Journal of Forecasting最新文献_第10页

Constructing a high-frequency World Economic Gauge using a mixed-frequency dynamic factor model 利用混合频率动态因素模型构建高频世界经济指数

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-04 DOI: 10.1002/for.3130

Chew Lian Chua, Sarantis Tsiaplias, Ruining Zhou

引用次数: 0

Performance and reporting predictability of hedge funds 对冲基金的业绩和报告可预测性

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-04 DOI: 10.1002/for.3122

Elisa Becker-Foss

引用次数: 0

An infinite hidden Markov model with stochastic volatility 具有随机波动性的无限隐马尔可夫模型

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-02 DOI: 10.1002/for.3123

Chenxing Li, John M. Maheu, Qiao Yang

引用次数: 0

Predicting tail risks by a Markov switching MGARCH model with varying copula regimes 用具有不同协整机制的马尔可夫切换 MGARCH 模型预测尾部风险

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-03-19 DOI: 10.1002/for.3117

Markus J. Fülle, Helmut Herwartz

{"title":"Predicting tail risks by a Markov switching MGARCH model with varying copula regimes","authors":"Markus J. Fülle, Helmut Herwartz","doi":"10.1002/for.3117","DOIUrl":"10.1002/for.3117","url":null,"abstract":"To improve the dynamic assessment of risks of speculative assets, we apply a Markov switching MGARCH approach to portfolio risk forecasting. More specifically, we take advantage of the flexible Markov switching copula multivariate GARCH (MS-C-MGARCH) model of Fülle and Herwartz (2022). As an empirical illustration, we take the perspective of a risk-averse agent and employ the suggested model for assessments of future risks of portfolios composed of a high-yield equity index (S&P 500) and two safe-haven investment instruments (i.e., Gold and US Treasury Bond Futures). We follow recent suggestions to employ the expected shortfall as a prime assessment of tail risks. To accurately evaluate the merits of the new model, we back-test the risk forecasting for daily returns over 10 years for heterogeneous market environments including, for example, the COVID-19 pandemic. We find that the MS-C-MGARCH model outperforms benchmark volatility models (MGARCH, C-MGARCH) in predicting both value-at-risk and expected shortfall. The superiority of the MS-C-MGARCH model becomes stronger, when the share of comparably risky assets in the portfolio is relatively large.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 6","pages":"2163-2186"},"PeriodicalIF":3.4,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140167233","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

The effects of governance quality on renewable and nonrenewable energy consumption: An explainable decision frame 治理质量对可再生能源和不可再生能源消费的影响：可解释的决策框架

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-03-15 DOI: 10.1002/for.3110

Futian Weng, Dongsheng Cheng, Muni Zhuang, Xin Lu, Cai Yang

引用次数: 0

How we missed the inflation surge: An anatomy of post-2020 inflation forecast errors 我们是如何错过通胀激增的？2020 年后通胀预测误差剖析

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-03-12 DOI: 10.1002/for.3088

Christoffer Koch, Diaa Noureldin

引用次数: 0

Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model-agnostic explanations for multivariate wind speed forecasting 基于注意门递归单元和局部可解释模型的可解释机器学习技术，用于多变量风速预报

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-03-11 DOI: 10.1002/for.3097

Lu Peng, Sheng-Xiang Lv, Lin Wang

{"title":"Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model-agnostic explanations for multivariate wind speed forecasting","authors":"Lu Peng, Sheng-Xiang Lv, Lin Wang","doi":"10.1002/for.3097","DOIUrl":"10.1002/for.3097","url":null,"abstract":"Wind power has emerged as a successful component within power systems. The ability to reliably and accurately forecast wind speed is of great importance in maintaining the security and stability of the power grid. However, the significance of explaining prediction models has often been overlooked by researchers. To address this gap, this study introduces a novel approach to wind speed forecasting that incorporates a significant decomposition method, attention-based machine learning, and local explanation techniques. The proposed model utilizes grid search variational mode decomposition to decompose the wind speed sequence into different modes while employing gate recurrent unit with an attention mechanism to achieve superior forecasting performance. Experimental evaluations conducted on eight real-world wind speed datasets demonstrate that the proposed approach outperforms other popular models across multiple performance criteria. In two specific experiments, the proposed approach achieved a minimal mean absolute percentage error of 2.74% and 1.70%, respectively. Furthermore, local interpretable model-agnostic explanations (LIME) were employed to assess the influence of factors, highlighting whether they positively or negatively affected the predicted values.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 6","pages":"2064-2087"},"PeriodicalIF":3.4,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140108185","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

Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions? 复杂和小规模与简单和大规模：在贝叶斯向量自回归中引入漂移系数何时会有回报？

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-03-11 DOI: 10.1002/for.3121

Martin Feldkircher, Luis Gruber, Florian Huber, Gregor Kastner

引用次数: 0

Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter? 预测农产品价格的实际波动：情绪重要吗？

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-03-11 DOI: 10.1002/for.3106

Matteo Bonato, Oguzhan Cepni, Rangan Gupta, Christian Pierdzioch

{"title":"Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter?","authors":"Matteo Bonato, Oguzhan Cepni, Rangan Gupta, Christian Pierdzioch","doi":"10.1002/for.3106","DOIUrl":"10.1002/for.3106","url":null,"abstract":"We analyze the out-of-sample predictive power of sentiment for the realized volatility of agricultural commodity price returns. We use high-frequency intra-day data covering the period from 2009 to 2020 to estimate realized volatility. Our baseline forecasting model is a heterogeneous autoregressive (HAR) model, which we extend to include sentiment. We further enhance this model by incorporating various key realized moments such as leverage, realized skewness, realized kurtosis, realized upside (“good”) volatility, realized downside (“bad”) volatility, realized jumps, realized upside tail risk, and realized downside tail risk. In order to setup a forecasting model, we use (i) forward and backward stepwise predictor selection and (ii) a model-based averaging algorithm. The forecasting models constructed through these algorithms outperform both the baseline HAR-RV model and the HAR-RV-sentiment model. We conclude that, for the agricultural commodities studied in our research, realized moments play a more significant role in forecasting realized volatility compared to sentiment.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 6","pages":"2088-2125"},"PeriodicalIF":3.4,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140108166","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

Text-based corn futures price forecasting using improved neural basis expansion network 利用改进的神经基础扩展网络进行基于文本的玉米期货价格预测

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-03-08 DOI: 10.1002/for.3119

Lin Wang, Wuyue An, Feng-Ting Li

{"title":"Text-based corn futures price forecasting using improved neural basis expansion network","authors":"Lin Wang, Wuyue An, Feng-Ting Li","doi":"10.1002/for.3119","DOIUrl":"10.1002/for.3119","url":null,"abstract":"The accurate forecasting of agricultural futures prices is critical for ensuring national food security. Therefore, this study proposes a text-based deep learning forecasting model. This model first uses the ChineseBERT + a text convolution neural network to classify Weibo text and obtain a raw sentiment index. Then, complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, correlation coefficient, and sample entropy are combined to decompose and reconstruct the raw sentiment index and obtain a denoised sentiment index. Subsequently, the neural basis expansion analysis with exogenous variables is improved by designing a weight coefficient and Optuna is used to optimize the designed weight coefficient and the hyperparameters. Finally, the SHapley Additive exPlanations value is used to increase the interpretability of prediction results. Corn futures prices for the Dalian Exchange are used in forecasting to validate the accuracy and stability of the proposed model. Experimental results show that the proposed denoising sentiment index contributes more to the improvement of predictive model performance than the raw sentiment index. The proposed text-based deep predictive model demonstrates strong predictive ability for prediction horizons of 30 and 60 days. SHapley Additive exPlanations value analysis shows that the three features with greater effects on corn futures prices are as follows: “Corn Spot Price of Zhengzhou market,” “CBOT_corn_futures_price,” and “Pork futures price.”","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 6","pages":"2042-2063"},"PeriodicalIF":3.4,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140072081","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