Content
July 2023, Volume 42, Issue 4
- 835-851 Semiparametric estimation of expected shortfall and its application in finance
by Yan Fang & Jian Li & Yinglin Liu & Yunfan Zhao - 852-871 Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage
by Mingzhe Wei & Georgios Sermpinis & Charalampos Stasinakis - 872-904 Uncertainty‐driven oil volatility risk premium and international stock market volatility forecasting
by Tong Fang & Deyu Miao & Zhi Su & Libo Yin - 905-923 Using shapely values to define subgroups of forecasts for combining
by Zhenni Ding & Huayou Chen & Ligang Zhou - 924-936 A review of scenario planning for emissions in environmental assessments
by Venmathy Samanaseh & Zainura Zainon Noor & Siti Norasyiqin & Che Hafizan & Muhammad Amani Mazlan & Florianna Lendai Michael - 937-956 Uncertainties and disagreements in expectations of professional forecasters: Evidence from an inflation targeting developing country
by Gabriel Caldas Montes & Igor Mendes Marcelino - 957-969 Electricity demand forecasting and risk management using Gaussian process model with error propagation
by Kuangyu Wen & Wenbin Wu & Ximing Wu - 970-988 Which factors drive Bitcoin volatility: Macroeconomic, technical, or both?
by Jiqian Wang & Feng Ma & Elie Bouri & Yangli Guo - 989-1007 A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies
by Carlos Trucíos & James W. Taylor - 1008-1035 A retrospective analysis of Journal of Forecasting: From 1982 to 2019
by Dejian Yu & Libo Sheng & Shunshun Shi
April 2023, Volume 42, Issue 3
- 455-463 Advances in forecasting: An introduction in light of the debate on inflation forecasting
by Anindya Banerjee & Stephen G. Hall & Georgios P. Kouretas & George S. Tavlas - 464-480 Nowcasting inflation with Lasso‐regularized vector autoregressions and mixed frequency data
by Tesi Aliaj & Milos Ciganovic & Massimiliano Tancioni - 481-513 Forecasting inflation in open economies: What can a NOEM model do?
by Roberto Duncan & Enrique Martínez‐García - 514-529 Forecasting inflation: The use of dynamic factor analysis and nonlinear combinations
by Stephen G. Hall & George S. Tavlas & Yongli Wang - 530-542 Evaluation and indirect inference estimation of inattentive features in a New Keynesian framework
by Jenyu Chou & Yifei Cao & Patrick Minford - 543-565 Forecasting housing investment
by Carlos Cañizares Martínez & Gabe J. de Bondt & Arne Gieseck - 566-577 Assessing the informational content of card transactions for nowcasting retail trade: Evidence for Latvia
by Anete Brinke & Ludmila Fadejeva & Boriss Siliverstovs & Kārlis Vilerts - 578-624 Jump forecasting in foreign exchange markets: A high‐frequency analysis
by Sevcan Uzun & Ahmet Sensoy & Duc Khuong Nguyen - 625-642 The role of expectations for currency crisis dynamics—The case of the Turkish lira
by Joscha Beckmann & Robert L. Czudaj - 643-656 The effects of shocks to interest rate expectations in the euro area: Estimates at the country level
by Martin Mandler & Michael Scharnagl - 657-684 Forecasting sovereign risk in the Euro area via machine learning
by Guillaume Belly & Lukas Boeckelmann & Carlos Mateo Caicedo Graciano & Alberto Di Iorio & Klodiana Istrefi & Vasileios Siakoulis & Arthur Stalla‐Bourdillon - 685-714 Worse than you think: Public debt forecast errors in advanced and developing economies
by Julia Estefania‐Flores & Davide Furceri & Siddharth Kothari & Jonathan D. Ostry - 715-738 Macro‐financial effects of monetary policy easing
by George N. Apostolakis & Nikolaos Giannellis & Athanasios P. Papadopoulos
March 2023, Volume 42, Issue 2
- 195-211 Robust forecasting in spatial autoregressive model with total variation regularization
by He Jiang - 212-222 Trading cryptocurrencies using algorithmic average true range systems
by Gil Cohen - 223-239 Structural and predictive analyses with a mixed copula‐based vector autoregression model
by Woraphon Yamaka & Rangan Gupta & Sukrit Thongkairat & Paravee Maneejuk - 240-259 Nonlinear inflation forecasting with recurrent neural networks
by Anna Almosova & Niek Andresen - 260-287 Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy
by Yuqi Dong & Jianzhou Wang & Xinsong Niu & Bo Zeng - 288-311 The effect of environment on housing prices: Evidence from the Google Street View
by Guan‐Yuan Wang - 312-330 Text‐based soybean futures price forecasting: A two‐stage deep learning approach
by Wuyue An & Lin Wang & Yu‐Rong Zeng - 331-346 Forecasting inflation and output growth with credit‐card‐augmented Divisia monetary aggregates
by William A. Barnett & Sohee Park - 347-368 Modeling the relation between the US real economy and the corporate bond‐yield spread in Bayesian VARs with non‐Gaussian innovations
by Tamás Kiss & Stepan Mazur & Hoang Nguyen & Pär Österholm - 369-401 Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil
by Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube - 402-417 Application of machine learning techniques to predict entrepreneurial firm valuation
by Ruling Zhang & Zengrui Tian & Killian J. McCarthy & Xiao Wang & Kun Zhang - 418-451 Real‐time forecasting of the Australian macroeconomy using flexible Bayesian VARs
by Chenghan Hou & Bao Nguyen & Bo Zhang
January 2023, Volume 42, Issue 1
- 3-16 Geopolitical risk and global financial cycle: Some forecasting experiments
by Afees A. Salisu & Philip C. Omoke & Abdulsalam Abidemi Sikiru - 17-33 Forecasting energy prices: Quantile‐based risk models
by Nicholas Apergis - 34-50 Estimation of short‐run predictive factor for US growth using state employment data
by Arabinda Basistha - 51-59 Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH‐MIDAS and deep learning models
by Yuping Song & Xiaolong Tang & Hemin Wang & Zhiren Ma - 60-75 A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information
by Feng Ma & M. I. M. Wahab & Julien Chevallier & Ziyang Li - 76-100 Trading volume and realized volatility forecasting: Evidence from the China stock market
by Min Liu & Wei‐Chong Choo & Chi‐Chuan Lee & Chien‐Chiang Lee - 101-123 Wind power prediction based on wind speed forecast using hidden Markov model
by Khatereh Ghasvarian Jahromi & Davood Gharavian & Hamid Reza Mahdiani - 124-153 Power grid operation optimization and forecasting using a combined forecasting system
by Lifang Zhang & Jianzhou Wang & Zhenkun Liu - 154-175 A new PM2.5 concentration forecasting system based on AdaBoost‐ensemble system with deep learning approach
by Zhongfei Li & Kai Gan & Shaolong Sun & Shouyang Wang - 176-192 A hybrid approach with step‐size aggregation to forecasting hierarchical time series
by Hakeem‐Ur Rehman & Guohua Wan & Raza Rafique
December 2022, Volume 41, Issue 8
- 1559-1569 Interest rate uncertainty and the predictability of bank revenues
by Oguzhan Cepni & Riza Demirer & Rangan Gupta & Ahmet Sensoy - 1570-1577 A Siamese network framework for bank intelligent Q&A prediction
by Wei Wei & Yingli Liang - 1578-1594 Mixed membership nearest neighbor model with feature difference
by Simon K. C. Cheung & Tommy K. Y. Cheung - 1595-1607 Forecasting value at risk and expected shortfall using high‐frequency data of domestic and international stock markets
by Man Wang & Yihan Cheng - 1608-1622 A generalized two‐factor square‐root framework for modeling occurrences of natural catastrophes
by Giuseppe Orlando & Michele Bufalo - 1623-1638 High‐frequency data and stock–bond investing
by Yu‐Sheng Lai - 1639-1660 Predicting earnings management through machine learning ensemble classifiers
by Ahmad Hammami & Mohammad Hendijani Zadeh - 1661-1668 Cryptocurrencies trading algorithms: A review
by Isabela Ruiz Roque da Silva & Eli Hadad Junior & Pedro Paulo Balbi - 1669-1690 Deep learning meets decision trees: An application of a heterogeneous deep forest approach in credit scoring for online consumer lending
by Yufei Xia & Xinyi Guo & Yinguo Li & Lingyun He & Xueyuan Chen - 1691-1700 Forecasting chlorophyll‐a concentration using empirical wavelet transform and support vector regression
by Jin‐Won Yu & Ju‐Song Kim & Yun‐Chol Jong & Xia Li & Gwang‐Il Ryang - 1701-1724 Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting
by Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva - 1725-1740 The role of investor sentiment in forecasting housing returns in China: A machine learning approach
by Oguzhan Cepni & Rangan Gupta & Yigit Onay
November 2022, Volume 41, Issue 7
- 1317-1337 Bayesian quantile forecasting via the realized hysteretic GARCH model
by Cathy W. S. Chen & Edward M. H. Lin & Tara F. J. Huang - 1338-1355 Are internally consistent forecasts rational?
by Jing Tian & Firmin Doko Tchatoka & Thomas Goodwin - 1356-1371 Forgetting approaches to improve forecasting
by Robert A. Hill & Paulo M. M. Rodrigues - 1372-1385 Central bank information and private‐sector expectations
by Jochen Güntner - 1386-1415 Modeling credit risk with a multi‐stage hybrid model: An alternative statistical approach
by Mohammad Shamsu Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib & Kunpeng Yuan - 1416-1432 Evaluating the predictive power of intraday technical trading in China's crude oil market
by Xiaoye Jin - 1433-1457 Forecasting international equity market volatility: A new approach
by Chao Liang & Yan Li & Feng Ma & Yaojie Zhang - 1458-1482 Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction
by Zi‐yu Chen & Fei Xiao & Xiao‐kang Wang & Min‐hui Deng & Jian‐qiang Wang & Jun‐Bo Li - 1483-1511 Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm
by Ying Wang & Jianzhou Wang & Hongmin Li & Hufang Yang & Zhiwu Li - 1512-1524 A weights direct determination neuronet for time‐series with applications in the industrial indices of the Federal Reserve Bank of St. Louis
by Spyridon D. Mourtas - 1525-1556 Uncertainty and predictability of real housing returns in the United Kingdom: A regional analysis
by Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna & Mark E. Wohar
September 2022, Volume 41, Issue 6
- 1049-1064 Uncertainty and forecastability of regional output growth in the UK: Evidence from machine learning
by Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch - 1065-1086 Distributional modeling and forecasting of natural gas prices
by Jonathan Berrisch & Florian Ziel - 1087-1098 Parallel architecture of CNN‐bidirectional LSTMs for implied volatility forecast
by Ji‐Eun Choi & Dong Wan Shin - 1099-1130 Forecast evaluation of DSGE models: Linear and nonlinear likelihood
by Kuo‐Hsuan Chin - 1131-1155 Anticipating financial distress of high‐tech startups in the European Union: A machine learning approach for imbalanced samples
by Yang Liu & Qingguo Zeng & Bobo Li & Lili Ma & Joaquín Ordieres‐Meré - 1156-1180 A novel robust structural quadratic forecasting model and applications
by He Jiang - 1181-1200 Nowcasting world GDP growth with high‐frequency data
by Caroline Jardet & Baptiste Meunier - 1201-1216 Deep learning with regularized robust long‐ and short‐term memory network for probabilistic short‐term load forecasting
by He Jiang & Weihua Zheng - 1217-1247 Subsampled factor models for asset pricing: The rise of Vasa
by Gianluca De Nard & Simon Hediger & Markus Leippold - 1248-1313 A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction
by Weidong Guo & Zach Zhizhong Zhou
August 2022, Volume 41, Issue 5
- 871-910 Predicting financial crises with machine learning methods
by Lanbiao Liu & Chen Chen & Bo Wang - 911-919 Stock market as a nowcasting indicator for real investment
by Stavros Degiannakis - 920-932 ANN–polynomial–Fourier series modeling and Monte Carlo forecasting of tourism data
by Salim Jibrin Danbatta & Asaf Varol - 933-944 Volatility forecasting for crude oil based on text information and deep learning PSO‐LSTM model
by Xingrui Jiao & Yuping Song & Yang Kong & Xiaolong Tang - 945-955 Cryptocurrency exchanges: Predicting which markets will remain active
by George Milunovich & Seung Ah Lee - 956-979 Corporate failure prediction using threshold‐based models
by David Veganzones - 980-996 Forecasting stock return volatility: The role of shrinkage approaches in a data‐rich environment
by Zhifeng Dai & Tingyu Li & Mi Yang - 997-1016 The influence of policy uncertainty on exchange rate forecasting
by Lee A. Smales - 1017-1036 A model sufficiency test using permutation entropy
by Xin Huang & Han Lin Shang & David Pitt - 1037-1045 Limited memory predictors based on polynomial approximation of periodic exponentials
by Nikolai Dokuchaev
July 2022, Volume 41, Issue 4
- 677-696 Multiperiod default probability forecasting
by Oliver Blümke - 697-717 A new hedging hypothesis regarding prediction interval formation in stock price forecasting
by Dan Zhu & Qingwei Wang & John Goddard - 718-751 Mixed data sampling regression: Parameter selection of smoothed least squares estimator
by Selma Toker & Nimet Özbay & Kristofer Månsson - 752-764 Recession forecasting with high‐dimensional data
by Lauri Nevasalmi - 765-792 Uncertainty and the predictability of stock returns
by Wensheng Cai & Zhiyuan Pan & Yudong Wang - 793-809 Dendritic neuron model neural network trained by modified particle swarm optimization for time‐series forecasting
by Ayse Yilmaz & Ufuk Yolcu - 810-828 Uncertainty and disagreement of inflation expectations: Evidence from household‐level qualitative survey responses
by Yongchen Zhao - 829-839 Evaluating heterogeneous forecasts for vintages of macroeconomic variables
by Philip Hans Franses & Max Welz - 840-852 Do sentiment indices always improve the prediction accuracy of exchange rates?
by Takumi Ito & Fumiko Takeda - 853-868 Forecasting oil futures realized range‐based volatility with jumps, leverage effect, and regime switching: New evidence from MIDAS models
by Xinjie Lu & Feng Ma & Jiqian Wang & Jing Liu
April 2022, Volume 41, Issue 3
- 407-421 Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model
by Qifa Xu & Lu Chen & Cuixia Jiang & Yezheng Liu - 422-434 Measuring multi‐volatility states of financial markets based on multifractal clustering model
by Xun Huang & Huiyue Tang - 435-454 The mutual predictability of Bitcoin and web search dynamics
by Bernd Süssmuth - 455-490 Random forest versus logit models: Which offers better early warning of fiscal stress?
by Barbara Jarmulska - 491-513 Assessing the usefulness of survey‐based data in forecasting firms' capital formation: Evidence from Italy
by Claire Giordano & Marco Marinucci & Andrea Silvestrini - 514-538 The global latent factor and international index futures returns predictability
by Shu‐Lien Chang & Hsiu‐Chuan Lee & Donald Lien - 539-550 A novel deep learning model based on convolutional neural networks for employee churn prediction
by Ebru Pekel Ozmen & Tuncay Ozcan - 551-566 Forecasting unemployment in the euro area with machine learning
by Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos - 567-591 Firm dynamics and bankruptcy processes: A new theoretical model
by Şaban Çelik & Bora Aktan & Bruce Burton - 592-614 Fundamental index aligned and excess market return predictability
by Samuel YM Ze‐To - 615-632 Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine
by Wen Zhang & Zhibin Wu - 633-652 Forecasting Bitcoin volatility: A new insight from the threshold regression model
by Yaojie Zhang & Mengxi He & Danyan Wen & Yudong Wang - 653-674 A dynamic scenario‐driven technique for stock price prediction and trading
by Yash Thesia & Vidhey Oza & Priyank Thakkar
March 2022, Volume 41, Issue 2
- 213-229 Evaluating the Eurosystem/ECB staff macroeconomic projections: The first 20 years
by G. Kontogeorgos & K. Lambrias - 230-251 Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach
by Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang - 252-258 Investigating the predictive ability of ONS big data‐based indicators
by George Kapetanios & Fotis Papailias - 259-278 Forecasting volatilities of oil and gas assets: A comparison of GAS, GARCH, and EGARCH models
by Yingying Xu & Donald Lien - 279-293 Bootstrap VAR forecasts: The effect of model uncertainties
by Diego Fresoli - 294-302 Spatial beta‐convergence forecasting models: Evidence from municipal homicide rates in Colombia
by Felipe Santos‐Marquez - 303-315 Forecasting realized volatility of international REITs: The role of realized skewness and realized kurtosis
by Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch - 316-330 Time‐varying trend models for forecasting inflation in Australia
by Na Guo & Bo Zhang & Jamie L. Cross - 331-344 Competition can help predict sales
by Sima M. Fortsch & Jeong Hoon Choi & Elena A. Khapalova - 345-360 Multistage optimization filter for trend‐based short‐term forecasting
by Usman Zafar & Neil Kellard & Dmitri Vinogradov - 361-382 What matters when developing oil price volatility forecasting frameworks?
by Panagiotis Delis & Stavros Degiannakis & George Filis - 383-404 Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility
by Hardik A. Marfatia & Qiang Ji & Jiawen Luo
January 2022, Volume 41, Issue 1
- 3-16 Singular spectrum analysis for value at risk in stochastic volatility models
by Josu Arteche & Javier García‐Enríquez - 17-42 Step‐ahead spot price densities using daily synchronously reported prices and wind forecasts
by Per B. Solibakke - 43-63 A state‐dependent linear recurrent formula with application to time series with structural breaks
by Donya Rahmani & Damien Fay - 64-85 A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China
by Pei Du & Jianzhou Wang & Wendong Yang & Tong Niu - 86-99 Moving beyond Volatility Index (VIX): HARnessing the term structure of implied volatility
by Adam Clements & Yin Liao & Yusui Tang - 100-117 Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels
by Bangzhu Zhu & Shunxin Ye & Ping Wang & Julien Chevallier & Yi‐Ming Wei - 118-133 A new Markov regime‐switching count time series approach for forecasting initial public offering volumes and detecting issue cycles
by Xinyu Wang & Cathy Ning - 134-157 Mixed‐frequency forecasting of crude oil volatility based on the information content of global economic conditions
by Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji - 158-166 Optimal forecast error as an unbiased estimator of abnormal return: A proposition
by Onur Enginar & Kazim Baris Atici - 167-180 Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series
by Miguel de Carvalho & Gabriel Martos - 181-200 A Bayesian time‐varying autoregressive model for improved short‐term and long‐term prediction
by Christoph Berninger & Almond Stöcker & David Rügamer - 201-210 Comparison of prospective Hawkes and recursive point process models for Ebola in DRC
by Sarita D. Lee & Andy A. Shen & Junhyung Park & Ryan J. Harrigan & Nicole A. Hoff & Anne W. Rimoin & Frederic Paik Schoenberg
December 2021, Volume 40, Issue 8
- 1379-1397 Scheduled macroeconomic news announcements and Forex volatility forecasting
by Tomáš Plíhal - 1398-1419 Conditional covariance matrix forecast using the hybrid exponentially weighted moving average approach
by Wei Kuang - 1420-1443 Systemic risk and macroeconomic forecasting: A globally applicable copula‐based approach
by Ghufran Ahmad & Muhammad Suhail Rizwan & Dawood Ashraf - 1444-1462 The reliability of geometric Brownian motion forecasts of S&P500 index values
by Amit K. Sinha - 1463-1478 Forecasting stock return volatility using a robust regression model
by Mengxi He & Xianfeng Hao & Yaojie Zhang & Fanyi Meng - 1479-1500 Predicting stock market volatility based on textual sentiment: A nonlinear analysis
by Weiguo Zhang & Xue Gong & Chao Wang & Xin Ye - 1501-1523 Forecasting regular and extreme gold price volatility: The roles of asymmetry, extreme event, and jump
by Xiafei Li & Dongxin Li & Xuhui Zhang & Guiwu Wei & Lan Bai & Yu Wei - 1524-1539 Multisource evidence theory‐based fraud risk assessment of China's listed companies
by Shi Qiu & Yuansheng Luo & Hongwei Guo - 1540-1565 Time series forecasting methods for the Baltic dry index
by Christos Katris & Manolis G. Kavussanos - 1566-1580 Interest rates forecasting: Between Hull and White and the CIR#—How to make a single‐factor model work
by Giuseppe Orlando & Michele Bufalo - 1581-1595 Stock markets and exchange rate behavior of the BRICS
by Afees A. Salisu & Juncal Cuñado & Kazeem Isah & Rangan Gupta - 1596-1610 On stock volatility forecasting based on text mining and deep learning under high‐frequency data
by Bolin Lei & Zhengdi Liu & Yuping Song
November 2021, Volume 40, Issue 7
- 1133-1153 The prudential role of Basel III liquidity provisions towards financial stability
by Stephanos Papadamou & Dimitrios Sogiakas & Vasilios Sogiakas & Kanellos Toudas - 1154-1178 Application of Google Trends‐based sentiment index in exchange rate prediction
by Takumi Ito & Motoki Masuda & Ayaka Naito & Fumiko Takeda - 1179-1189 Assessing liquidity‐adjusted risk forecasts
by Theo Berger & Christina Uffmann - 1190-1213 Forecasting value at risk and conditional value at risk using option market data
by Annalisa Molino & Carlo Sala - 1214-1229 Do local and global factors impact the emerging markets' sovereign yield curves? Evidence from a data‐rich environment
by Oguzhan Cepni & Ibrahim Ethem Guney & Doruk Kucuksarac & M. Hasan Yilmaz - 1230-1244 Convolution‐based filtering and forecasting: An application to WTI crude oil prices
by Christian Gourieroux & Joann Jasiak & Michelle Tong - 1245-1273 Agricultural commodity price dynamics and their determinants: A comprehensive econometric approach
by Jesus Crespo Cuaresma & Jaroslava Hlouskova & Michael Obersteiner - 1274-1290 Prediction of remaining time on site for e‐commerce users: A SOM and long short‐term memory study
by Ling‐Jing Kao & Chih‐Chou Chiu & Hung‐Jui Wang & Chang Yu Ko - 1291-1309 Cointegration, information transmission, and the lead‐lag effect between industry portfolios and the stock market
by Victor Troster & José Penalva & Abderrahim Taamouti & Dominik Wied - 1310-1324 The information content of uncertainty indices for natural gas futures volatility forecasting
by Chao Liang & Feng Ma & Lu Wang & Qing Zeng - 1325-1341 Human resources and corporate failure prediction modeling: Evidence from Belgium
by Xavier Brédart & Eric Séverin & David Veganzones - 1342-1375 Forecasting asset returns with network‐based metrics: A statistical and economic analysis
by Eduard Baitinger
September 2021, Volume 40, Issue 6
- 945-962 Shocks to the equity capital ratio of financial intermediaries and the predictability of stock return volatility
by Feng He & Libo Yin - 963-976 Forecasting US overseas travelling with univariate and multivariate models
by Apergis Nicholas - 977-999 Forecasting exchange rates for Central and Eastern European currencies using country‐specific factors
by Krystian Jaworski - 1000-1026 Recession probabilities for the Eurozone at the zero lower bound: Challenges to the term spread and rise of alternatives
by Ralf Fendel & Nicola Mai & Oliver Mohr - 1027-1053 Rationality and anchoring of inflation expectations: An assessment from survey‐based and market‐based measures
by Helder Ferreira de Mendonça & Pedro Mendes Garcia & José Valentim Machado Vicente - 1054-1069 Forecasting of intermittent demands under the risk of inventory obsolescence
by Kamal Sanguri & Kampan Mukherjee - 1070-1085 Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models
by Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier - 1086-1094 Testing bias in professional forecasts
by Philip Hans Franses - 1095-1117 Strategic bias and popularity effect in the prediction of economic surprises
by Luiz Félix & Roman Kräussl & Philip Stork - 1118-1130 Non‐linear mixed‐effects models for time series forecasting of smart meter demand
by Cameron Roach & Rob Hyndman & Souhaib Ben Taieb
August 2021, Volume 40, Issue 5
- 733-768 Forecasting US stock market volatility: How to use international volatility information
by Yaojie Zhang & Yudong Wang & Feng Ma - 769-791 Should crude oil price volatility receive more attention than the price of crude oil? An empirical investigation via a large‐scale out‐of‐sample forecast evaluation of US macroeconomic data
by Nima Nonejad - 792-816 An empirical study on the role of trading volume and data frequency in volatility forecasting
by Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo - 817-833 The value added of the Bank of Japan's range forecasts
by Yoichi Tsuchiya - 834-848 Treating cross‐sectional and time series momentum returns as forecasts
by Oh Kang Kwon & Stephen Satchell - 849-860 Can night trading sessions improve forecasting performance of gold futures' volatility in China?
by Xuan Yao & Xiaofeng Hui & Kaican Kang - 861-882 Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models
by Konstantin Kuck & Karsten Schweikert - 883-910 Intraday conditional value at risk: A periodic mixed‐frequency generalized autoregressive score approach
by Tobias Eckernkemper & Bastian Gribisch - 911-920 Design of link prediction algorithm for complex network based on the comprehensive influence of predicting nodes and neighbor nodes
by Yang Wang & Jifa Wang - 921-941 Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect
by Jiqian Wang & Feng Ma & M.I.M. Wahab & Dengshi Huang
July 2021, Volume 40, Issue 4
- 577-602 Research constituents, intellectual structure, and collaboration pattern in the Journal of Forecasting: A bibliometric analysis
by H. Kent Baker & Satish Kumar & Debidutta Pattnaik - 603-625 A performance analysis of prediction intervals for count time series
by Annika Homburg & Christian H. Weiß & Layth C. Alwan & Gabriel Frahm & Rainer Göb - 626-635 Granger causality of bivariate stationary curve time series
by Han Lin Shang & Kaiying Ji & Ufuk Beyaztas - 636-652 The tensor auto‐regressive model
by Chelsey Hill & James Li & Matthew J. Schneider & Martin T. Wells - 653-666 Stock index forecasting: A new fuzzy time series forecasting method
by Hao Wu & Haiming Long & Yue Wang & Yanqi Wang - 667-685 Forecasting volatility with outliers in Realized GARCH models
by Guanghui Cai & Zhimin Wu & Lei Peng - 686-699 An approach to increasing forecast‐combination accuracy through VAR error modeling
by Till Weigt & Bernd Wilfling - 700-707 Point and density forecasting of macroeconomic and financial uncertainties of the USA
by Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna - 708-729 Forecasting systemic risk in portfolio selection: The role of technical trading rules
by Noureddine Kouaissah & Amin Hocine
April 2021, Volume 40, Issue 3
- 367-386 Forecasting negative yield‐curve distributions
by Jae‐Yun Jun & Victor Lebreton & Yves Rakotondratsimba - 387-415 Is optimum always optimal? A revisit of the mean‐variance method under nonlinear measures of dependence and non‐normal liquidity constraints
by Mazin A.M. Al Janabi - 416-438 Predicting intraday jumps in stock prices using liquidity measures and technical indicators
by Ao Kong & Hongliang Zhu & Robert Azencott - 439-457 Forecasting financial vulnerability in the USA: A factor model approach
by Hyeongwoo Kim & Wen Shi - 458-480 Forecasting the production side of GDP
by Gregor Bäurle & Elizabeth Steiner & Gabriel Züllig - 481-499 Forecasting US inflation using Markov dimension switching
by Jan Prüser - 500-511 Dynamic VaR forecasts using conditional Pearson type IV distribution
by Wei Kuang - 512-527 Block bootstrap prediction intervals for parsimonious first‐order vector autoregression
by Jing Li - 528-546 Forecasting mortality rates with the adaptive spatial temporal autoregressive model
by Yanlin Shi - 547-574 State‐dependent evaluation of predictive ability
by Boriss Siliverstovs & Daniel S. Wochner