our results reveal that the marginal predictive power of uncertainty is heterogeneous, varying substantially across variables, forecast horizons, and model specifications. The incremental forecasting value of uncertainty measures that is visible only in a couple of instances becomes statistically insignificant when forecasts are combined.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"2703-2734"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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