Journal of Forecasting最新文献_第3页

Bank Capital Requirements, Lending Supply, and Economic Activity: A Scenario Analysis Perspective 银行资本需求、贷款供给和经济活动：情景分析视角

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2025-01-06 DOI: 10.1002/for.3239

Antonio M. Conti, Andrea Nobili, Federico M. Signoretti

{"title":"Bank Capital Requirements, Lending Supply, and Economic Activity: A Scenario Analysis Perspective","authors":"Antonio M. Conti, Andrea Nobili, Federico M. Signoretti","doi":"10.1002/for.3239","DOIUrl":"https://doi.org/10.1002/for.3239","url":null,"abstract":"<div>\u0000 \u0000 <p>We evaluate the relation between bank capital, lending supply, and economic activity using Italian data over 1993–2015, a period which covers three key post-crisis regulatory and supervisory measures (the Basel III reform, the 2011 European Banking Authority [EBA] stress test, the European Central Bank's [ECB] Comprehensive Assessment, and launch of the Single Supervisory Mechanism—SSM). We quantify the impact of increased bank capital requirements using a novel procedure that recovers the magnitude of the policy measures, relying on scenario analysis and Bayesian VARs with a rich characterization of the banking sector. We document that the EBA and SSM measures unpredictably raised Tier 1 ratio by about 2.5 percentage points, leading to an average reduction in credit to firms and households by 5% and 4%, respectively, and to a decline in real GDP by over 2% and 4%. The Basel III bank capital increase is instead correctly anticipated in out-of-sample forecasting. These findings are robust to time-varying model parameters and consistent with narrative sign restriction techniques.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"1132-1164"},"PeriodicalIF":3.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564879","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

Analyzing and Forecasting Container Throughput With a Hybrid Decomposition-Reconstruction-Ensemble Method: A Study of Two China Ports 基于混合分解-重构-集成方法的集装箱吞吐量分析与预测——以中国两个港口为例

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2025-01-05 DOI: 10.1002/for.3253

Yi Xiao, Sheng Wu, Chen He, Yi Hu

{"title":"Analyzing and Forecasting Container Throughput With a Hybrid Decomposition-Reconstruction-Ensemble Method: A Study of Two China Ports","authors":"Yi Xiao, Sheng Wu, Chen He, Yi Hu","doi":"10.1002/for.3253","DOIUrl":"https://doi.org/10.1002/for.3253","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate container throughput forecasting is critical for enhancing port efficiency and ensuring global trade stability, particularly in the face of economic uncertainties, geopolitical tensions, and supply chain disruptions. Existing forecasting methods often struggle to model the nonlinear, nonstationary, and noise-laden characteristics of throughput data, creating a clear gap in the ability to provide reliable predictions. To address this, we propose a novel hybrid model, VMD-ISE-TCNT, designed to tackle these challenges. The model employs variational mode decomposition (VMD) to decompose time series into intrinsic modes, with an improved signal energy (ISE) criterion automating the selection of optimal mode numbers. These modes are categorized into low- and high-frequency components and forecasted separately using temporal convolutional networks (TCNs), leveraging their strength in capturing multiscale temporal dependencies. The Theil UII-S loss function is integrated to enhance model robustness by prioritizing proportional accuracy and reducing outlier sensitivity. Empirical evaluations using 24 years of data from China's two largest container ports—Shanghai and Shenzhen—demonstrate the superior performance of the VMD-ISE-TCNT model compared to traditional and hybrid benchmarks. By addressing frequency-specific patterns and automating key processes, this model provides a scalable and interpretable solution for advancing port operations and ensuring resilience in global trade.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1424-1440"},"PeriodicalIF":3.4,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206794","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

Economic Conditions and Predictability of US Stock Returns Volatility: Local Factor Versus National Factor in a GARCH-MIDAS Model 经济条件与美国股票收益波动的可预测性：GARCH-MIDAS模型中的地方因素与国家因素

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2025-01-05 DOI: 10.1002/for.3251

Afees A. S alisu, Wenting Liao, Rangan Gupta, Oguzhan Cepni

{"title":"Economic Conditions and Predictability of US Stock Returns Volatility: Local Factor Versus National Factor in a GARCH-MIDAS Model","authors":"Afees A. S alisu, Wenting Liao, Rangan Gupta, Oguzhan Cepni","doi":"10.1002/for.3251","DOIUrl":"https://doi.org/10.1002/for.3251","url":null,"abstract":"<div>\u0000 \u0000 <p>The aim of this paper is to utilize the generalized autoregressive conditional heteroscedasticity–mixed data sampling (GARCH-MIDAS) framework to predict the daily volatility of state-level stock returns in the United States (US), based on the weekly metrics from the corresponding broad economic conditions indexes (ECIs). In light of the importance of a common factor in explaining a large proportion of the total variability in the state-level economic conditions, we first apply a dynamic factor model with stochastic volatility (DFM-SV) to filter out the national factor from the local components of weekly state-level ECIs. We find that both the local and national factors of the ECI generally tend to affect state-level volatility negatively. Furthermore, the GARCH-MIDAS model, supplemented by these predictors, surpasses the benchmark GARCH-MIDAS model with realized volatility (GARCH-MIDAS-RV) in a majority of states. Interestingly, the local factor often assumes a more influential role overall, compared with the national factor. Moreover, when the stochastic volatilities associated with the local and national factors are integrated into the GARCH-MIDAS model, they outperform the GARCH-MIDAS-RV in over 80% of the states. Our findings have important implications for investors and policymakers.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1441-1466"},"PeriodicalIF":3.4,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206795","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

Estimation of Constrained Factor Models for High-Dimensional Time Series 高维时间序列约束因子模型的估计

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2025-01-05 DOI: 10.1002/for.3249

Yitian Liu, Jiazhu Pan, Qiang Xia

引用次数: 0

Forecasting Natural Gas Futures Prices Using Hybrid Machine Learning Models During Turbulent Market Conditions: The Case of the Russian–Ukraine Crisis 在动荡的市场条件下使用混合机器学习模型预测天然气期货价格：以俄罗斯-乌克兰危机为例

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2025-01-05 DOI: 10.1002/for.3250

Pavan Kumar Nagula, Christos Alexakis

{"title":"Forecasting Natural Gas Futures Prices Using Hybrid Machine Learning Models During Turbulent Market Conditions: The Case of the Russian–Ukraine Crisis","authors":"Pavan Kumar Nagula, Christos Alexakis","doi":"10.1002/for.3250","DOIUrl":"https://doi.org/10.1002/for.3250","url":null,"abstract":"<div>\u0000 \u0000 <p>Recently, many researchers have shown keen interest in natural gas price prediction using machine learning and hybrid architectures. Our research forecasts natural gas future prices with different hybrid machine learning models using over a hundred technical indicators. The hybrid deep cross-network model outperformed the single-stage deep cross-network regression and hybrid support vector machine models with 33% and 46% lower mean absolute error and 22% and 1.2 times better directional hit rate during 11 months of turbulent market circumstances due to the Russia–Ukraine crisis. The hybrid deep cross-network model is 14, 5, and 6 times more profitable than the hybrid support vector machine, the benchmark passive buy-and-hold strategy, and the single-stage deep cross-network regression models. The hybrid deep cross-network model is resilient during low- and high-volatility periods. Deep cross-network algorithm technical indicator interactions are more statistically significant than support vector machine polynomial kernel interactions. Energy traders and policymakers can exploit our findings.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1501-1512"},"PeriodicalIF":3.4,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206685","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 Gold Volatility in an Uncertain Environment: The Roles of Large and Small Shock Sizes 在不确定环境下预测黄金波动：大小冲击的作用

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2025-01-05 DOI: 10.1002/for.3247

Li Zhang, Lu Wang, Yu Ji, Zhigang Pan

{"title":"Forecasting Gold Volatility in an Uncertain Environment: The Roles of Large and Small Shock Sizes","authors":"Li Zhang, Lu Wang, Yu Ji, Zhigang Pan","doi":"10.1002/for.3247","DOIUrl":"https://doi.org/10.1002/for.3247","url":null,"abstract":"<div>\u0000 \u0000 <p>In a complex and volatile macroeconomic environment, precious metals, which have the functions of preservation, appreciation, and hedging, play an important role in investment risk management. Therefore, this study adopts the extended GARCH-MIDAS model to investigate the underlying connection between gold price volatility and different uncertain shocks. In this paper, we consider five uncertainty indicators and then decompose them into different states to capture their shock sizes. Next, we introduce uncertainty shocks into the MIDAS structure to test whether they contain relevant and valid information about gold price volatility forecasts. Specifically, parameter significance suggests a positive association between uncertain indicators and gold price volatility, but variability in the influence of their shock sizes on gold price volatility. Out-of-sample results present that the extended model that includes asymmetric shock sizes outperforms other competitive models. Besides, the model that includes large shock sizes exhibits better predictive performance than the model that includes small shocks. Finally, based on the empirical analyses, this paper provides new insights for the gold industry, futures exchanges, government regulators, and investors engaged in futures hedging to achieve risk control and financial stability in response to uncertain shocks.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1478-1500"},"PeriodicalIF":3.4,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206684","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

A Snapshot of Central Bank (Two-Year) Forecasting: A Mixed Picture 央行（两年）预测简况：喜忧参半

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-30 DOI: 10.1002/for.3244

Charles A. E. Goodhart, Manoj Pradhan

{"title":"A Snapshot of Central Bank (Two-Year) Forecasting: A Mixed Picture","authors":"Charles A. E. Goodhart, Manoj Pradhan","doi":"10.1002/for.3244","DOIUrl":"https://doi.org/10.1002/for.3244","url":null,"abstract":"<div>\u0000 \u0000 <p>Between 2001 and 2023, Central Bank forecasts were patently inaccurate. In this paper, we argue that many of such forecast failings were already present during the earlier years of inflation targetry. Central Banks normally adjust monetary policy so that inflation hits the Inflation Target (IT) within two years. Since a central bank must believe its policy stance is appropriate to achieve this goal, its inflation forecast at the two-year horizon should generally be close to target. We examine whether this has held for three main Central Banks, Bank of England, ECB, and Fed. Although over the IT period prior to 2020, both forecasts and outcomes were commendably close to target, we found that this was due to a sizeable forecast <i>underestimate</i> of the effects of policy and inherent resilience to revive inflation after the GFC crisis hit, largely offset by an <i>overestimate</i> of the effect of monetary policy to restore inflation to target during the more normal times. We attribute such latter overestimation to an unwarranted belief in forward-looking, “well anchored”, expectations amongst households and firms, and to a failure to recognize the underlying disinflationary trends, especially in 2010–2019. We outline a novel means for assessing whether these latter trends were primarily demand driven, e.g. secular stagnation, or supply shocks, a labor supply surge. Finally, we examine how forecasts for the uncertainty of outcomes and relative risk (skew) to the central forecast have developed by examining the Bank of England's fan chart, again at the two-year horizon.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"1097-1131"},"PeriodicalIF":3.4,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565297","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

A Review of Methods for Long-Term Electric Load Forecasting 电力负荷长期预测方法综述

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-26 DOI: 10.1002/for.3248

Thangjam Aditya, Sanjita Jaipuria, Pradeep Kumar Dadabada

{"title":"A Review of Methods for Long-Term Electric Load Forecasting","authors":"Thangjam Aditya, Sanjita Jaipuria, Pradeep Kumar Dadabada","doi":"10.1002/for.3248","DOIUrl":"https://doi.org/10.1002/for.3248","url":null,"abstract":"<div>\u0000 \u0000 <p>Long-term load forecasting (LTLF) has been a fundamental least-cost planning tool for electric utilities. In the past, utilities were monopolies and paid less attention to uncertainty in their LTLF methodologies. Nowadays, such casualness is pricey in competitive markets because utilities need to examine the financial implications of forecast uncertainty for survival. Hence, the aim of this paper is to critique the LTLF research trends with a focus on uncertainty quantification (UQ). For this purpose, we examined 40 LTLF articles published between January 2003 and February 2021. We found that UQ is a nascent area of LTLF research. Our review found two approaches to UQ in LTLF: probabilistic scenario analysis and direct probabilistic methods. The former approach is more helpful to risk analysts but has major caveats in addressing interdependencies of socioeconomic and climate scenarios. We identified very little LTLF research that examines uncertainties associated with climate extremes, distributed generation resources, and demand-side management. Lastly, there is enormous potential for mitigating financial risks by embracing asymmetric cost functions in LTLF research. Future LTLF researchers can work on these identified gaps to help utilities in risk estimation, cost-reliability balancing, and estimation of reserve margin under climate change.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1403-1423"},"PeriodicalIF":3.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206986","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

Money Matters: Broad Divisia Money and the Recovery of the US Nominal GDP From the COVID-19 Recession 货币问题：广义货币与美国名义GDP从COVID-19衰退中复苏

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-24 DOI: 10.1002/for.3242

Michael D. Bordo, John V. Duca

{"title":"Money Matters: Broad Divisia Money and the Recovery of the US Nominal GDP From the COVID-19 Recession","authors":"Michael D. Bordo, John V. Duca","doi":"10.1002/for.3242","DOIUrl":"https://doi.org/10.1002/for.3242","url":null,"abstract":"<div>\u0000 \u0000 <p>The rise of inflation in 2021 and 2022 surprised many macroeconomists who ignored the earlier surge in money growth because of past instability in the demand for simple-sum monetary aggregates. We find that the demand for more theoretically based Divisia aggregates can be modeled and that these aggregates provide useful information about nominal GDP. Unlike M2 and Divisia-M2, whose velocities do not internalize shifts in liabilities across commercial and shadow banks, the velocities of broader Divisia monetary aggregates are stable and can be empirically modeled through the Covid-19 pandemic. In the long run, these velocities depend on regulation and mutual fund costs that affect the substitutability of money for other financial assets. In the short run, we control for swings in mortgage activity and use vaccination rates and the stringency of government pandemic restrictions to control for the unusual pandemic effects. The velocity of broad Divisia money declines during crises like the Great and COVID Recessions but later rebounds. In these recessions, monetary policy lowered short-term interest rates to zero and engaged in quantitative easing of about $4 trillion. Nevertheless, broad money growth was more robust in the COVID Recession, reflecting a less impaired banking system that promoted rather than hindered deposit creation. Our framework implies that nominal GDP growth and inflation rebounded more quickly from the COVID Recession versus the Great Recession. Our different scenarios for future Divisia money growth and the unwinding of the pandemic have different implications for medium-term nominal GDP growth and inflationary pressures.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"1071-1096"},"PeriodicalIF":3.4,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565390","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

Explainable Soybean Futures Price Forecasting Based on Multi-Source Feature Fusion 基于多源特征融合的可解释大豆期货价格预测

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-24 DOI: 10.1002/for.3246

Binrong Wu, Sihao Yu, Sheng-Xiang Lv

{"title":"Explainable Soybean Futures Price Forecasting Based on Multi-Source Feature Fusion","authors":"Binrong Wu, Sihao Yu, Sheng-Xiang Lv","doi":"10.1002/for.3246","DOIUrl":"https://doi.org/10.1002/for.3246","url":null,"abstract":"<div>\u0000 \u0000 <p>The prediction and early warning of soybean futures prices have been even more crucial for the formulation of food-related policies and trade risk management. Amid increasing geopolitical conflicts and uncertainty in trade policies across countries in recent years, there have been significant fluctuations in global soybean futures prices, making it necessary to investigate fluctuations in soybean futures prices, reveal the price determination mechanism, and accurately predict trends in future prices. Therefore, this study proposes a comprehensive and interpretable framework for soybean futures price forecasting. Specifically, this study employs a set of methodologies. Using a snow ablation optimizer (SAO), this study improves the parameters of a time fusion transformer (TFT) model, an advanced interpretable predictive model based on a self-attention mechanism. Besides, it addresses the factors influencing soybean futures prices and constructs effective fusion features through a feature fusion method. To explore volatility trends, the original soybean futures price series are decomposed using variational mode decomposition (VMD). This study also enhances the accuracy of soybean futures price predictions by introducing global geopolitical risk coefficients and trading volumes as predictors. The empirical findings suggest that the VMD-SAO-TFT model enhances prediction accuracy and interpretability, offering implications for decision-makers to achieve accurate predictions and early warning of agricultural futures prices.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1363-1382"},"PeriodicalIF":3.4,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206967","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