Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2024

• Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024, "Forecasting Growth-at-Risk of the United States: Housing Price versus Housing Sentiment or Attention," Working Papers, University of Pretoria, Department of Economics, number 202401, Jan.
• Massimiliano Caporin & Petre Caraiani & Oguzhan Cepni & Rangan Gupta, 2024, "Predicting the Conditional Distribution of US Stock Market Systemic Stress: The Role of Climate Risks," Working Papers, University of Pretoria, Department of Economics, number 202407, Mar.
• Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024, "Forecasting Realized US Stock Market Volatility: Is there a Role for Economic Policy Uncertainty?," Working Papers, University of Pretoria, Department of Economics, number 202408, Mar.
• Afees A. Salisu & Ahamuefula E.Oghonna & Rangan Gupta & Oguzhan Cepni, 2024, "Energy Market Uncertainties and US State-Level Stock Market Volatility: A GARCH-MIDAS Approach," Working Papers, University of Pretoria, Department of Economics, number 202409, Mar.
• Bruno Tag Sales & Hudson Da Silva Torrent & Rangan Gupta, 2024, "Forecasting Real Housing Price Returns of the United States using Machine Learning: The Role of Climate Risks," Working Papers, University of Pretoria, Department of Economics, number 202412, Mar.
• Oguzhan Cepni & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2024, "Political Geography and Stock Market Volatility: The Role of Political Alignment across Sentiment Regimes," Working Papers, University of Pretoria, Department of Economics, number 202414, Mar.
• Matteo Foglia & Vasilios Plakandaras & Rangan Gupta & Qiang Ji, 2024, "Long-Span Multi-Layer Spillovers between Moments of Advanced Equity Markets: The Role of Climate Risks," Working Papers, University of Pretoria, Department of Economics, number 202415, Apr.
• Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta & Qiang Ji, 2024, "Energy Market Uncertainties and Exchange Rate Volatility: A GARCH-MIDAS Approach," Working Papers, University of Pretoria, Department of Economics, number 202418, Apr.
• Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2024, "Climate Risks and Forecastability of US Inflation: Evidence from Dynamic Quantile Model Averaging," Working Papers, University of Pretoria, Department of Economics, number 202420, May.
• Thanoj K. Muddana & Komal S.R. Bhimireddy & Anandamayee Majumdar & Rangan Gupta, 2024, "Forecasting Gold Returns Volatility Over 1258-2023: The Role of Moments," Working Papers, University of Pretoria, Department of Economics, number 202421, May.
• Rangan Gupta & Christian Pierdzioch, 2024, "Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices," Working Papers, University of Pretoria, Department of Economics, number 202423, Jun.
• Elie Bouri & Rangan Gupta & Asingamaanda Liphadzi & Christian Pierdzioch, 2024, "Forecasting Stock Returns Volatility of the G7 Over Centuries: The Role of Climate Risks," Working Papers, University of Pretoria, Department of Economics, number 202424, Jun.
• Kejin Wu & Sayar Karmakar & Rangan Gupta, 2024, "GARCHX-NoVaS: A Model-Free Approach to Incorporate Exogenous Variables," Working Papers, University of Pretoria, Department of Economics, number 202425, Jun.
• Rangan Gupta & Christian Pierdzioch & Aviral K. Tiwari, 2024, "Gasoline Prices and Presidential Approval Ratings of the United States," Working Papers, University of Pretoria, Department of Economics, number 202427, Jun.
• Rangan Gupta & Christian Pierdzioch, 2024, "Climate Policy Uncertainty and Financial Stress: Evidence for China," Working Papers, University of Pretoria, Department of Economics, number 202428, Jun.
• Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta, 2024, "Geopolitical Risks and Oil Returns Volatility: A GARCH-MIDAS Approach," Working Papers, University of Pretoria, Department of Economics, number 202429, Jun.
• Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta & Sisa Shiba, 2024, "Energy Market Uncertainties and Gold Return Volatility: A GARCH-MIDAS Approach," Working Papers, University of Pretoria, Department of Economics, number 202431, Jul.
• Afees A. Salisu & Ahamuefula E. Ogbonna & Elie Bouri & Rangan Gupta, 2024, "Climate Risks and Prediction of Sectoral REITs Volatility: International Evidence," Working Papers, University of Pretoria, Department of Economics, number 202434, Aug.
• Elie Bouri & Rangan Gupta & Christian Pierdzioch & Onur Polat, 2024, "Forecasting U.S. Recessions Using Over 150 Years of Data: Stock-Market Moments versus Oil-Market Moments," Working Papers, University of Pretoria, Department of Economics, number 202435, Aug.
• Rangan Gupta & Anandamayee Majumdar & Christian Pierdzioch & Onur Polat, 2024, "Climate Risks and Real Gold Returns over 750 Years," Working Papers, University of Pretoria, Department of Economics, number 202436, Aug.
• Vincenzo Candila & Oguzhan Cepni & Giampiero M. Gallo & Rangan Gupta, 2024, "Influence of Local and Global Economic Policy Uncertainty on the Volatility of US State-Level Equity Returns: Evidence from a GARCH-MIDAS Approach with Shrinkage and Cluster Analysis," Working Papers, University of Pretoria, Department of Economics, number 202437, Aug.
• O-Chia Chuang & Rangan Gupta & Christian Pierdzioch & Buliao Shu, 2024, "Financial Uncertainty and Gold Market Volatility: Evidence from a GARCH-MIDAS Approach with Variable Selection," Working Papers, University of Pretoria, Department of Economics, number 202441, Sep.
• Afees A. Salisu & Ahamuefula E. Ogbonna & Elie Bouri & Rangan Gupta, 2024, "Economic Policy Uncertainty and Bank-Level Stock Returns Volatility of the United States: A Mixed-Frequency Perspective," Working Papers, University of Pretoria, Department of Economics, number 202444, Oct.
• Matteo Bonato & Rangan Gupta & Christian Pierdzioch, 2024, "Do Shortages Forecast Aggregate and Sectoral U.S. Stock Market Realized Variance? Evidence from a Century of Data," Working Papers, University of Pretoria, Department of Economics, number 202450, Nov.
• Andrea Kolková, 2024, "Data Analysis in Demand Forecasting: A Case Study of Poetry Book Sales in the European Area," Central European Business Review, Prague University of Economics and Business, volume 2024, issue 5, pages 51-69, DOI: 10.18267/j.cebr.371.
• Qi Shi, 2024, "The Second RP-PCA Factor and Crude Oil Price Predictability," Prague Economic Papers, Prague University of Economics and Business, volume 2024, issue 6, pages 662-690, DOI: 10.18267/j.pep.879.
• Ayaz Zeynalov, 2024, "Impact of Oil Price Shocks on Russian Macroeconomic Performance," Politická ekonomie, Prague University of Economics and Business, volume 2024, issue 4, pages 676-701, DOI: 10.18267/j.polek.1412.
• Oktay Özkan & Babatunde Sunday Eweade & Tomiwa Sunday Adebayo, 2024, "Examining the Effects of Energy Efficiency R&D and Renewable Energy on Environmental Sustainability Amidst Political Risk in France," Politická ekonomie, Prague University of Economics and Business, volume 2024, issue Spec.issu, pages 331-356, DOI: 10.18267/j.polek.1437.
• João Amador & Paulo Barbosa & João Cortes, 2024, "Distance to Export: A Machine Learning Approach with Portuguese Firms," Working Papers, Banco de Portugal, Economics and Research Department, number w202420.
• Luke Hartigan & Tom Rosewall, 2024, "Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator," RBA Research Discussion Papers, Reserve Bank of Australia, number rdp2024-04, Jul, DOI: 10.47688/rdp2024-04.
• Tenorio, Juan & Perez, Wilder, 2024, "GDP nowcasting with Machine Learning and Unstructured Data," Working Papers, Banco Central de Reserva del Perú, number 2024-003, Apr.
• Fernando Pérez Forero, 2024, "Forecasting Peruvian Monetary Aggregates in a Nonlinear and Uncertain Environment," Working Papers, Banco Central de Reserva del Perú, number 2024-010, Dec.
• Fernando Pérez Forero, 2024, "Exploring the presence of Nonlinearities in the Peruvian Economy - Monetary Policy Implications," Working Papers, Banco Central de Reserva del Perú, number 2024-017, Dec.
• Foteini Kyriazi & Efthymios Xylangouras & Theodoros Papadogonas, 2024, "On the Forecastability of Agricultural Output," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, volume 16, issue 4, pages 443-467, December, DOI: https://doi.org/10.15353/rea.v16i4..
• Saswat Patra & Malay Bhattacharyya, 2024, "Charting the Unknown: First Passage Time Probabilities for Pearson Diffusion Process and Application to Options Risk Management," American Business Review, Pompea College of Business, University of New Haven, volume 27, issue 2, pages 623-639.
• Chalerm Jaitang & Zhaohua Li & Christopher Gan, 2024, "An Empirical Analysis of Private SMEs' Insolvency in Thailand Using Machine Learning," Asian Journal of Applied Economics/ Applied Economics Journal, Kasetsart University, Faculty of Economics, Center for Applied Economic Research, volume 31, issue 2, pages 1-30.
• Georgy Bronitsky & Elena Vakulenko, 2024, "Using Google Trends to forecast migration from Russia: Search query aggregation and accounting for lag structure," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 73, pages 78-101.
• Ekaterina Astafyeva & Marina Turuntseva, 2024, "Forecast evaluation improving using the simplest methods of individual forecasts’ combination," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 74, pages 78-103.
• Marina Mikitchuk, 2024, "Forming the benefit-oriented official assistance: Cross-country analysis," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 74, pages 124-143.
• Anton Skrobotov, 2024, "Time series forecasting under structural breaks," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 76, pages 120-139.
• Berit Hanna Czock & Cordelia Frings & Fabian Arnold, 2024, "Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany," EWI Working Papers, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI), number 2024-4, Jun.
• Morteza Beiranvand & Seyed Saeed Malek Sadati & Seyed Mohammad Javad Razmi, 2024, "Nowcasting Iran's GDP Using Sentiment Analysis of Economic News," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, volume 11, issue 3, pages 135-164.
• Mihaela SIMIONESCU, 2024, "The Role of the European Directive on Renewable Energy Consumption in Reducing Pollution in CEE Countries from the European Union," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 2, pages 5-21, July.
• Taoxiong Liu & Huolan Cheng, 2024, "Can The Classical Economic Model Improve The Performance Of Deep Learning? A GDP Forecasting Example," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 2, pages 86-110, July.
• Xianning WANG & Xikai HUANG & Longkun TIAN & Huiyan ZHOU, 2024, "Can the Futures Price of Agricultural Products Predict the Scale of China's Agricultural Production?," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 4, pages 128-143, December.
• Pablo PINCHEIRA-BROWN & Nicolás HARDY, 2024, "More predictable than ever, with the worst MSPE ever," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 4, pages 5-30, December.
• Vlad TEODORESCU & Catalina-Ioana TOADER, 2024, "Using Machine Learning to Model Bankruptcy Risk in Listed Companies," PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ECONOMICS AND SOCIAL SCIENCES, Bucharest University of Economic Studies, Romania, volume 6, issue 1, pages 610-619, August.
• Ignace De Vos & Gerdie Everaert, 2025, "GLS Estimation of Local Projections: Trading Robustness for Efficiency," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium, Ghent University, Faculty of Economics and Business Administration, number 24/1095, Jun.
• Paramita Mukherjee & Dipankor Coondoo & Poulomi Lahiri, 2024, "Forecasting Hourly Spot Prices in Indian Electricity Market," Studies in Microeconomics, , volume 12, issue 3, pages 273-295, December, DOI: 10.1177/23210222221108019.
• Renáta K?e?ková & Daniela ?álková & Radka Procházková & Sergyi Yekimov, 2024, "Macroeconomics And Tourism Demand: Evaluating The Role Of Economic Indicators In The Czech Republic?S Hospitality Industry," Proceedings of Economics and Finance Conferences, International Institute of Social and Economic Sciences, number 14516470, Oct.
• Milen Arro-Cannarsa & Rolf Scheufele, 2024, "Nowcasting GDP: what are the gains from machine learning algorithms?," Working Papers, Swiss National Bank, number 2024-06.
• Jiawen Xu & Pierre Perron, 2024, "Forecasting in the presence of in-sample and out-of-sample breaks," Advanced Studies in Theoretical and Applied Econometrics, Springer, in: Subal C. Kumbhakar & Robin C. Sickles & Hung-Jen Wang, "Advances in Applied Econometrics", DOI: 10.1007/978-3-031-48385-1_20.
• Sami Ben Jabeur & Salma Mefteh-Wali & Jean-Laurent Viviani, 2024, "Forecasting gold price with the XGBoost algorithm and SHAP interaction values," Annals of Operations Research, Springer, volume 334, issue 1, pages 679-699, March, DOI: 10.1007/s10479-021-04187-w.
• Mehdi Mili & Jean‐Michel Sahut & Frédéric Teulon & Lubica Hikkerova, 2024, "A multidimensional Bayesian model to test the impact of investor sentiment on equity premium," Annals of Operations Research, Springer, volume 334, issue 1, pages 919-939, March, DOI: 10.1007/s10479-023-05165-0.
• Daniel Goller & Sandro Heiniger, 2024, "A general framework to quantify the event importance in multi-event contests," Annals of Operations Research, Springer, volume 341, issue 1, pages 71-93, October, DOI: 10.1007/s10479-023-05540-x.
• Patrick Oliver Schenk & Christoph Kern, 2024, "Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, volume 18, issue 2, pages 131-184, June, DOI: 10.1007/s11943-024-00344-2.
• Andrey Shternshis & Piero Mazzarisi, 2024, "Variance of entropy for testing time-varying regimes with an application to meme stocks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 47, issue 1, pages 215-258, June, DOI: 10.1007/s10203-023-00427-9.
• Ewelina Osowska & Piotr Wójcik, 2024, "Predicting the reaction of financial markets to Federal Open Market Committee post-meeting statements," Digital Finance, Springer, volume 6, issue 1, pages 145-175, March, DOI: 10.1007/s42521-023-00096-8.
• Ewelina Osowska & Piotr Wójcik, 2024, "Correction: Predicting the reaction of financial markets to Federal Open Market Committee post-meeting statements," Digital Finance, Springer, volume 6, issue 1, pages 177-177, March, DOI: 10.1007/s42521-023-00100-1.
• Pål Boug & Håvard Hungnes & Takamitsu Kurita, 2024, "The empirical modelling of house prices and debt revisited: a policy-oriented perspective," Empirical Economics, Springer, volume 66, issue 1, pages 369-404, January, DOI: 10.1007/s00181-023-02461-3.
• Zirui Guo & Yihan Li & Guangyan Jia, 2024, "Research on the effectiveness of the volatility–tail risk-managed portfolios in China’s market," Empirical Economics, Springer, volume 66, issue 3, pages 1191-1222, March, DOI: 10.1007/s00181-023-02493-9.
• Thomas F. P. Wiesen & Paul M. Beaumont, 2024, "A joint impulse response function for vector autoregressive models," Empirical Economics, Springer, volume 66, issue 4, pages 1553-1585, April, DOI: 10.1007/s00181-023-02496-6.
• Michal Franta & Jan Libich, 2024, "Holding the economy by the tail: analysis of short- and long-run macroeconomic risks," Empirical Economics, Springer, volume 66, issue 4, pages 1443-1489, April, DOI: 10.1007/s00181-023-02514-7.
• Zhikai Zhang & Yaojie Zhang & Yudong Wang, 2024, "Forecasting the equity premium using weighted regressions: Does the jump variation help?," Empirical Economics, Springer, volume 66, issue 5, pages 2049-2082, May, DOI: 10.1007/s00181-023-02521-8.
• Huawei Niu & Tianyu Liu, 2024, "Forecasting the volatility of European Union allowance futures with macroeconomic variables using the GJR-GARCH-MIDAS model," Empirical Economics, Springer, volume 67, issue 1, pages 75-96, July, DOI: 10.1007/s00181-023-02551-2.
• Robert Lehmann, 2024, "A real-time regional accounts database for Germany with applications to GDP revisions and nowcasting," Empirical Economics, Springer, volume 67, issue 2, pages 817-838, August, DOI: 10.1007/s00181-024-02566-3.
• Nima Nonejad, 2024, "Point forecasts of the price of crude oil: an attempt to “beat” the end-of-month random-walk benchmark," Empirical Economics, Springer, volume 67, issue 4, pages 1497-1539, October, DOI: 10.1007/s00181-024-02599-8.
• Fameliti Stavroula & Skintzi Vasiliki, 2024, "Macroeconomic attention and commodity market volatility," Empirical Economics, Springer, volume 67, issue 5, pages 1967-2007, November, DOI: 10.1007/s00181-024-02613-z.
• Yasmeen Bayaa & Mahmoud Qadan, 2024, "Interest rate uncertainty and the shape of the yield curve of U.S. treasury bonds," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, volume 14, issue 4, pages 981-1003, December, DOI: 10.1007/s40822-024-00278-8.
• Aktham Maghyereh & Salem Adel Ziadat, 2024, "Pattern and determinants of tail-risk transmission between cryptocurrency markets: new evidence from recent crisis episodes," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 10, issue 1, pages 1-34, December, DOI: 10.1186/s40854-023-00592-1.
• Xiaozhen Jing & Dezhong Xu & Bin Li & Tarlok Singh, 2024, "Does the U.S. extreme indicator matter in stock markets? International evidence," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 10, issue 1, pages 1-27, December, DOI: 10.1186/s40854-024-00610-w.
• Blanco-Oliver Antonio & Lara-Rubio Juan & Irimia-Diéguez Ana & Liébana-Cabanillas Francisco, 2024, "Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 10, issue 1, pages 1-30, December, DOI: 10.1186/s40854-024-00625-3.
• Malvina Marchese & María Dolores Martínez-Miranda & Jens Perch Nielsen & Michael Scholz, 2024, "Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 10, issue 1, pages 1-16, December, DOI: 10.1186/s40854-024-00657-9.
• Fred Espen Benth & Carlo Sgarra, 2024, "A Barndorff-Nielsen and Shephard model with leverage in Hilbert space for commodity forward markets," Finance and Stochastics, Springer, volume 28, issue 4, pages 1035-1076, October, DOI: 10.1007/s00780-024-00546-0.
• Ioannis Sitzimis, 2024, "Forecasting methods in Greek coastal shipping: The case of Southwest Crete," Future Business Journal, Springer, volume 10, issue 1, pages 1-16, December, DOI: 10.1186/s43093-024-00352-2.
• Leila Hedhili Zaier & Khaled Mokni & Ahdi Noomen Ajmi, 2024, "Causality relationships between climate policy uncertainty, renewable energy stocks, and oil prices: a mixed-frequency causality analysis," Future Business Journal, Springer, volume 10, issue 1, pages 1-11, December, DOI: 10.1186/s43093-024-00399-1.
• Sandra Dreher & Sebastian Eichfelder & Felix Noth, 2024, "Does IFRS information on tax loss carryforwards and negative performance improve predictions of earnings and cash flows?," Journal of Business Economics, Springer, volume 94, issue 1, pages 1-39, January, DOI: 10.1007/s11573-023-01147-7.
• J. Peter Leo Deepak & Yavana Rani Subramanian & J. Josephine Lalitha & K. Vidhya, 2024, "Optimum Level of Currency Reserves: Investigation and Forecasting of Indian Rupee Using ARIMA Model," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), volume 20, issue 1, pages 137-150, August, DOI: 10.1007/s41549-023-00091-3.
• Maximilian Boeck & Massimiliano Marcellino & Michael Pfarrhofer & Tommaso Tornese, 2024, "Predicting Tail-Risks for the Italian Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), volume 20, issue 3, pages 339-366, November, DOI: 10.1007/s41549-025-00106-1.
• Jörg Döpke & Tim Köhler & Lars Tegtmeier, 2024, "Are they worth it? – An evaluation of predictions for NBA ‘Fantasy Sports’," Journal of Economics and Finance, Springer;Academy of Economics and Finance, volume 48, issue 1, pages 142-165, March, DOI: 10.1007/s12197-023-09646-7.
• Isaac K. Ofori & Camara K. Obeng & Simplice A. Asongu, 2024, "What Really Drives Economic Growth in Sub-Saharan Africa? Evidence from the Lasso Regularization and Inferential Techniques," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), volume 15, issue 1, pages 144-179, March, DOI: 10.1007/s13132-022-01055-1.
• Dervis Kirikkaleli & Fusun Celebi Boz & Melike Torun, 2024, "Do Economic and Financial Stabilities Matter for Political Stability in Estonia?," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), volume 15, issue 3, pages 15202-15217, September, DOI: 10.1007/s13132-023-01662-6.
• Filip Lubinski, 2024, "Book review. J. Doyne Farmer, Making Sense of Chaos. A Better Economics for a Better World, Penguin (2024), pp. 364," Journal of Evolutionary Economics, Springer, volume 34, issue 4, pages 1013-1017, December, DOI: 10.1007/s00191-024-00876-4.
• Agnieszka Orwat-Acedańska, 2024, "Accuracy of small area mortality prediction methods: evidence from Poland," Journal of Population Research, Springer, volume 41, issue 1, pages 1-20, March, DOI: 10.1007/s12546-023-09326-7.
• Gavin Ooft & Sailesh Bhaghoe & Philip Hans Franses, 2024, "Forecasting Annual Inflation Using Weekly Money Supply," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), volume 22, issue 1, pages 25-43, March, DOI: 10.1007/s40953-023-00376-5.
• Kristian Jönsson, 2024, "Neighbor Weighting and Distance Metrics in Nearest Neighbor Nowcasting of Swedish GDP," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), volume 22, issue 4, pages 1077-1089, December, DOI: 10.1007/s40953-024-00400-2.
• Branco, Rafael R. & Rubesam, Alexandre & Zevallos, Mauricio, 2024, "Forecasting realized volatility: Does anything beat linear models?," Journal of Empirical Finance, Elsevier, volume 78, issue C, DOI: 10.1016/j.jempfin.2024.101524.
• Watanabe, Toshiaki & Nakajima, Jouchi, 2024, "High-frequency realized stochastic volatility model," Journal of Empirical Finance, Elsevier, volume 79, issue C, DOI: 10.1016/j.jempfin.2024.101559.
• Salisu, Afees A. & Demirer, Riza & Gupta, Rangan, 2024, "Technological shocks and stock market volatility over a century," Journal of Empirical Finance, Elsevier, volume 79, issue C, DOI: 10.1016/j.jempfin.2024.101561.
• Syuhada, Khreshna & Hakim, Arief & Suprijanto, Djoko, 2024, "Assessing systemic risk and connectedness among dirty and clean energy markets from the quantile and expectile perspectives," Energy Economics, Elsevier, volume 129, issue C, DOI: 10.1016/j.eneco.2023.107261.
• Salisu, Afees A. & Isah, Kazeem & Oloko, Tirimisiyu O., 2024, "Technology shocks and crude oil market connection: The role of climate change," Energy Economics, Elsevier, volume 130, issue C, DOI: 10.1016/j.eneco.2024.107325.
• Phella, Anthoulla & Gabriel, Vasco J. & Martins, Luis F., 2024, "Predicting tail risks and the evolution of temperatures," Energy Economics, Elsevier, volume 131, issue C, DOI: 10.1016/j.eneco.2023.107286.
• Wang, Yushi & Wu, Libo & Zhou, Yang, 2024, "Household's willingness to pay for renewable electricity: A meta-analysis," Energy Economics, Elsevier, volume 131, issue C, DOI: 10.1016/j.eneco.2024.107390.
• Gupta, Rangan & Nielsen, Joshua & Pierdzioch, Christian, 2024, "Stock market bubbles and the realized volatility of oil price returns," Energy Economics, Elsevier, volume 132, issue C, DOI: 10.1016/j.eneco.2024.107432.
• Bonaccolto, Giovanni & Caporin, Massimiliano & Iacopini, Matteo, 2024, "Extreme time-varying spillovers between high carbon emission stocks, green bond and crude oil: Comment," Energy Economics, Elsevier, volume 132, issue C, DOI: 10.1016/j.eneco.2024.107469.
• Haas, Christian & Budin, Constantin & d’Arcy, Anne, 2024, "How to select oil price prediction models — The effect of statistical and financial performance metrics and sentiment scores," Energy Economics, Elsevier, volume 133, issue C, DOI: 10.1016/j.eneco.2024.107466.
• Yang, Jinyu & Dong, Dayong & Liang, Chao & Cao, Yang, 2024, "Monetary policy uncertainty and the price bubbles in energy markets," Energy Economics, Elsevier, volume 133, issue C, DOI: 10.1016/j.eneco.2024.107503.
• Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie & Wang, Qunwei, 2024, "Forecasting carbon prices under diversified attention: A dynamic model averaging approach with common factors," Energy Economics, Elsevier, volume 133, issue C, DOI: 10.1016/j.eneco.2024.107537.
• Blazsek, Szabolcs & Escribano, Alvaro & Kristof, Erzsebet, 2024, "Global, Arctic, and Antarctic sea ice volume predictions using score-driven threshold climate models," Energy Economics, Elsevier, volume 134, issue C, DOI: 10.1016/j.eneco.2024.107591.
• Billio, Monica & Casarin, Roberto & Costola, Michele & Veggente, Veronica, 2024, "Learning from experts: Energy efficiency in residential buildings," Energy Economics, Elsevier, volume 136, issue C, DOI: 10.1016/j.eneco.2024.107650.
• Tan, Jinghua & Li, Zhixi & Zhang, Chuanhui & Shi, Long & Jiang, Yuansheng, 2024, "A multiscale time-series decomposition learning for crude oil price forecasting," Energy Economics, Elsevier, volume 136, issue C, DOI: 10.1016/j.eneco.2024.107733.
• Ouyang, Zisheng & Lu, Min & Ouyang, Zhongzhe & Zhou, Xuewei & Wang, Ren, 2024, "A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention," Energy Economics, Elsevier, volume 138, issue C, DOI: 10.1016/j.eneco.2024.107851.
• Tian, Guangning & Peng, Yuchao & Du, Huancheng & Meng, Yuhao, 2024, "Forecasting crude oil returns in different degrees of ambiguity: Why machine learn better?," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107867.
• Zhao, Yue & Brooks, Adria E. & Du, Xiaodong, 2024, "Electricity market resilience in the face of Hurricane Harvey: A network-oriented approach," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107879.
• Sánchez-García, Javier & Mattera, Raffaele & Cruz-Rambaud, Salvador & Cerqueti, Roy, 2024, "Measuring financial stability in the presence of energy shocks," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107922.
• Fields, Micah & Lindequist, David, 2024, "Global spillovers of US climate policy risk: Evidence from EU carbon emissions futures," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107931.
• Lipiecki, Arkadiusz & Uniejewski, Bartosz & Weron, Rafał, 2024, "Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107934.
• Yang, Kun & Sun, Yuying & Hong, Yongmiao & Wang, Shouyang, 2024, "Forecasting interval carbon price through a multi-scale interval-valued decomposition ensemble approach," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107952.
• Zhao, Yuan & Gong, Xue & Zhang, Weiguo & Xu, Weijun, 2024, "Forecasting carbon futures returns using feature selection and Markov chain with sample distribution," Energy Economics, Elsevier, volume 140, issue C, DOI: 10.1016/j.eneco.2024.107962.
• Kim, Sunjin & Park, Daehyeon & Ryu, Doojin, 2024, "Potential sanctions on the Northeast Asia supergrid: A network theory perspective," Energy, Elsevier, volume 302, issue C, DOI: 10.1016/j.energy.2024.131655.
• Wen, Danyan & Wang, Huihui & Wang, Yudong & Xiao, Jihong, 2024, "Crude oil futures and the short-term price predictability of petroleum products," Energy, Elsevier, volume 307, issue C, DOI: 10.1016/j.energy.2024.132750.
• He, Mengxi & Zhang, Zhikai & Zhang, Yaojie, 2024, "Forecasting crude oil prices with global ocean temperatures," Energy, Elsevier, volume 311, issue C, DOI: 10.1016/j.energy.2024.133341.
• Hong, Yun & Yao, Youfu, 2024, "Can comment letters impact excess perks? Evidence from China," International Review of Financial Analysis, Elsevier, volume 91, issue C, DOI: 10.1016/j.irfa.2023.102943.
• Zhang, Jiaming & Xiang, Yitian & Zou, Yang & Guo, Songlin, 2024, "Volatility forecasting of Chinese energy market: Which uncertainty have better performance?," International Review of Financial Analysis, Elsevier, volume 91, issue C, DOI: 10.1016/j.irfa.2023.102952.
• Bouazizi, Tarek & Guesmi, Khaled & Galariotis, Emilios & Vigne, Samuel A., 2024, "Crude oil prices in times of crisis: The role of Covid-19 and historical events," International Review of Financial Analysis, Elsevier, volume 91, issue C, DOI: 10.1016/j.irfa.2023.102955.
• Teng, Huei-Wen & Kang, Ming-Hsuan & Lee, I-Han & Bai, Le-Chi, 2024, "Bridging accuracy and interpretability: A rescaled cluster-then-predict approach for enhanced credit scoring," International Review of Financial Analysis, Elsevier, volume 91, issue C, DOI: 10.1016/j.irfa.2023.103005.
• Wang, Yuejing & Ye, Wuyi & Jiang, Ying & Liu, Xiaoquan, 2024, "Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model," International Review of Financial Analysis, Elsevier, volume 92, issue C, DOI: 10.1016/j.irfa.2024.103094.
• Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024, "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, volume 92, issue C, DOI: 10.1016/j.irfa.2024.103102.
• Ghosh, Indranil & Alfaro-Cortés, Esteban & Gámez, Matías & García-Rubio, Noelia, 2024, "Reflections of public perception of Russia-Ukraine conflict and Metaverse on the financial outlook of Metaverse coins: Fresh evidence from Reddit sentiment analysis," International Review of Financial Analysis, Elsevier, volume 93, issue C, DOI: 10.1016/j.irfa.2024.103215.
• Heger, Julia & Min, Aleksey & Zagst, Rudi, 2024, "Analyzing credit spread changes using explainable artificial intelligence," International Review of Financial Analysis, Elsevier, volume 94, issue C, DOI: 10.1016/j.irfa.2024.103315.
• Huang, Yujun, 2024, "Do ESG ETFs provide downside risk protection during Covid-19? Evidence from forecast combination models," International Review of Financial Analysis, Elsevier, volume 94, issue C, DOI: 10.1016/j.irfa.2024.103320.
• Bouazizi, Tarek & Abid, Ilyes & Guesmi, Khaled & Makrychoriti, Panagiota, 2024, "Evolving energies: Analyzing stability amidst recent challenges in the natural gas market," International Review of Financial Analysis, Elsevier, volume 95, issue PA, DOI: 10.1016/j.irfa.2024.103346.
• Moffo, Ahmadou Mustapha Fonton, 2024, "A machine learning approach in stress testing US bank holding companies," International Review of Financial Analysis, Elsevier, volume 95, issue PC, DOI: 10.1016/j.irfa.2024.103476.
• Ben Hamida, Amal & de Peretti, Christian & Belkacem, Lotfi, 2024, "The link between abnormal numbers and price movements of financial securities: How does Benford’s law predict stock returns?," International Review of Financial Analysis, Elsevier, volume 95, issue PC, DOI: 10.1016/j.irfa.2024.103517.
• Yang, Ni & Fernandez-Perez, Adrian & Indriawan, Ivan, 2024, "Spillover between investor sentiment and volatility: The role of social media," International Review of Financial Analysis, Elsevier, volume 96, issue PA, DOI: 10.1016/j.irfa.2024.103643.
• Zhang, Xiaoyun & Guo, Qiang, 2024, "How useful are energy-related uncertainty for oil price volatility forecasting?," Finance Research Letters, Elsevier, volume 60, issue C, DOI: 10.1016/j.frl.2023.104953.
• Baruník, Jozef & Hanus, Luboš, 2024, "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, volume 61, issue C, DOI: 10.1016/j.frl.2024.105003.
• Liu, Dinggao & Chen, Kaijie & Cai, Yi & Tang, Zhenpeng, 2024, "Interpretable EU ETS Phase 4 prices forecasting based on deep generative data augmentation approach," Finance Research Letters, Elsevier, volume 61, issue C, DOI: 10.1016/j.frl.2024.105038.
• Tang, Wenjin & Bu, Hui & Zuo, Yuan & Wu, Junjie, 2024, "Unlocking the power of the topic content in news headlines: BERTopic for predicting Chinese corporate bond defaults," Finance Research Letters, Elsevier, volume 62, issue PA, DOI: 10.1016/j.frl.2024.105062.
• Kirtac, Kemal & Germano, Guido, 2024, "Sentiment trading with large language models," Finance Research Letters, Elsevier, volume 62, issue PB, DOI: 10.1016/j.frl.2024.105227.
• Li, Wei & Zhang, Junchao & Cao, Xiangye & Han, Wei, 2024, "Is the prediction of precious metal market volatility influenced by internet searches regarding uncertainty?," Finance Research Letters, Elsevier, volume 62, issue PB, DOI: 10.1016/j.frl.2024.105269.
• Ma, Feng & Lyu, Zhichong & Li, Haibo, 2024, "Can ChatGPT predict Chinese equity premiums?," Finance Research Letters, Elsevier, volume 65, issue C, DOI: 10.1016/j.frl.2024.105631.
• Chen, Zhenlong & Liu, Junjie & Hao, Xiaozhen, 2024, "Can the ‘good-bad’ volatility and the leverage effect improve the prediction of cryptocurrency volatility?—Evidence from SHARV-MGJR model," Finance Research Letters, Elsevier, volume 67, issue PA, DOI: 10.1016/j.frl.2024.105757.
• Salisu, Afees A. & Ogbonna, Ahamuefula E. & Gupta, Rangan & Ji, Qiang, 2024, "Energy market uncertainties and exchange rate volatility: A GARCH-MIDAS approach," Finance Research Letters, Elsevier, volume 67, issue PB, DOI: 10.1016/j.frl.2024.105847.
• Göncü, Ahmet & Kuzubaş, Tolga U. & Saltoğlu, Burak, 2024, "Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction," Finance Research Letters, Elsevier, volume 67, issue PB, DOI: 10.1016/j.frl.2024.105874.
• Nguyen, Hien Thi & Nguyen, Hoang & Tran, Minh-Ngoc, 2024, "Deep learning enhanced volatility modeling with covariates," Finance Research Letters, Elsevier, volume 69, issue PB, DOI: 10.1016/j.frl.2024.106145.
• Liu, Wei-han & Xu, Xingfu, 2024, "Forecasting crude oil price: A deep forest ensemble approach," Finance Research Letters, Elsevier, volume 69, issue PB, DOI: 10.1016/j.frl.2024.106153.
• Bouri, Elie & Gupta, Rangan & Pierdzioch, Christian & Polat, Onur, 2024, "Forecasting U.S. recessions using over 150 years of data: Stock-market moments versus oil-market moments," Finance Research Letters, Elsevier, volume 69, issue PB, DOI: 10.1016/j.frl.2024.106179.
• Wang, Qi & Zhang, Li, 2024, "Are natural resource volatility curses or blessings for economic performance? Stories of resource-rich regions," Finance Research Letters, Elsevier, volume 69, issue PB, DOI: 10.1016/j.frl.2024.106240.
• Kim, Hyeongwoo & Son, Jisoo, 2024, "What charge-off rates are predictable by macroeconomic latent factors?," Journal of Financial Stability, Elsevier, volume 74, issue C, DOI: 10.1016/j.jfs.2024.101301.
• Biswas, Rita & Loungani, Prakash & Liang, Zhongwen & Michaelides, Michael, 2024, "Linkages between financial and macroeconomic indicators in emerging markets and developing economies," Global Finance Journal, Elsevier, volume 62, issue C, DOI: 10.1016/j.gfj.2024.101007.
• Steinmetz, Julia & Jentsch, Carsten, 2024, "Bootstrap consistency for the Mack bootstrap," Insurance: Mathematics and Economics, Elsevier, volume 115, issue C, pages 83-121, DOI: 10.1016/j.insmatheco.2024.01.001.
• Fava, Santino Del & Gupta, Rangan & Pierdzioch, Christian & Rognone, Lavinia, 2024, "Forecasting international financial stress: The role of climate risks," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 92, issue C, DOI: 10.1016/j.intfin.2024.101975.
• Huang, Zih-Chun & Sangiorgi, Ivan & Urquhart, Andrew, 2024, "Forecasting Bitcoin volatility using machine learning techniques," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 97, issue C, DOI: 10.1016/j.intfin.2024.102064.
• Alexandridis, Antonios K. & Panopoulou, Ekaterini & Souropanis, Ioannis, 2024, "Forecasting exchange rate volatility: An amalgamation approach," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 97, issue C, DOI: 10.1016/j.intfin.2024.102067.
• Iseringhausen, Martin, 2024, "A time-varying skewness model for Growth-at-Risk," International Journal of Forecasting, Elsevier, volume 40, issue 1, pages 229-246, DOI: 10.1016/j.ijforecast.2023.02.006.
• Segnon, Mawuli & Gupta, Rangan & Wilfling, Bernd, 2024, "Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks," International Journal of Forecasting, Elsevier, volume 40, issue 1, pages 29-43, DOI: 10.1016/j.ijforecast.2022.11.007.
• Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024, "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, volume 40, issue 2, pages 430-456, DOI: 10.1016/j.ijforecast.2023.10.010.
• Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2024, "Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates," International Journal of Forecasting, Elsevier, volume 40, issue 2, pages 626-640, DOI: 10.1016/j.ijforecast.2022.04.002.
• Cascaldi-Garcia, Danilo & Ferreira, Thiago R.T. & Giannone, Domenico & Modugno, Michele, 2024, "Back to the present: Learning about the euro area through a now-casting model," International Journal of Forecasting, Elsevier, volume 40, issue 2, pages 661-686, DOI: 10.1016/j.ijforecast.2023.04.005.
• Poutré, Cédric & Dionne, Georges & Yergeau, Gabriel, 2024, "The profitability of lead–lag arbitrage at high frequency," International Journal of Forecasting, Elsevier, volume 40, issue 3, pages 1002-1021, DOI: 10.1016/j.ijforecast.2023.09.001.
• Gonzalo, Jesús & Pitarakis, Jean-Yves, 2024, "Out-of-sample predictability in predictive regressions with many predictor candidates," International Journal of Forecasting, Elsevier, volume 40, issue 3, pages 1166-1178, DOI: 10.1016/j.ijforecast.2023.10.005.
• Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024, "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, volume 40, issue 4, pages 1521-1538, DOI: 10.1016/j.ijforecast.2024.01.001.
• Berrisch, Jonathan & Ziel, Florian, 2024, "Multivariate probabilistic CRPS learning with an application to day-ahead electricity prices," International Journal of Forecasting, Elsevier, volume 40, issue 4, pages 1568-1586, DOI: 10.1016/j.ijforecast.2024.01.005.
• Gibbs, Christopher G. & Vasnev, Andrey L., 2024, "Conditionally optimal weights and forward-looking approaches to combining forecasts," International Journal of Forecasting, Elsevier, volume 40, issue 4, pages 1734-1751, DOI: 10.1016/j.ijforecast.2024.03.002.
• Fritzsch, Simon & Timphus, Maike & Weiß, Gregor, 2024, "Marginals versus copulas: Which account for more model risk in multivariate risk forecasting?," Journal of Banking & Finance, Elsevier, volume 158, issue C, DOI: 10.1016/j.jbankfin.2023.107035.
• Cheng, Hang & Guo, Hui & Shi, Yongdong, 2024, "Multifactor conditional equity premium model: Evidence from China's stock market," Journal of Banking & Finance, Elsevier, volume 161, issue C, DOI: 10.1016/j.jbankfin.2024.107117.
• Cao, Cong, 2024, "How to better predict the effect of urban traffic and weather on air pollution? Norwegian evidence from machine learning approaches," Journal of Economic Behavior & Organization, Elsevier, volume 221, issue C, pages 544-569, DOI: 10.1016/j.jebo.2024.03.018.
• Zhang, Li & Liang, Chao & Huynh, Luu Duc Toan & Wang, Lu & Damette, Olivier, 2024, "Measuring the impact of climate risk on renewable energy stock volatility: A case study of G20 economies," Journal of Economic Behavior & Organization, Elsevier, volume 223, issue C, pages 168-184, DOI: 10.1016/j.jebo.2024.05.005.
• Clements, Michael P., 2024, "Survey expectations and adjustments for multiple testing," Journal of Economic Behavior & Organization, Elsevier, volume 224, issue C, pages 338-354, DOI: 10.1016/j.jebo.2024.06.009.
• Qiu, Yajie & Deschamps, Bruno & Liu, Xiaoquan, 2024, "Uncertainty and macroeconomic forecasts: Evidence from survey data," Journal of Economic Behavior & Organization, Elsevier, volume 224, issue C, pages 463-480, DOI: 10.1016/j.jebo.2024.06.008.
• Xiao, Wei, 2024, "Initial anchors and limited information in learning-to-forecast experiments," Journal of Economic Behavior & Organization, Elsevier, volume 225, issue C, pages 192-227, DOI: 10.1016/j.jebo.2024.06.038.
• Chen, Heng & Li, Xu & Pei, Guangyu & Xin, Qian, 2024, "Heterogeneous overreaction in expectation formation: Evidence and theory," Journal of Economic Theory, Elsevier, volume 218, issue C, DOI: 10.1016/j.jet.2024.105839.
• Hsu, Po-Hsuan & Taylor, Mark P. & Wang, Zigan & Li, Yan, 2024, "The out-of-sample performance of carry trades," Journal of International Money and Finance, Elsevier, volume 143, issue C, DOI: 10.1016/j.jimonfin.2024.103042.
• Hecq, Alain & Issler, João Victor & Voisin, Elisa, 2024, "A short term credibility index for central banks under inflation targeting: An application to Brazil," Journal of International Money and Finance, Elsevier, volume 143, issue C, DOI: 10.1016/j.jimonfin.2024.103057.
• Bei, Zeyun & Lin, Juan & Zhou, Yinggang, 2024, "No safe haven, only diversification and contagion — Intraday evidence around the COVID-19 pandemic," Journal of International Money and Finance, Elsevier, volume 143, issue C, DOI: 10.1016/j.jimonfin.2024.103069.
• Conlon, Thomas & Cotter, John & Eyiah-Donkor, Emmanuel, 2024, "Forecasting the price of oil: A cautionary note," Journal of Commodity Markets, Elsevier, volume 33, issue C, DOI: 10.1016/j.jcomm.2023.100378.
• Lazar, Emese & Pan, Jingqi & Wang, Shixuan, 2024, "On the estimation of Value-at-Risk and Expected Shortfall at extreme levels," Journal of Commodity Markets, Elsevier, volume 34, issue C, DOI: 10.1016/j.jcomm.2024.100391.
• Ma, Tian & Li, Ganghui & Zhang, Huajing, 2024, "Stock return predictability using economic narrative: Evidence from energy sectors," Journal of Commodity Markets, Elsevier, volume 35, issue C, DOI: 10.1016/j.jcomm.2024.100418.
• Li, Kaixin & Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie, 2024, "Forecasting crude oil returns with oil-related industry ESG indices," Journal of Commodity Markets, Elsevier, volume 36, issue C, DOI: 10.1016/j.jcomm.2024.100444.
• Cavicchioli, Maddalena, 2024, "A matrix unified framework for deriving various impulse responses in Markov switching VAR: Evidence from oil and gas markets," The Journal of Economic Asymmetries, Elsevier, volume 29, issue C, DOI: 10.1016/j.jeca.2023.e00349.
• Alves, Renan Santos & Palma, Andreza A., 2024, "The effectiveness of fiscal policy in Brazil through the MIDAS Lens," Journal of Policy Modeling, Elsevier, volume 46, issue 1, pages 113-128, DOI: 10.1016/j.jpolmod.2023.10.004.
• Sen, Abhibasu & Dutta Choudhury, Karabi, 2024, "Forecasting the Crude Oil prices for last four decades using deep learning approach," Resources Policy, Elsevier, volume 88, issue C, DOI: 10.1016/j.resourpol.2023.104438.
• Simionescu, Mihaela & Cifuentes-Faura, Javier, 2024, "The digital economy and energy poverty in Central and Eastern Europe," Utilities Policy, Elsevier, volume 91, issue C, DOI: 10.1016/j.jup.2024.101841.
• Bolivar, Osmar, 2024, "GDP nowcasting: A machine learning and remote sensing data-based approach for Bolivia," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 5, issue 3, DOI: 10.1016/j.latcb.2024.100126.
• Durand, Luigi & Fornero, Jorge Alberto, 2024, "Estimating the output gap in times of COVID-19," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 5, issue 4, DOI: 10.1016/j.latcb.2024.100129.
• Han, Zhao, 2024, "Asymmetric information and misaligned inflation expectations," Journal of Monetary Economics, Elsevier, volume 143, issue C, DOI: 10.1016/j.jmoneco.2023.10.010.
• Gazzani, Andrea & Venditti, Fabrizio & Veronese, Giovanni, 2024, "Oil price shocks in real time," Journal of Monetary Economics, Elsevier, volume 144, issue C, DOI: 10.1016/j.jmoneco.2023.12.005.
• López-Salido, David & Loria, Francesca, 2024, "Inflation at risk," Journal of Monetary Economics, Elsevier, volume 145, issue S, DOI: 10.1016/j.jmoneco.2024.103570.
• Tong, Bin & Diao, Xundi & Li, Xiaoping, 2024, "Forecasting VaRs via hybrid EVT with normal and non-normal filters: A comparative analysis from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, volume 83, issue C, DOI: 10.1016/j.pacfin.2024.102271.
• Pan, Ging-Ginq & Shiu, Yung-Ming & Wu, Tu-Cheng, 2024, "Extrapolation and option-implied kurtosis in volatility forecasting," Pacific-Basin Finance Journal, Elsevier, volume 84, issue C, DOI: 10.1016/j.pacfin.2024.102286.
• Lu, Yao & Zhao, Zhihui & Tian, Yuan & Zhan, Minghua, 2024, "How does the economic structure break change the forecast effect of money and credit on output? Evidence based on machine learning algorithms," Pacific-Basin Finance Journal, Elsevier, volume 84, issue C, DOI: 10.1016/j.pacfin.2024.102325.
• Huang, Xiaowei & He, Chenyu & Zhang, Man, 2024, "Economic policy uncertainty and capital flows' tail risk in China," Pacific-Basin Finance Journal, Elsevier, volume 85, issue C, DOI: 10.1016/j.pacfin.2024.102370.
• Huynh, Tran & Uebelmesser, Silke, 2024, "Early warning models for systemic banking crises: Can political indicators improve prediction?," European Journal of Political Economy, Elsevier, volume 81, issue C, DOI: 10.1016/j.ejpoleco.2023.102484.
• Christensen, Peter & Francisco, Paul & Myers, Erica & Shao, Hansen & Souza, Mateus, 2024, "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, Elsevier, volume 234, issue C, DOI: 10.1016/j.jpubeco.2024.105098.
• Salisu, Afees A. & Ogbonna, Ahamuefula E. & Gupta, Rangan & Bouri, Elie, 2024, "Energy-related uncertainty and international stock market volatility," The Quarterly Review of Economics and Finance, Elsevier, volume 95, issue C, pages 280-293, DOI: 10.1016/j.qref.2024.04.005.
• Wang, Lu & Wang, Xing & Liang, Chao, 2024, "Natural gas volatility prediction via a novel combination of GARCH-MIDAS and one-class SVM," The Quarterly Review of Economics and Finance, Elsevier, volume 98, issue C, DOI: 10.1016/j.qref.2024.101927.
• du Plessis, Emile, 2024, "Reading between the lines: Quantitative text analysis of banking crises," Research in Economics, Elsevier, volume 78, issue 4, DOI: 10.1016/j.rie.2024.101000.
• Mati, Sagiru & Baita, Abubakar Jamilu & Ismael, Goran Yousif & Abdullahi, Salisu Garba & Samour, Ahmed & Ozsahin, Dilber Uzun, 2024, "Enhancing CO2 emissions prediction in Africa: A novel approach integrating enviroeconomic factors and nature-inspired neural network in the presence of unit root," Renewable Energy, Elsevier, volume 237, issue PA, DOI: 10.1016/j.renene.2024.121561.
• Westphal, Igor, 2024, "The effects of reducing renewable power intermittency through portfolio diversification," Renewable and Sustainable Energy Reviews, Elsevier, volume 197, issue C, DOI: 10.1016/j.rser.2024.114415.
• Manner, Hans & Rodríguez, Gabriel & Stöckler, Florian, 2024, "A changepoint analysis of exchange rate and commodity price risks for Latin American stock markets," International Review of Economics & Finance, Elsevier, volume 89, issue PA, pages 1385-1403, DOI: 10.1016/j.iref.2023.08.021.
• Guo, Yanfeng & Zhao, Huanyu, 2024, "Volatility spillovers between oil and coal prices and its implications for energy portfolio management in China," International Review of Economics & Finance, Elsevier, volume 89, issue PB, pages 446-457, DOI: 10.1016/j.iref.2023.10.004.
• Luo, Tao & Sun, Huaping & Zhang, Lixia & Bai, Jiancheng, 2024, "Do the dynamics of macroeconomic attention drive the yen/dollar exchange market volatility?," International Review of Economics & Finance, Elsevier, volume 89, issue PB, pages 597-611, DOI: 10.1016/j.iref.2023.09.012.
• Li, Xiaodan & Gong, Xue & Ge, Futing & Huang, Jingjing, 2024, "Forecasting stock volatility using pseudo-out-of-sample information," International Review of Economics & Finance, Elsevier, volume 90, issue C, pages 123-135, DOI: 10.1016/j.iref.2023.11.014.
• Li, Zhao-Chen & Xie, Chi & Wang, Gang-Jin & Zhu, You & Zeng, Zhi-Jian & Gong, Jue, 2024, "Forecasting global stock market volatilities: A shrinkage heterogeneous autoregressive (HAR) model with a large cross-market predictor set," International Review of Economics & Finance, Elsevier, volume 93, issue PB, pages 673-711, DOI: 10.1016/j.iref.2024.05.008.
• Peng, Lijuan & Liang, Chao & Yang, Baoying & Wang, Lu, 2024, "Crude oil volatility forecasting: Insights from a novel time-varying parameter GARCH-MIDAS model," International Review of Economics & Finance, Elsevier, volume 94, issue C, DOI: 10.1016/j.iref.2024.103413.
• Patra, Saswat, 2024, "An empirical analysis of the volume-volatility nexus in crude oil markets under structural breaks: Implications for forecasting," International Review of Economics & Finance, Elsevier, volume 94, issue C, DOI: 10.1016/j.iref.2024.103434.
• He, Mengxi & Wen, Danyan & Xing, Lu & Zhang, Yaojie, 2024, "Industry volatility concentration and the predictability of aggregate stock market volatility," International Review of Economics & Finance, Elsevier, volume 95, issue C, DOI: 10.1016/j.iref.2024.103488.