Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2023

• Yoonseok Lee & Donggyu Sul, 2023, "Depth-weighted Forecast Combination: Application to COVID-19 Cases," Advances in Econometrics, Emerald Group Publishing Limited, "Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications", DOI: 10.1108/S0731-90532023000045B011.
• Valeriia Baklanova & Aleksei Kurkin & Tamara Teplova, 2023, "Investor sentiment and the NFT hype index: to buy or not to buy?," China Finance Review International, Emerald Group Publishing Limited, volume 14, issue 3, pages 522-548, December, DOI: 10.1108/CFRI-06-2023-0175.
• Aivars Spilbergs & Diego Norena-Chavez & Eleftherios Thalassinos & Graţiela Georgiana Noja & Mirela Cristea, 2023, "Challenges to Credit Risk Management in the Context of Growing Macroeconomic Instability in the Baltic States Caused by COVID-19," Contemporary Studies in Economic and Financial Analysis, Emerald Group Publishing Limited, "Digital Transformation, Strategic Resilience, Cyber Security and Risk Management", DOI: 10.1108/S1569-37592023000111A006.
• Soumya Bhadury & Satadru Das & Saurabh Ghosh & Pawan Gopalakrishnan, 2023, "Impact of crude prices shock on GDP growth: using a linear, nonlinear and extreme value framework," Indian Growth and Development Review, Emerald Group Publishing Limited, volume 16, issue 1, pages 91-103, March, DOI: 10.1108/IGDR-05-2022-0065.
• Özgür İcan & Taha Buğra Çelik, 2023, "Weak-form market efficiency and corruption: a cross-country comparative analysis," Journal of Capital Markets Studies, Emerald Group Publishing Limited, volume 7, issue 1, pages 72-90, April, DOI: 10.1108/JCMS-12-2022-0046.
• Elias Shohei Kamimura & Anderson Rogério Faia Pinto & Marcelo Seido Nagano, 2023, "A recent review on optimisation methods applied to credit scoring models," Journal of Economics, Finance and Administrative Science, Emerald Group Publishing Limited, volume 28, issue 56, pages 352-371, June, DOI: 10.1108/JEFAS-09-2021-0193.
• Afees Salisu & Douglason Godwin Omotor, 2023, "Forecasting expenditure components in Nigeria," Journal of Economic Studies, Emerald Group Publishing Limited, volume 51, issue 4, pages 783-807, September, DOI: 10.1108/JES-02-2023-0087.
• Hardik Marfatia, 2023, "The financial market's ability to forecast economic growth: information from sectoral movements," Journal of Economic Studies, Emerald Group Publishing Limited, volume 50, issue 7, pages 1467-1484, January, DOI: 10.1108/JES-08-2022-0466.
• Mehdi Mili & Ahmed Bouteska, 2023, "Forecasting nonlinear dependency between cryptocurrencies and foreign exchange markets using dynamic copula: evidence from GAS models," Journal of Risk Finance, Emerald Group Publishing Limited, volume 24, issue 4, pages 464-482, May, DOI: 10.1108/JRF-04-2022-0074.
• Abhigayan Adhikary & Manoranjan Pal, 2023, "Long Run Predictions Using Gompertz Curves - A State Wise Analysis of COVID-19 Infections in India," International Econometric Review (IER), Econometric Research Association, volume 15, issue 2, pages 45-58, September.
• Mariusz Pyra, 2023, "A Scenario Analysis for the Decarbonisation Process in Poland’s Road Transport Sector," European Research Studies Journal, European Research Studies Journal, volume 0, issue 1, pages 411-432.
• Pablo Pincheira-Brown & Nicolás Hardy & Cristobal Henrriquez & Ignacio Tapia & Andrea Bentancor, 2023, "Forecasting Base Metal Prices with an International Stock Index," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, volume 73, issue 3, pages 277-302, October.
• Gary Koop & Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon & Ping Wu, 2023, "Incorporating Short Data into Large Mixed-Frequency VARs for Regional Nowcasting," Working Papers, Federal Reserve Bank of Cleveland, number 23-09, May, DOI: 10.26509/frbc-wp-202309.
• Kurt Graden Lunsford & Kenneth D. West, 2023, "Random Walk Forecasts of Stationary Processes Have Low Bias," Working Papers, Federal Reserve Bank of Cleveland, number 23-18, Aug, DOI: 10.26509/frbc-wp-202318.
• Tony Chernis & Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023, "Predictive Density Combination Using a Tree-Based Synthesis Function," Working Papers, Federal Reserve Bank of Cleveland, number 23-30, Nov, DOI: 10.26509/frbc-wp-202330.
• James Mitchell & Saeed Zaman, 2023, "The Distributional Predictive Content of Measures of Inflation Expectations," Working Papers, Federal Reserve Bank of Cleveland, number 23-31, Nov, DOI: 10.26509/frbc-wp-202331.
• Todd E. Clark & Matthew V. Gordon & Saeed Zaman, 2023, "Forecasting Core Inflation and Its Goods, Housing, and Supercore Components," Working Papers, Federal Reserve Bank of Cleveland, number 23-34, Dec, DOI: 10.26509/frbc-wp-202334.
• Bennett Schmanski & Chiara Scotti & Clara Vega, 2023, "Fed Communication, News, Twitter, and Echo Chambers," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2023-036, May, DOI: 10.17016/FEDS.2023.036.
• Kenneth Eva & Fabian Winkler, 2023, "A Comprehensive Empirical Evaluation of Biases in Expectation Formation," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2023-042, Jun, DOI: 10.17016/FEDS.2023.042.
• Mary Chen & Matthew DeHaven & Isabel Kitschelt & Seung Jung Lee & Martin Sicilian, 2023, "Identifying Financial Crises Using Machine Learning on Textual Data," International Finance Discussion Papers, Board of Governors of the Federal Reserve System (U.S.), number 1374, Mar, DOI: 10.17016/IFDP.2023.1374.
• Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2023, "Lessons from Nowcasting GDP across the World," International Finance Discussion Papers, Board of Governors of the Federal Reserve System (U.S.), number 1385, Dec, DOI: 10.17016/IFDP.2023.1385.
• Raffaella Giacomini & Sokbae Lee & Silvia Sarpietro, 2023, "A Robust Method for Microforecasting and Estimation of Random Effects," Working Paper Series, Federal Reserve Bank of Chicago, number WP 2023-26, Aug, DOI: 10.21033/wp-2023-26.
• Irene Botosaru & Raffaella Giacomini & Martin Weidner, 2023, "Forecasted Treatment Effects," Working Paper Series, Federal Reserve Bank of Chicago, number WP 2023-32, Aug, DOI: 10.21033/wp-2023-32.
• Maximilian Ahrens & Deniz Erdemlioglu & Michael McMahon & Christopher J. Neely & Xiye Yang, 2023, "Mind Your Language: Market Responses to Central Bank Speeches," Working Papers, Federal Reserve Bank of St. Louis, number 2023-013, May, revised 28 Sep 2024, DOI: 10.20955/wp.2023.013.
• Miguel Faria-e-Castro & Fernando Leibovici, 2023, "Artificial Intelligence and Inflation Forecasts," Working Papers, Federal Reserve Bank of St. Louis, number 2023-015, Jul, revised 26 Feb 2024, DOI: 10.20955/wp.2023.015.
• Aaron Amburgey & Michael W. McCracken, 2023, "Growth-at-Risk is Investment-at-Risk," Working Papers, Federal Reserve Bank of St. Louis, number 2023-020, Aug, revised 14 Aug 2025, DOI: 10.20955/wp.2023.020.
• Silvia Goncalves & Michael W. McCracken & Yongxu Yao, 2023, "Bootstrapping out-of-sample predictability tests with real-time data," Working Papers, Federal Reserve Bank of St. Louis, number 2023-029, Nov, revised 03 Sep 2024, DOI: 10.20955/wp.2023.029.
• Katie Baker & Martín Almuzara & Hannah O’Keeffe & Argia M. Sbordone, 2023, "Reintroducing the New York Fed Staff Nowcast," Liberty Street Economics, Federal Reserve Bank of New York, number 20230908, Sep.
• Thorsten Drautzburg, 2023, "A Structural Approach to Combining External and DSGE Model Forecasts," Working Papers, Federal Reserve Bank of Philadelphia, number 23-10, Jun, DOI: 10.21799/frbp.wp.2023.10.
• Ludovic Dobbelaere & Igor Lebrun, 2023, "Working Paper 07-23 - Évaluation de la précision des prévisions à court terme et des perspectives à moyen terme du BFP. Une mise à jour du Working Paper 05-20
  [Working Paper 07-23 - Evaluatie va," Working Papers, Federal Planning Bureau, Belgium, number 202307, Dec.
• Boris I. Alekhin, 2023, "Interregional Differences in Inflation through the Prism of Ackley’s Theory," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 8-25, February, DOI: 10.31107/2075-1990-2023-1-8-25.
• Tsukhlo Sergey, 2023, "Russian industry in 2022," Published Papers, Gaidar Institute for Economic Policy, number ppaper-2023-1281, revised 2023.
• Barinova Vera & Zemtsov Stepan & Demidova Ksenia & Levakov P., 2023, "Business activity of small and medium-sized enterprises in Russia in the context of sanctions," Published Papers, Gaidar Institute for Economic Policy, number ppaper-2023-1285, revised 2023.
• Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023, "Analysis of Opportunities to Improve the Quality of Natural Resource Price by Combining Forecasts Resulting from Methods Based on Regression Estimates of Weights
  [Анализ Возможностей Улучшения Каче," Russian Economic Development, Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
• Anastasia D. Petaykina, 2023, "Predicting Changes in Household Consumption Using Neural Networks
  [Прогнозирование Изменений Потребления Домашних Хозяйств С Использованием Нейронных Сетей]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 7, pages 42-53, July.
• Konstantin S. Rybak, 2023, "Evaluating the Role of Global Factors in GDP Nowcasting
  [Анализ Важности Глобальных Факторов Для Наукастинга Ввп]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 12, pages 18-23, December.
• Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023, "Анализ Возможностей Улучшения Качества Прогнозов Цен На Природные Ресурсы Методами Комбинирования На Основе Регрессионных Оценок Весов," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
• Anastasia D. Petaykina, 2023, "Прогнозирование Изменений Потребления Домашних Хозяйств С Использованием Нейронных Сетей," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 7, pages 42-53, July.
• Konstantin S. Rybak, 2023, "Анализ Важности Глобальных Факторов Для Наукастинга Ввп," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 12, pages 18-23, December.
• Andrey Polbin & Andrei Shumilov, 2023, "Forecasting inflation in Russia using a TVP model with Bayesian shrinkage," Working Papers, Gaidar Institute for Economic Policy, number wpaper-2023-1462, revised 2023.
• Dean Fantazzini & Yufeng Xiao, 2023, "Detecting Pump-and-Dumps with Crypto-Assets: Dealing with Imbalanced Datasets and Insiders’ Anticipated Purchases," Econometrics, MDPI, volume 11, issue 3, pages 1-73, August.
• James T. E. Chapman & Ajit Desai, 2023, "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, volume 5, issue 4, pages 1-32, November.
• Rangan Gupta & Yuvana Jaichand & Christian Pierdzioch & Reneé van Eyden, 2023, "Realized Stock-Market Volatility of the United States and the Presidential Approval Rating," Mathematics, MDPI, volume 11, issue 13, pages 1-27, July.
• Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2023, "Climate Risks and Forecasting Stock Market Returns in Advanced Economies over a Century," Mathematics, MDPI, volume 11, issue 9, pages 1-21, April.
• Mihnea Constantinescu, 2023, "Sparse Warcasting," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 15-2023, Sep, revised 02 Oct 2023.
• John B. Guerard & Dimitrios D. Thomakos & Foteini Kyriazi & Konstantinos Mamais, 2023, "On the Predictability of the DJIA and S&P500 Indices," Working Papers, The George Washington University, The Center for Economic Research, number 2023-001, Jan.
• Dr. Marc Ingo Wolter & Florian Bernardt & Jannik Daßler & Saskia Reuschel & Dr. Britta Stöver, 2023, "Klimafolgen und Anpassung – 2023," GWS Research Report Series, GWS - Institute of Economic Structures Research, number 23-6.
• Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2023, "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," Post-Print, HAL, number emse-04624940, Jul, DOI: 10.1016/j.ijforecast.2022.04.009.
• Jonathan Benchimol & Lahcen Bounader, 2023, "Optimal monetary policy under bounded rationality," Post-Print, HAL, number emse-04624979, Aug, DOI: 10.1016/j.jfs.2023.101151.
• Laurent Ferrara & Anna Simoni, 2023, "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Post-Print, HAL, number hal-03919944, Oct, DOI: 10.1080/07350015.2022.2116025.
• F. Blasques & Christian Francq & Sébastien Laurent, 2023, "Quasi score-driven models," Post-Print, HAL, number hal-04069143, May, DOI: 10.1016/j.jeconom.2021.12.005.
• Andreas Dibiasi & Samad Sarferaz, 2023, "Measuring macroeconomic uncertainty: A cross-country analysis," Post-Print, HAL, number hal-04167343, Apr, DOI: 10.1016/j.euroecorev.2023.104383.
• Sahed Abdelkader & Kahoui Hacene, 2023, "Electricity Consumption Forecasting in Algeria using ARIMA and Long Short-Term Memory Neural Network," Post-Print, HAL, number hal-04183403, Jun.
• Benjamin Monnery & François-Charles Wolff, 2023, "Is participatory democracy in line with social protest? Evidence from the French Yellow Vests movement," Post-Print, HAL, number hal-04197291, DOI: 10.1007/s11127-023-01105-5.
• Gaetan Bakalli & Stéphane Guerrier & Olivier Scaillet, 2023, "A penalized two-pass regression to predict stock returns with time-varying risk premia," Post-Print, HAL, number hal-04325655, Dec, DOI: 10.1016/j.jeconom.2022.12.004.
• Benjamin Monnery & François-Charles Wolff, 2023, "Is participatory democracy in line with social protest? Evidence from the French Yellow Vests movement," Working Papers, HAL, number hal-04194969.
• J. van den Berg, Gerard & Kunaschk, Max & Lang, Julia & Stephan, Gesine & Uhlendorff, Arne, 2023, "Predicting re-employment: machine learning versus assessments by unemployed workers and by their caseworkers," Working Paper Series, IFAU - Institute for Evaluation of Labour Market and Education Policy, number 2023:22, Nov.
• Andersson, Jonas & Sheybanivaziri, Samaneh, 2023, "Probabilistic forecasting of electricity prices using an augmented LMARX-model," Discussion Papers, Norwegian School of Economics, Department of Business and Management Science, number 2023/11, Jul.
• Bårdsen, Gunnar & Nymoen, Ragnar, 2023, "Dynamic time series modelling and forecasting of COVID-19 in Norway," Memorandum, Oslo University, Department of Economics, number 3/2023, May.
• Vladimir Sviyazov, 2023, "Is There a Weekend Effect? Russian Stock Market Research Based on Fuzzy Systems," HSE Economic Journal, National Research University Higher School of Economics, volume 27, issue 3, pages 412-434.
• Watanabe, Toshiaki & Nakajima, Jouchi, 2023, "High-frequency realized stochastic volatility model," Discussion paper series, Hitotsubashi Institute for Advanced Study, Hitotsubashi University, number HIAS-E-127, Jan.
• Kouach Yassine & EL Attar Abderrahim & EL Hachloufi Mostafa, 2023, "Retakaful Contributions Model Using Machine Learning Techniques," Journal of Islamic Monetary Economics and Finance, Bank Indonesia, volume 9, issue 3, pages 511-532, September, DOI: https://doi.org/10.21098/jimf.v9i3..
• Saurabh Ghosh & Abhishek Ranjan, 2023, "A Machine Learning Approach To Gdp Nowcasting: An Emerging Market Experience," Bulletin of Monetary Economics and Banking, Bank Indonesia, volume 26, issue Special I, pages 33-54, February, DOI: https://doi.org/10.59091/1410-8046..
• Fortin, Ines & Hlouskova, Jaroslava, 2023, "Regime-dependent nowcasting of the Austrian economy," IHS Working Paper Series, Institute for Advanced Studies, number 51, Dec.
• Marcus Buckmann & Andreas Joseph, 2023, "An Interpretable Machine Learning Workflow with an Application to Economic Forecasting," International Journal of Central Banking, International Journal of Central Banking, volume 19, issue 4, pages 449-522, October.
• Caterina Lepore & Roshen Fernando, 2023, "Global Economic Impacts of Physical Climate Risks," IMF Working Papers, International Monetary Fund, number 2023/183, Sep.
• José Eduardo Medina Reyes & Agustín Ignacio Cabrera Llanos & Salvador Cruz Aké, 2023, "Fuzzy Gaussian GARCH and Fuzzy Gaussian EGARCH Models: Foreign Exchange Market Forecast," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, volume 18, issue 3, pages 1-22, Julio - S.
• Enrique R. Casares & María Guadalupe García-Salazar & Leobardo Pedro Plata Pérez & José Manuel Ramos Varela, 2023, "Deuda externa y crecimiento económico. Una calibración para México," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, volume 18, issue 3, pages 1-24, Julio - S.
• Patrycja Klusak & Matthew Agarwala & Matt Burke & Moritz Kraemer & Kamiar Mohaddes, 2023, "Rising Temperatures, Falling Ratings: The Effect of Climate Change on Sovereign Creditworthiness," Management Science, INFORMS, volume 69, issue 12, pages 7468-7491, December, DOI: 10.1287/mnsc.2023.4869.
• Marica Valente & Timm Gries & Lorenzo Trapani, 2023, "Informal employment from migration shocks," Working Papers, Faculty of Economics and Statistics, Universität Innsbruck, number 2023-09, Sep.
• Marc Burri, 2023, "Do daily lead texts help nowcasting GDP growth?," IRENE Working Papers, IRENE Institute of Economic Research, number 23-02, Jul.
• Sinem Kutlu Horvath & Ipek M. Yurttaguler, 2023, "Modeling Exchange Rate Volatility in Türkiye: An Empirical Research," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, volume 10, issue 2, pages 435-455, July, DOI: 10.26650/JEPR1217028.
• van den Berg, Gerard J. & Kunaschk, Max & Lang, Julia & Stephan, Gesine & Uhlendorff, Arne, 2023, "Predicting Re-Employment: Machine Learning versus Assessments by Unemployed Workers and by Their Caseworkers," IZA Discussion Papers, Institute of Labor Economics (IZA), number 16426, Sep.
• Dimitrios D. Thomakos & Marilou Ioakimidis & Konstantinos Eleftheriou, 2023, "Forecasting Tourism Demand for Medical Services," Journal of Developing Areas, Tennessee State University, College of Business, volume 57, issue 3, pages 315-320, July-Sept.
• Maiti,Dibyendu & Khari,Bhavna, 2023, "Digitalisation, Governance and the Informal Sector," IDE Discussion Papers, Institute of Developing Economies, Japan External Trade Organization(JETRO), number 898, May.
• Kachour Maher & Bakouch Hassan S. & Mohammadi Zohreh, 2023, "A New INAR(1) Model for ℤ-Valued Time Series Using the Relative Binomial Thinning Operator," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, volume 243, issue 2, pages 125-152, April, DOI: 10.1515/jbnst-2022-0059.
• Collischon Matthias, 2023, "Identifying Supervisory or Managerial Status in German Administrative Records," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, volume 243, issue 2, pages 183-195, April, DOI: 10.1515/jbnst-2022-0035.
• Haowen Bao & Zongwu Cai & Yuying Sun & Shouyang Wang, 2023, "Penalized Model Averaging for High Dimensional Quantile Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202302, Jan.
• Shahnaz Parsaeian, 2023, "Structural Breaks in Seemingly Unrelated Regression Models," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202308, Aug.
• Zongwu Cai & Gunawan, 2023, "A Combination Forecast for Nonparametric Models with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202310, Sep, revised Sep 2023.
• Haowen Bao & Zongwu Cai & Yuying Sun & Shouyang Wang, 2023, "Penalized Optimal Forecast Combination for Quantile Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202514, Jan, revised May 2025.
• Nithin Mani & Alok Kumar Mishra & Jijin Pandikasala, 2023, "How Serious is India’s Nonperforming Assets Crisis? A Structural Satellite Version of the Financial-Macroeconometric Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, volume 30, issue 4, pages 761-794, December, DOI: 10.1007/s10690-023-09397-9.
• Ruzhen Yan & Ding Yue & Xu Wu & Wei Gao, 2023, "Multiscale Multifractal Detrended Fluctuation Analysis and Trend Identification of Liquidity in the China's Stock Markets," Computational Economics, Springer;Society for Computational Economics, volume 61, issue 2, pages 487-511, February, DOI: 10.1007/s10614-021-10215-5.
• Yushu Li & Hyunjoo Kim Karlsson, 2023, "Investigating the Asymmetric Behavior of Oil Price Volatility Using Support Vector Regression," Computational Economics, Springer;Society for Computational Economics, volume 61, issue 4, pages 1765-1790, April, DOI: 10.1007/s10614-022-10266-2.
• Jan G. De Gooijer, 2023, "Penalized Averaging of Quantile Forecasts from GARCH Models with Many Exogenous Predictors," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 1, pages 407-424, June, DOI: 10.1007/s10614-022-10289-9.
• Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Mariem Nsaibi, 2023, "Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 2, pages 663-687, August, DOI: 10.1007/s10614-022-10305-y.
• Ba Chu & Shafiullah Qureshi, 2023, "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 4, pages 1567-1609, December, DOI: 10.1007/s10614-022-10312-z.

2022

• Zhang, Han & Guo, Bin & Liu, Lanbiao, 2022, "The time-varying bond risk premia in China," Journal of Empirical Finance, Elsevier, volume 65, issue C, pages 51-76, DOI: 10.1016/j.jempfin.2021.11.004.
• Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022, "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, volume 106, issue C, DOI: 10.1016/j.eneco.2021.105760.
• Pincheira-Brown, Pablo & Bentancor, Andrea & Hardy, Nicolás & Jarsun, Nabil, 2022, "Forecasting fuel prices with the Chilean exchange rate: Going beyond the commodity currency hypothesis," Energy Economics, Elsevier, volume 106, issue C, DOI: 10.1016/j.eneco.2021.105802.
• Mahler, Valentin & Girard, Robin & Kariniotakis, Georges, 2022, "Data-driven structural modeling of electricity price dynamics," Energy Economics, Elsevier, volume 107, issue C, DOI: 10.1016/j.eneco.2022.105811.
• Ren, Xiaohang & Duan, Kun & Tao, Lizhu & Shi, Yukun & Yan, Cheng, 2022, "Carbon prices forecasting in quantiles," Energy Economics, Elsevier, volume 108, issue C, DOI: 10.1016/j.eneco.2022.105862.
• Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022, "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, volume 108, issue C, DOI: 10.1016/j.eneco.2022.105934.
• Li, Xiafei & Liang, Chao & Chen, Zhonglu & Umar, Muhammad, 2022, "Forecasting crude oil volatility with uncertainty indicators: New evidence," Energy Economics, Elsevier, volume 108, issue C, DOI: 10.1016/j.eneco.2022.105936.
• Luo, Keyu & Guo, Qiang & Li, Xiafei, 2022, "Can the return connectedness indices from grey energy to natural gas help to forecast the natural gas returns?," Energy Economics, Elsevier, volume 109, issue C, DOI: 10.1016/j.eneco.2022.105947.
• Salisu, Afees A. & Olaniran, Abeeb & Tchankam, Jean Paul, 2022, "Oil tail risk and the tail risk of the US Dollar exchange rates," Energy Economics, Elsevier, volume 109, issue C, DOI: 10.1016/j.eneco.2022.105960.
• Umar, Zaghum & Aharon, David Y. & Esparcia, Carlos & AlWahedi, Wafa, 2022, "Spillovers between sovereign yield curve components and oil price shocks," Energy Economics, Elsevier, volume 109, issue C, DOI: 10.1016/j.eneco.2022.105963.
• Xing, Li-Min & Zhang, Yue-Jun, 2022, "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, volume 110, issue C, DOI: 10.1016/j.eneco.2022.106014.
• Guo, Xiaozhu & Huang, Yisu & Liang, Chao & Umar, Muhammad, 2022, "Forecasting volatility of EUA futures: New evidence," Energy Economics, Elsevier, volume 110, issue C, DOI: 10.1016/j.eneco.2022.106021.
• Serafin, Tomasz & Marcjasz, Grzegorz & Weron, Rafał, 2022, "Trading on short-term path forecasts of intraday electricity prices," Energy Economics, Elsevier, volume 112, issue C, DOI: 10.1016/j.eneco.2022.106125.
• Çepni, Oğuzhan & Gupta, Rangan & Pienaar, Daniel & Pierdzioch, Christian, 2022, "Forecasting the realized variance of oil-price returns using machine learning: Is there a role for U.S. state-level uncertainty?," Energy Economics, Elsevier, volume 114, issue C, DOI: 10.1016/j.eneco.2022.106229.
• Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022, "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, volume 114, issue C, DOI: 10.1016/j.eneco.2022.106285.
• Alturki, Sultan & Olson, Eric, 2022, "Oil sentiment and the U.S. inflation premium," Energy Economics, Elsevier, volume 114, issue C, DOI: 10.1016/j.eneco.2022.106317.
• Nonejad, Nima, 2022, "Equity premium prediction using the price of crude oil: Uncovering the nonlinear predictive impact," Energy Economics, Elsevier, volume 115, issue C, DOI: 10.1016/j.eneco.2022.106395.
• Huo, Da & Zhang, Xiaotao & Meng, Shuang & Wu, Gang & Li, Junhang & Di, Ruoqi, 2022, "Green finance and energy efficiency: Dynamic study of the spatial externality of institutional support in a digital economy by using hidden Markov chain," Energy Economics, Elsevier, volume 116, issue C, DOI: 10.1016/j.eneco.2022.106431.
• Kertlly de Medeiros, Rennan & da Nóbrega Besarria, Cássio & Pitta de Jesus, Diego & Phillipe de Albuquerquemello, Vinicius, 2022, "Forecasting oil prices: New approaches," Energy, Elsevier, volume 238, issue PC, DOI: 10.1016/j.energy.2021.121968.
• Kuang, Wei, 2022, "The economic value of high-frequency data in equity-oil hedge," Energy, Elsevier, volume 239, issue PA, DOI: 10.1016/j.energy.2021.121904.
• Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2022, "Geopolitical risk trends and crude oil price predictability," Energy, Elsevier, volume 258, issue C, DOI: 10.1016/j.energy.2022.124824.
• Ellington, Michael & Stamatogiannis, Michalis P. & Zheng, Yawen, 2022, "A study of cross-industry return predictability in the Chinese stock market," International Review of Financial Analysis, Elsevier, volume 83, issue C, DOI: 10.1016/j.irfa.2022.102249.
• Nonejad, Nima, 2022, "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, volume 83, issue C, DOI: 10.1016/j.irfa.2022.102251.
• Ye, Wuyi & Xia, Wenjing & Wu, Bin & Chen, Pengzhan, 2022, "Using implied volatility jumps for realized volatility forecasting: Evidence from the Chinese market," International Review of Financial Analysis, Elsevier, volume 83, issue C, DOI: 10.1016/j.irfa.2022.102277.
• Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan & Gabauer, David, 2022, "Forecasting stock-market tail risk and connectedness in advanced economies over a century: The role of gold-to-silver and gold-to-platinum price ratios," International Review of Financial Analysis, Elsevier, volume 83, issue C, DOI: 10.1016/j.irfa.2022.102300.
• Sattarhoff, Cristina & Gronwald, Marc, 2022, "Measuring informational efficiency of the European carbon market — A quantitative evaluation of higher order dependence," International Review of Financial Analysis, Elsevier, volume 84, issue C, DOI: 10.1016/j.irfa.2022.102403.
• Alanya-Beltran, Willy, 2022, "Modelling stock returns volatility with dynamic conditional score models and random shifts," Finance Research Letters, Elsevier, volume 45, issue C, DOI: 10.1016/j.frl.2021.102121.
• Sheng, Xin & Gupta, Rangan & Salisu, Afees A. & Bouri, Elie, 2022, "OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning," Finance Research Letters, Elsevier, volume 45, issue C, DOI: 10.1016/j.frl.2021.102125.
• Salisu, Afees A. & Tchankam, Jean Paul, 2022, "US Stock return predictability with high dimensional models," Finance Research Letters, Elsevier, volume 45, issue C, DOI: 10.1016/j.frl.2021.102194.
• Kutuk, Yasin & Barokas, Lina, 2022, "Multivariate CDS risk premium prediction with SOTA RNNs on MI[N]T countries," Finance Research Letters, Elsevier, volume 45, issue C, DOI: 10.1016/j.frl.2021.102198.
• Duan, Yuejiao & Goodell, John W. & Li, Haoran & Li, Xinming, 2022, "Assessing machine learning for forecasting economic risk: Evidence from an expanded Chinese financial information set," Finance Research Letters, Elsevier, volume 46, issue PA, DOI: 10.1016/j.frl.2021.102273.
• Nonejad, Nima, 2022, "Forecasting crude oil price volatility out-of-sample using news-based geopolitical risk index: What forms of nonlinearity help improve forecast accuracy the most?," Finance Research Letters, Elsevier, volume 46, issue PA, DOI: 10.1016/j.frl.2021.102310.
• Lyócsa, Štefan & Baumöhl, Eduard & Výrost, Tomáš, 2022, "YOLO trading: Riding with the herd during the GameStop episode," Finance Research Letters, Elsevier, volume 46, issue PA, DOI: 10.1016/j.frl.2021.102359.
• Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan, 2022, "Oil tail risks and the forecastability of the realized variance of oil-price: Evidence from over 150 years of data," Finance Research Letters, Elsevier, volume 46, issue PB, DOI: 10.1016/j.frl.2021.102378.
• Doan, Bao & Lee, John B. & Liu, Qianqiu & Reeves, Jonathan J., 2022, "Beta measurement with high frequency returns," Finance Research Letters, Elsevier, volume 47, issue PA, DOI: 10.1016/j.frl.2021.102632.
• Su, Hao & Ying, Chengwei & Zhu, Xiaoneng, 2022, "Disaster risk matters in the bond market," Finance Research Letters, Elsevier, volume 47, issue PA, DOI: 10.1016/j.frl.2022.102764.
• Nonejad, Nima, 2022, "An interesting finding about the ability of geopolitical risk to forecast aggregate equity return volatility out-of-sample," Finance Research Letters, Elsevier, volume 47, issue PB, DOI: 10.1016/j.frl.2022.102710.
• Urom, Christian & Ndubuisi, Gideon & Guesmi, Khaled, 2022, "How do financial and commodity markets volatility react to real economic activity?," Finance Research Letters, Elsevier, volume 47, issue PB, DOI: 10.1016/j.frl.2022.102733.
• Hanauer, Matthias X. & Kononova, Marina & Rapp, Marc Steffen, 2022, "Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets," Finance Research Letters, Elsevier, volume 48, issue C, DOI: 10.1016/j.frl.2022.102856.
• Achakzai, Muhammad Atif Khan & Juan, Peng, 2022, "Using machine learning Meta-Classifiers to detect financial frauds," Finance Research Letters, Elsevier, volume 48, issue C, DOI: 10.1016/j.frl.2022.102915.
• González-Pla, Francisco & Lovreta, Lidija, 2022, "Modeling and forecasting firm-specific volatility: The role of asymmetry and long-memory," Finance Research Letters, Elsevier, volume 48, issue C, DOI: 10.1016/j.frl.2022.102931.
• Mei, Dexiang & Xie, Yutang, 2022, "U.S. grain commodity futures price volatility: Does trade policy uncertainty matter?," Finance Research Letters, Elsevier, volume 48, issue C, DOI: 10.1016/j.frl.2022.103028.
• Jiménez, Inés & Mora-Valencia, Andrés & Perote, Javier, 2022, "Has the interaction between skewness and kurtosis of asset returns information content for risk forecasting?," Finance Research Letters, Elsevier, volume 49, issue C, DOI: 10.1016/j.frl.2022.103105.
• Bouri, Elie & Christou, Christina & Gupta, Rangan, 2022, "Forecasting returns of major cryptocurrencies: Evidence from regime-switching factor models," Finance Research Letters, Elsevier, volume 49, issue C, DOI: 10.1016/j.frl.2022.103193.
• Papík, Mário & Papíková, Lenka, 2022, "Detecting accounting fraud in companies reporting under US GAAP through data mining," International Journal of Accounting Information Systems, Elsevier, volume 45, issue C, DOI: 10.1016/j.accinf.2022.100559.
• Al-Mudafer, Muhammed Taher & Avanzi, Benjamin & Taylor, Greg & Wong, Bernard, 2022, "Stochastic loss reserving with mixture density neural networks," Insurance: Mathematics and Economics, Elsevier, volume 105, issue C, pages 144-174, DOI: 10.1016/j.insmatheco.2022.03.010.
• Ang, Zi Qing & Lee, See Keong, 2022, "Hierarchical Bayesian Gaussian process regression model for loss reserving using combinations of squared exponential kernels," Insurance: Mathematics and Economics, Elsevier, volume 105, issue C, pages 54-63, DOI: 10.1016/j.insmatheco.2022.03.008.
• Henckaerts, Roel & Antonio, Katrien, 2022, "The added value of dynamically updating motor insurance prices with telematics collected driving behavior data," Insurance: Mathematics and Economics, Elsevier, volume 105, issue C, pages 79-95, DOI: 10.1016/j.insmatheco.2022.03.011.
• Meng, Shengwang & Gao, Yaqian & Huang, Yifan, 2022, "Actuarial intelligence in auto insurance: Claim frequency modeling with driving behavior features and improved boosted trees," Insurance: Mathematics and Economics, Elsevier, volume 106, issue C, pages 115-127, DOI: 10.1016/j.insmatheco.2022.06.001.
• Hu, Changyue & Quan, Zhiyu & Chong, Wing Fung, 2022, "Imbalanced learning for insurance using modified loss functions in tree-based models," Insurance: Mathematics and Economics, Elsevier, volume 106, issue C, pages 13-32, DOI: 10.1016/j.insmatheco.2022.04.010.
• Steinmetz, Julia & Jentsch, Carsten, 2022, "Asymptotic theory for Mack's model," Insurance: Mathematics and Economics, Elsevier, volume 107, issue C, pages 223-268, DOI: 10.1016/j.insmatheco.2022.08.007.
• Verschuren, Robert Matthijs, 2022, "Frequency-severity experience rating based on latent Markovian risk profiles," Insurance: Mathematics and Economics, Elsevier, volume 107, issue C, pages 379-392, DOI: 10.1016/j.insmatheco.2022.09.007.
• Xu, Shuzhe & Zhang, Chuanlong & Hong, Don, 2022, "BERT-based NLP techniques for classification and severity modeling in basic warranty data study," Insurance: Mathematics and Economics, Elsevier, volume 107, issue C, pages 57-67, DOI: 10.1016/j.insmatheco.2022.07.013.
• Zhang, Dan & Farnoosh, Arash & Lantz, Frédéric, 2022, "Does something change in the oil market with the COVID-19 crisis?," International Economics, Elsevier, volume 169, issue C, pages 252-268, DOI: 10.1016/j.inteco.2022.01.008.
• Ferrara, Laurent & Yapi, Joseph, 2022, "Measuring exchange rate risks during periods of uncertainty," International Economics, Elsevier, volume 170, issue C, pages 202-212, DOI: 10.1016/j.inteco.2022.04.001.
• He, Mengxi & Zhang, Yaojie, 2022, "Climate policy uncertainty and the stock return predictability of the oil industry," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 81, issue C, DOI: 10.1016/j.intfin.2022.101675.
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• Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2022, "Forecasting realized volatility of agricultural commodities," International Journal of Forecasting, Elsevier, volume 38, issue 1, pages 74-96, DOI: 10.1016/j.ijforecast.2019.08.011.
• Lahiri, Kajal & Yang, Cheng, 2022, "Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York," International Journal of Forecasting, Elsevier, volume 38, issue 2, pages 545-566, DOI: 10.1016/j.ijforecast.2021.10.005.
• Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022, "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, volume 38, issue 2, pages 596-612, DOI: 10.1016/j.ijforecast.2020.12.005.
• Larson, William D. & Sinclair, Tara M., 2022, "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, volume 38, issue 2, pages 635-647, DOI: 10.1016/j.ijforecast.2021.01.001.
• Conlon, Thomas & Cotter, John & Eyiah-Donkor, Emmanuel, 2022, "The illusion of oil return predictability: The choice of data matters!," Journal of Banking & Finance, Elsevier, volume 134, issue C, DOI: 10.1016/j.jbankfin.2021.106331.
• Caporin, Massimiliano & Costola, Michele & Garibal, Jean-Charles & Maillet, Bertrand, 2022, "Systemic risk and severe economic downturns: A targeted and sparse analysis," Journal of Banking & Finance, Elsevier, volume 134, issue C, DOI: 10.1016/j.jbankfin.2021.106339.
• Taylor, James W., 2022, "Forecasting Value at Risk and expected shortfall using a model with a dynamic omega ratio," Journal of Banking & Finance, Elsevier, volume 140, issue C, DOI: 10.1016/j.jbankfin.2022.106519.
• Huang, Rong & Pilbeam, Keith & Pouliot, William, 2022, "Are macroeconomic forecasters optimists or pessimists? A reassessment of survey based forecasts," Journal of Economic Behavior & Organization, Elsevier, volume 197, issue C, pages 706-724, DOI: 10.1016/j.jebo.2022.03.012.
• Benchimol, Jonathan & El-Shagi, Makram & Saadon, Yossi, 2022, "Do expert experience and characteristics affect inflation forecasts?," Journal of Economic Behavior & Organization, Elsevier, volume 201, issue C, pages 205-226, DOI: 10.1016/j.jebo.2022.06.025.
• Obaid, Khaled & Pukthuanthong, Kuntara, 2022, "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, volume 144, issue 1, pages 273-297, DOI: 10.1016/j.jfineco.2021.06.002.
• Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022, "Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications," Food Policy, Elsevier, volume 112, issue C, DOI: 10.1016/j.foodpol.2022.102345.
• Kishor, N. Kundan & Marfatia, Hardik A. & Nam, Gooan & Rizi, Majid Haghani, 2022, "The local employment effect of house prices: Evidence from U.S. States," Journal of Housing Economics, Elsevier, volume 55, issue C, DOI: 10.1016/j.jhe.2021.101805.
• Degiannakis, Stavros & Filis, George, 2022, "Oil price volatility forecasts: What do investors need to know?," Journal of International Money and Finance, Elsevier, volume 123, issue C, DOI: 10.1016/j.jimonfin.2021.102594.
• Casabianca, Elizabeth Jane & Catalano, Michele & Forni, Lorenzo & Giarda, Elena & Passeri, Simone, 2022, "A machine learning approach to rank the determinants of banking crises over time and across countries," Journal of International Money and Finance, Elsevier, volume 129, issue C, DOI: 10.1016/j.jimonfin.2022.102739.
• Dai, Peng-Fei & Xiong, Xiong & Duc Huynh, Toan Luu & Wang, Jiqiang, 2022, "The impact of economic policy uncertainties on the volatility of European carbon market," Journal of Commodity Markets, Elsevier, volume 26, issue C, DOI: 10.1016/j.jcomm.2021.100208.
• Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2022, "Common factors and the dynamics of cereal prices. A forecasting perspective," Journal of Commodity Markets, Elsevier, volume 28, issue C, DOI: 10.1016/j.jcomm.2021.100240.
• Alfeus, Mesias & Nikitopoulos, Christina Sklibosios, 2022, "Forecasting volatility in commodity markets with long-memory models," Journal of Commodity Markets, Elsevier, volume 28, issue C, DOI: 10.1016/j.jcomm.2022.100248.
• Stolbov, Mikhail & Shchepeleva, Maria, 2022, "Modeling global real economic activity: Evidence from variable selection across quantiles," The Journal of Economic Asymmetries, Elsevier, volume 25, issue C, DOI: 10.1016/j.jeca.2021.e00238.
• Liu, Guangqiang & Guo, Xiaozhu, 2022, "Forecasting stock market volatility using commodity futures volatility information," Resources Policy, Elsevier, volume 75, issue C, DOI: 10.1016/j.resourpol.2021.102481.
• Salisu, Afees A. & Gupta, Rangan & Ji, Qiang, 2022, "Forecasting oil prices over 150 years: The role of tail risks," Resources Policy, Elsevier, volume 75, issue C, DOI: 10.1016/j.resourpol.2021.102508.
• Yan, Xiang & Bai, Jiancheng & Li, Xiafei & Chen, Zhonglu, 2022, "Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?," Resources Policy, Elsevier, volume 75, issue C, DOI: 10.1016/j.resourpol.2021.102521.
• Salisu, Afees A. & Gupta, Rangan & Karmakar, Sayar & Das, Sonali, 2022, "Forecasting output growth of advanced economies over eight centuries: The role of gold market volatility as a proxy of global uncertainty," Resources Policy, Elsevier, volume 75, issue C, DOI: 10.1016/j.resourpol.2021.102527.
• Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022, "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, volume 76, issue C, DOI: 10.1016/j.resourpol.2022.102570.
• Gupta, Rangan & Pierdzioch, Christian & Salisu, Afees A., 2022, "Oil-price uncertainty and the U.K. unemployment rate: A forecasting experiment with random forests using 150 years of data," Resources Policy, Elsevier, volume 77, issue C, DOI: 10.1016/j.resourpol.2022.102662.
• Hong, Yanran & Wang, Lu & Liang, Chao & Umar, Muhammad, 2022, "Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework," Resources Policy, Elsevier, volume 77, issue C, DOI: 10.1016/j.resourpol.2022.102667.
• Gupta, Rangan & Pierdzioch, Christian, 2022, "Climate risks and forecastability of the realized volatility of gold and other metal prices," Resources Policy, Elsevier, volume 77, issue C, DOI: 10.1016/j.resourpol.2022.102681.
• Mei, Dexiang & Zhao, Chenchen & Luo, Qin & Li, Yan, 2022, "Forecasting the Chinese low-carbon index volatility," Resources Policy, Elsevier, volume 77, issue C, DOI: 10.1016/j.resourpol.2022.102732.
• Lin, Yu & Liao, Qidong & Lin, Zixiao & Tan, Bin & Yu, Yuanyuan, 2022, "A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction," Resources Policy, Elsevier, volume 78, issue C, DOI: 10.1016/j.resourpol.2022.102884.
• Kakade, Kshitij & Jain, Ishan & Mishra, Aswini Kumar, 2022, "Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach," Resources Policy, Elsevier, volume 78, issue C, DOI: 10.1016/j.resourpol.2022.102903.
• Liu, Guangqiang & Zeng, Qing & Lei, Juan, 2022, "Dynamic risks from climate policy uncertainty: A case study for the natural gas market," Resources Policy, Elsevier, volume 79, issue C, DOI: 10.1016/j.resourpol.2022.103014.
• Zhao, Jing, 2022, "Exploring the influence of the main factors on the crude oil price volatility: An analysis based on GARCH-MIDAS model with Lasso approach," Resources Policy, Elsevier, volume 79, issue C, DOI: 10.1016/j.resourpol.2022.103031.
• Song, Yixuan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2022, "Forecasting crude oil market volatility: A newspaper-based predictor regarding petroleum market volatility," Resources Policy, Elsevier, volume 79, issue C, DOI: 10.1016/j.resourpol.2022.103093.
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• Xu, Yongan & Li, Ming & Yan, Wen & Bai, Jiancheng, 2022, "Predictability of the renewable energy market returns: The informational gains from the climate policy uncertainty," Resources Policy, Elsevier, volume 79, issue C, DOI: 10.1016/j.resourpol.2022.103141.
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• Umar, Zaghum & Yousaf, Imran & Gubareva, Mariya & Vo, Xuan Vinh, 2022, "Spillover and risk transmission between the term structure of the US interest rates and Islamic equities," Pacific-Basin Finance Journal, Elsevier, volume 72, issue C, DOI: 10.1016/j.pacfin.2022.101712.
• Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022, "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, volume 76, issue C, DOI: 10.1016/j.pacfin.2022.101886.
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• Su, Zhi & Liu, Peng & Fang, Tong, 2022, "Uncertainty matters in US financial information spillovers: Evidence from a directed acyclic graph approach," The Quarterly Review of Economics and Finance, Elsevier, volume 84, issue C, pages 229-242, DOI: 10.1016/j.qref.2022.01.007.
• Fasanya, Ismail O. & Oyewole, Oluwatomisin J. & Oliyide, Johnson A., 2022, "Investors' sentiments and the dynamic connectedness between cryptocurrency and precious metals markets," The Quarterly Review of Economics and Finance, Elsevier, volume 86, issue C, pages 347-364, DOI: 10.1016/j.qref.2022.08.009.
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• Salisu, Afees A. & Cuñado, Juncal & Gupta, Rangan, 2022, "Geopolitical risks and historical exchange rate volatility of the BRICS," International Review of Economics & Finance, Elsevier, volume 77, issue C, pages 179-190, DOI: 10.1016/j.iref.2021.09.017.
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• Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je & Gau, Yin-Feng, 2022, "Risk-return trade-off in the Australian Securities Exchange: Accounting for overnight effects, realized higher moments, long-run relations, and fractional cointegration," International Review of Economics & Finance, Elsevier, volume 80, issue C, pages 384-401, DOI: 10.1016/j.iref.2022.02.057.
• Jiménez, Inés & Mora-Valencia, Andrés & Perote, Javier, 2022, "Semi-nonparametric risk assessment with cryptocurrencies," Research in International Business and Finance, Elsevier, volume 59, issue C, DOI: 10.1016/j.ribaf.2021.101567.
• Kristóf, Tamás & Virág, Miklós, 2022, "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, volume 61, issue C, DOI: 10.1016/j.ribaf.2022.101644.
• Wang, Yaqi & Wang, Chunfeng & Sensoy, Ahmet & Yao, Shouyu & Cheng, Feiyang, 2022, "Can investors’ informed trading predict cryptocurrency returns? Evidence from machine learning," Research in International Business and Finance, Elsevier, volume 62, issue C, DOI: 10.1016/j.ribaf.2022.101683.
• Su, Yuandong & Lu, Xinjie & Zeng, Qing & Huang, Dengshi, 2022, "Good air quality and stock market returns," Research in International Business and Finance, Elsevier, volume 62, issue C, DOI: 10.1016/j.ribaf.2022.101723.
• Tripathy, Naliniprava, 2022, "Long memory and volatility persistence across BRICS stock markets," Research in International Business and Finance, Elsevier, volume 63, issue C, DOI: 10.1016/j.ribaf.2022.101782.
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