Journal of Forecasting最新文献

{"title":"Bayesian Semiparametric Multivariate Realized GARCH Modeling","authors":"Efthimios Nikolakopoulos","doi":"10.1002/for.3285","DOIUrl":"https://doi.org/10.1002/for.3285","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper introduces a novel Bayesian semiparametric multivariate GARCH framework for modeling returns and realized covariance, as well as approximating their joint unknown conditional density. We extend existing parametric multivariate realized GARCH models by incorporating a Dirichlet process mixture of countably infinite normal distributions for returns and (inverse-)Wishart distributions for realized covariance. This approach captures time-varying dynamics in higher order conditional moments of both returns and realized covariance. Our new class of models demonstrates superior out-of-sample forecasting performance, providing significantly improved multiperiod density forecasts for returns and realized covariance, as well as competitive covariance point forecasts.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 7","pages":"2106-2131"},"PeriodicalIF":2.7,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196362","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}

{"title":"Default Prediction Framework With Optimal Feature Set and Matching Ratio","authors":"Guotai Chi,&nbsp;Fengshan Bai,&nbsp;Hongping Tan,&nbsp;Ying Zhou","doi":"10.1002/for.3284","DOIUrl":"https://doi.org/10.1002/for.3284","url":null,"abstract":"<div>\u0000 \u0000 <p>We propose a default prediction framework that incorporates imbalance handling and feature selection. For imbalance handling, we determine the optimal ratio of non-default to default firms by minimizing the Type-II error of the majority voting deep fully connected network (MV-DFCN) model. For feature selection, we design a two-stage process that first eliminates highly correlated and redundant features, and then refines the feature set using backward selection. Experimental results show that the DFCN model within the proposed framework outperforms baseline models in terms of G-Mean and AUC and achieves the lowest Type-II error rate. Furthermore, the framework outperforms eight baseline combinations of imbalance handling and feature selection strategies. Additionally, SHAP values are used to assess feature contributions, and nine features with statistically significant impacts are identified.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 7","pages":"2067-2088"},"PeriodicalIF":2.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197103","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}

{"title":"Real-Time Forecasting Using Mixed-Frequency VARs With Time-Varying Parameters","authors":"Markus Heinrich,&nbsp;Magnus Reif","doi":"10.1002/for.3276","DOIUrl":"https://doi.org/10.1002/for.3276","url":null,"abstract":"<p>This paper provides a detailed assessment of the real-time forecast accuracy of a wide range of vector autoregressive models that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed-frequency time-varying parameter vector autoregressive model with stochastic volatility. Monte Carlo simulation shows that the novel model is well-suited to estimate missing monthly observations in an environment that is subject to parameter instability. In a real-time forecast exercise, the model delivers accurate now- and forecasts and, on average, outperforms its competitors. Particularly, inflation and unemployment rate forecasts are more precise.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 7","pages":"2055-2066"},"PeriodicalIF":2.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3276","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196592","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}

{"title":"Multiple Seasonal Autoregressive Integrated Moving Average Models","authors":"Francesco Lisi,&nbsp;Matteo Grigoletto","doi":"10.1002/for.3283","DOIUrl":"https://doi.org/10.1002/for.3283","url":null,"abstract":"<p>Many empirical time series show periodic patterns. SARIMA models and exponential smoothing methods are classical approaches to account for seasonal dynamics. However, they allow to model just one periodic component, while several time series have multiple seasonality, with periodic components possibly tangled among them. To face this case, some seasonal-trend decomposition methods have been proposed in the literature, for example, the TBATS model, the MSTL model, the ADAM model, and the Prophet model, while SARIMA models have been quite neglected. To fill this gap, in this work, we suggest a suitable generalization of the SARIMA model, called mSARIMA, able to account for multiple seasonality. First, we define the model, describe its characteristics, and propose a test for residual multiperiodic correlation. Then, we analyze the predictive performance by comparing the mSARIMA model with other approaches, namely, the TBATS, MSTL, ADAM, and Prophet models, under different kinds of seasonality. The results suggest that when seasonality has a stochastic nature, mSARIMA models are more effective in predicting the series. However, if seasonality is basically deterministic, then the model decomposition approach is more suitable. Finally, we provide two comparative forecasting applications for the 5-min series of the number of calls handled by a large North American commercial bank and for the 10-min traffic data on the eastbound lanes of the Ventura Highway in Los Angeles.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 6","pages":"2037-2052"},"PeriodicalIF":2.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3283","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767431","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}

{"title":"Stock Return Prediction Based on a Functional Capital Asset Pricing Model","authors":"Ufuk Beyaztas,&nbsp;Kaiying Ji,&nbsp;Han Lin Shang,&nbsp;Eliza Wu","doi":"10.1002/for.3282","DOIUrl":"https://doi.org/10.1002/for.3282","url":null,"abstract":"<p>The capital asset pricing model (CAPM) is readily used to capture a linear relationship between the daily returns of an asset and a market index. We extend this model to an intraday high-frequency setting by proposing a functional CAPM estimation approach. The functional CAPM is a stylized example of a function-on-function linear regression with a bivariate functional regression coefficient. The two-dimensional regression coefficient measures the cross-covariance between cumulative intraday asset returns and market returns. We apply it to the Standard and Poor's 500 index and its constituent stocks to demonstrate its practicality. We investigate the functional CAPM's in-sample goodness of fit and out-of-sample prediction for an asset's cumulative intraday return. The findings suggest that the proposed functional CAPM methods have superior model goodness of fit and forecast accuracy compared to the traditional CAPM empirical estimation. In particular, the functional methods produce better model goodness of fit and prediction accuracy for stocks traditionally considered less price efficient or more information opaque.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 6","pages":"2017-2036"},"PeriodicalIF":2.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3282","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767963","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}

{"title":"Sparse Ensemble Matters: Evidence From Unemployment Rate Forecasting","authors":"Sheng Cheng,&nbsp;Han Feng,&nbsp;Jue Wang","doi":"10.1002/for.3281","DOIUrl":"https://doi.org/10.1002/for.3281","url":null,"abstract":"<div>\u0000 \u0000 <p>Sparse ensemble forecasting has become an increasingly competitive technique for forecasting research and practice in recent years. This paper examines the role of sparse ensemble in unemployment rates forecasting using expert forecasters. First, we show how the effectiveness of sparse ensembles is influenced by the complexity and accuracy of the base models. Second, we extend sparse regularization techniques to settings with unknown bias and variance employing Monte Carlo simulations. Third, we highlight the critical role of the regularization coefficient \u0000<span></span><math>\u0000 <mi>λ</mi></math>, which serves as a key shrinkage factor and necessitates a balance between model sparsity and forecasting accuracy. Experimental results on unemployment rate data demonstrate the superiority of sparse ensemble learning over equal-weight strategies. This framework provides novel insights into predictive modeling within the fields of economics and labor markets.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 6","pages":"2002-2016"},"PeriodicalIF":2.7,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767992","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}

{"title":"A Deep Learning Test of the Martingale Difference Hypothesis","authors":"João A. Bastos","doi":"10.1002/for.3280","DOIUrl":"https://doi.org/10.1002/for.3280","url":null,"abstract":"<div>\u0000 \u0000 <p>A deep learning binary classifier is proposed to test if asset returns follow martingale difference sequences. The Neyman–Pearson classification paradigm is applied to control the type I error of the test. In Monte Carlo simulations, I find that this approach has better power properties than variance ratio and portmanteau tests against several alternative processes. I apply this procedure to a large set of exchange rate returns and find that it detects several potential deviations from the martingale difference hypothesis that the conventional statistical tests fail to capture.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 6","pages":"1993-2001"},"PeriodicalIF":2.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767904","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}

{"title":"Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions","authors":"Alain Hecq,&nbsp;Marie Ternes,&nbsp;Ines Wilms","doi":"10.1002/for.3277","DOIUrl":"https://doi.org/10.1002/for.3277","url":null,"abstract":"<p>Reverse Unrestricted MIxed DAta Sampling (RU-MIDAS) regressions are used to model high-frequency responses by means of low-frequency variables. However, due to the periodic structure of RU-MIDAS regressions, the dimensionality grows quickly if the frequency mismatch between the high- and low-frequency variables is large. Additionally, the number of high-frequency observations available for estimation decreases. We propose to counteract this reduction in sample size by pooling the high-frequency coefficients and further reducing the dimensionality through a sparsity-inducing convex regularizer that accounts for the temporal ordering among the different lags. To this end, the regularizer prioritizes the inclusion of lagged coefficients according to the recency of the information they contain. We demonstrate the proposed method on two empirical applications, one on realized volatility forecasting with macroeconomic data and another on demand forecasting for a bicycle-sharing system with ridership data on other transportation types.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 6","pages":"1946-1968"},"PeriodicalIF":2.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3277","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767382","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}

{"title":"A Novel Framework for Agricultural Futures Price Prediction With BERT-Based Topic Identification and Sentiment Analysis","authors":"Wensheng Wang,&nbsp;Yuxi Liu","doi":"10.1002/for.3278","DOIUrl":"https://doi.org/10.1002/for.3278","url":null,"abstract":"<div>\u0000 \u0000 <p>In China's financial and economic system, the agricultural futures market plays an important role in guiding the market to self regulate and providing efficient information transmission for regulators. The effective prediction of futures prices can assist in guiding agricultural production, monitoring operational risks arising from significant price fluctuations, and enhancing the predictability and pertinence of the country's macroeconomic regulation policies. This study investigates the main variety of grain futures—soybean futures, taking into account complex market and non-market influencing factors. Using historical market data and related news headlines of soybean futures as source data and integrating topic identification and sentiment analysis techniques, a novel framework for predicting agricultural futures prices that integrates topic sentiment is constructed. This model uses BERTopic to extract topic information from agricultural news texts, then integrates FinBERT to construct topic-based sentiment features, fuses them with structured market features, and constructs LSTM price prediction model with multi-feature inputs. In order to better model the short-term features and state transfer patterns of the time series, hidden Markov model (HMM) is further used to extract the hidden states, which are deeply fused with the LSTM model. The empirical results show that the model fusing topic and sentiment features significantly improves the forecasting accuracy in all lags, LSTM works best in short-term forecasting, and the combination of HMM and LSTM exhibits significant performance advantages in medium- and long-term forecasting. Compared with the baseline model that relies only on market features, topic sentiment features provide important incremental information for price forecasting, and the contribution of each topic sentiment feature calculated based on the PI metric is close to 50%. In addition, deep learning–based prediction model performs better than baseline machine learning models in dealing with extreme external shocks such as climate disasters, the COVID-19 pandemic, and the Russia–Ukraine conflict.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 6","pages":"1969-1992"},"PeriodicalIF":2.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767381","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}

{"title":"Measuring the Impact of Transition Risk on Financial Markets: A Joint VaR-ES Approach","authors":"Laura Garcia-Jorcano,&nbsp;Lidia Sanchis-Marco","doi":"10.1002/for.3274","DOIUrl":"https://doi.org/10.1002/for.3274","url":null,"abstract":"&lt;p&gt;Based on a joint quantile and expected shortfall semiparametric methodology, we propose a novel approach to forecasting market risk conditioned to transition risk exposure. This method allows us to forecast two climate-related financial risk measures called \u0000&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;o&lt;/mi&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;l&lt;/mi&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;mi&gt;e&lt;/mi&gt;\u0000 &lt;mi&gt;V&lt;/mi&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;&lt;/math&gt; and \u0000&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;o&lt;/mi&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;l&lt;/mi&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;mi&gt;e&lt;/mi&gt;\u0000 &lt;mi&gt;E&lt;/mi&gt;\u0000 &lt;mi&gt;S&lt;/mi&gt;&lt;/math&gt;, being jointly elicitable, that capture the dependence of the European extreme bank returns on changes in carbon returns at extreme quantiles representing green and brown states. We evaluate our approach using a novel backtesting procedure and introduce related measures (\u0000&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mi&gt;Δ&lt;/mi&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;o&lt;/mi&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;l&lt;/mi&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;mi&gt;e&lt;/mi&gt;&lt;/math&gt; and \u0000&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mi&gt;E&lt;/mi&gt;\u0000 &lt;mi&gt;x&lt;/mi&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;mi&gt;o&lt;/mi&gt;\u0000 &lt;mi&gt;s&lt;/mi&gt;\u0000 &lt;mi&gt;u&lt;/mi&gt;\u0000 &lt;mi&gt;r&lt;/mi&gt;\u0000 &lt;mi&gt;e&lt;/mi&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;l&lt;/mi&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;mi&gt;e&lt;/mi&gt;&lt;/math&gt;). The main evidence states that the \u0000&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;o&lt;/mi&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;l&lt;/mi&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;mi&gt;e&lt;/mi&gt;\u0000 &lt;mi&gt;E&lt;/mi&gt;\u0000 &lt;mi&gt;S&lt;/mi&gt;&lt;/math&gt; measure presents the highest risk for the brown (green) state due to the presence of carbon cost (carbon risk premium) in Ph.II (Ph.III) of the EU Emissions Trading System. Furthermore, we found the highest (lowest) financial risk forecasts for \u0000&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;o&lt;/mi&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;l&lt;/mi&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;mi&gt;e&lt;/mi&gt;\u0000 &lt;mi&gt;E&lt;/mi&gt;\u0000 &lt;mi&gt;S&lt;/mi&gt;&lt;/math&gt; in green (brown) states during COVID-19. These results offer important implications for investors and policymakers regarding the effects of transition risk on the European financial system.&lt;","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 6","pages":"1907-1945"},"PeriodicalIF":2.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3274","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767378","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}