Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2023

• Grothe, Oliver & Kächele, Fabian & Krüger, Fabian, 2023, "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Energy Economics, Elsevier, volume 120, issue C, DOI: 10.1016/j.eneco.2023.106602.
• Garratt, Anthony & Petrella, Ivan & Zhang, Yunyi, 2023, "Asymmetry and interdependence when evaluating U.S. Energy Information Administration forecasts," Energy Economics, Elsevier, volume 121, issue C, DOI: 10.1016/j.eneco.2023.106620.
• Abdollahi, Hooman, 2023, "Oil price volatility and new evidence from news and Twitter," Energy Economics, Elsevier, volume 122, issue C, DOI: 10.1016/j.eneco.2023.106711.
• Liu, Yue & Sun, Huaping & Meng, Bo & Jin, Shunlin & Chen, Bin, 2023, "How to purchase carbon emission right optimally for energy-consuming enterprises? Analysis based on optimal stopping model," Energy Economics, Elsevier, volume 124, issue C, DOI: 10.1016/j.eneco.2023.106758.
• Diebold, Francis X. & Göbel, Maximilian & Goulet Coulombe, Philippe, 2023, "Assessing and comparing fixed-target forecasts of Arctic sea ice: Glide charts for feature-engineered linear regression and machine learning models," Energy Economics, Elsevier, volume 124, issue C, DOI: 10.1016/j.eneco.2023.106833.
• Tian, Guangning & Peng, Yuchao & Meng, Yuhao, 2023, "Forecasting crude oil prices in the COVID-19 era: Can machine learn better?," Energy Economics, Elsevier, volume 125, issue C, DOI: 10.1016/j.eneco.2023.106788.
• Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023, "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, volume 125, issue C, DOI: 10.1016/j.eneco.2023.106843.
• Nonejad, Nima, 2023, "Modeling the out-of-sample predictive relationship between equity premium, returns on the price of crude oil and economic policy uncertainty using multivariate time-varying dimension models," Energy Economics, Elsevier, volume 126, issue C, DOI: 10.1016/j.eneco.2023.106964.
• Bennedsen, Mikkel & Hillebrand, Eric & Jensen, Sebastian, 2023, "A neural network approach to the environmental Kuznets curve," Energy Economics, Elsevier, volume 126, issue C, DOI: 10.1016/j.eneco.2023.106985.
• Li, Yan & Huynh, Luu Duc Toan & Xu, Yongan & Liang, Hao, 2023, "The forecast ability of a belief-based momentum indicator in full-day, daytime, and nighttime volatilities of Chinese oil futures," Energy Economics, Elsevier, volume 127, issue PB, DOI: 10.1016/j.eneco.2023.107064.
• Wang, Cheng & Bouri, Elie & Xu, Yahua & Zhang, Dingsheng, 2023, "Intraday and overnight tail risks and return predictability in the crude oil market: Evidence from oil-related regular news and extreme shocks," Energy Economics, Elsevier, volume 127, issue PB, DOI: 10.1016/j.eneco.2023.107121.
• Panarello, Demetrio & Gatto, Andrea, 2023, "Decarbonising Europe – EU citizens’ perception of renewable energy transition amidst the European Green Deal," Energy Policy, Elsevier, volume 172, issue C, DOI: 10.1016/j.enpol.2022.113272.
• Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023, "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, volume 262, issue PB, DOI: 10.1016/j.energy.2022.125589.
• Kuang, Wei, 2023, "The equity-oil hedge: A comparison between volatility and alternative risk frameworks," Energy, Elsevier, volume 271, issue C, DOI: 10.1016/j.energy.2023.127045.
• Li, Zhao-Chen & Xie, Chi & Zeng, Zhi-Jian & Wang, Gang-Jin & Zhang, Ting, 2023, "Forecasting global stock market volatilities in an uncertain world," International Review of Financial Analysis, Elsevier, volume 85, issue C, DOI: 10.1016/j.irfa.2022.102463.
• Zhang, Ning & Su, Xiaoman & Qi, Shuyuan, 2023, "An empirical investigation of multiperiod tail risk forecasting models," International Review of Financial Analysis, Elsevier, volume 86, issue C, DOI: 10.1016/j.irfa.2023.102498.
• Ghosh, Indranil & Alfaro-Cortés, Esteban & Gámez, Matías & García-Rubio, Noelia, 2023, "Prediction and interpretation of daily NFT and DeFi prices dynamics: Inspection through ensemble machine learning & XAI," International Review of Financial Analysis, Elsevier, volume 87, issue C, DOI: 10.1016/j.irfa.2023.102558.
• Gao, Jun & Gao, Xiang & Gu, Chen, 2023, "Forecasting European stock volatility: The role of the UK," International Review of Financial Analysis, Elsevier, volume 89, issue C, DOI: 10.1016/j.irfa.2023.102728.
• Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023, "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, volume 89, issue C, DOI: 10.1016/j.irfa.2023.102770.
• Achakzai, Muhammad Atif Khan & Peng, Juan, 2023, "Detecting financial statement fraud using dynamic ensemble machine learning," International Review of Financial Analysis, Elsevier, volume 89, issue C, DOI: 10.1016/j.irfa.2023.102827.
• Citterio, Alberto & King, Timothy, 2023, "The role of Environmental, Social, and Governance (ESG) in predicting bank financial distress," Finance Research Letters, Elsevier, volume 51, issue C, DOI: 10.1016/j.frl.2022.103411.
• Ghosh, Indranil & Alfaro-Cortés, Esteban & Gámez, Matías & García, Noelia, 2023, "Do travel uncertainty and invasion rhetoric spur Metaverse financial asset? – Gauging the role of media influence," Finance Research Letters, Elsevier, volume 51, issue C, DOI: 10.1016/j.frl.2022.103434.
• Yamani, Ehab, 2023, "The informational role of fund flow in the profitable predictability of mutual funds," Finance Research Letters, Elsevier, volume 51, issue C, DOI: 10.1016/j.frl.2022.103445.
• Díaz-Mendoza, Ana Carmen & Pardo, Ángel, 2023, "Water and traditional asset classes," Finance Research Letters, Elsevier, volume 52, issue C, DOI: 10.1016/j.frl.2022.103394.
• Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Jia, Zhimin, 2023, "Complete subset averaging methods in corporate bond return prediction," Finance Research Letters, Elsevier, volume 54, issue C, DOI: 10.1016/j.frl.2023.103727.
• Gupta, Rangan & Nel, Jacobus & Salisu, Afees A. & Ji, Qiang, 2023, "Predictability of economic slowdowns in advanced countries over eight centuries: The role of climate risks," Finance Research Letters, Elsevier, volume 54, issue C, DOI: 10.1016/j.frl.2023.103795.
• Kawakami, Tabito, 2023, "Quantile prediction for Bitcoin returns using financial assets’ realized measures," Finance Research Letters, Elsevier, volume 55, issue PA, DOI: 10.1016/j.frl.2023.103843.
• Gao, Shang & Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie, 2023, "Forecasting stock market volatility: The sum of the parts is more than the whole," Finance Research Letters, Elsevier, volume 55, issue PA, DOI: 10.1016/j.frl.2023.103849.
• Korkusuz, Burak & Kambouroudis, Dimos & McMillan, David G., 2023, "Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets," Finance Research Letters, Elsevier, volume 55, issue PB, DOI: 10.1016/j.frl.2023.103992.
• Hartkopf, Jan Patrick & Reh, Laura, 2023, "Challenging golden standards in EWMA smoothing parameter calibration based on realized covariance measures," Finance Research Letters, Elsevier, volume 56, issue C, DOI: 10.1016/j.frl.2023.104129.
• Zhang, Jiaming & Zou, Yang & Xiang, Yitian & Guo, Songlin, 2023, "Climate change and Japanese economic policy uncertainty: Asymmetric analysis," Finance Research Letters, Elsevier, volume 56, issue C, DOI: 10.1016/j.frl.2023.104165.
• Hou, Yunfei & Hu, Changsheng, 2023, "Understanding the role of aggregate analyst attention in resolving stock market uncertainty," Finance Research Letters, Elsevier, volume 57, issue C, DOI: 10.1016/j.frl.2023.104183.
• Xu, Yongan & Duong, Duy & Xu, Hualong, 2023, "Attention! Predicting crude oil prices from the perspective of extreme weather," Finance Research Letters, Elsevier, volume 57, issue C, DOI: 10.1016/j.frl.2023.104190.
• Jiang, Wei & Tang, Wanqing & Liu, Xiao, 2023, "Forecasting realized volatility of Chinese crude oil futures with a new secondary decomposition ensemble learning approach," Finance Research Letters, Elsevier, volume 57, issue C, DOI: 10.1016/j.frl.2023.104254.
• Zhu, Qinwen & Diao, Xundi & Wu, Chongfeng, 2023, "Volatility forecast with the regularity modifications," Finance Research Letters, Elsevier, volume 58, issue PA, DOI: 10.1016/j.frl.2023.104008.
• Feng, Yun & Hou, Weijie & Song, Yuping, 2023, "Tail risk in the Chinese stock market: An AEV model on the maximal drawdowns," Finance Research Letters, Elsevier, volume 58, issue PA, DOI: 10.1016/j.frl.2023.104294.
• Zhang, Zhihao, 2023, "Are climate risks helpful for understanding inflation in BRICS countries?," Finance Research Letters, Elsevier, volume 58, issue PB, DOI: 10.1016/j.frl.2023.104441.
• Luo, Tao & Zhang, Lixia & Sun, Huaping & Bai, Jiancheng, 2023, "Enhancing exchange rate volatility prediction accuracy: Assessing the influence of different indices on the USD/CNY exchange rate," Finance Research Letters, Elsevier, volume 58, issue PB, DOI: 10.1016/j.frl.2023.104483.
• Shu, Qi & Xiong, Heng & Jiang, Wenjun & Mamon, Rogemar, 2023, "A novel perspective on forecasting non-ferrous metals’ volatility: Integrating deep learning techniques with econometric models," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104482.
• Gupta, Rangan & Ji, Qiang & Pierdzioch, Christian & Plakandaras, Vasilios, 2023, "Forecasting the conditional distribution of realized volatility of oil price returns: The role of skewness over 1859 to 2023," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104501.
• Barua, Ronil & Sharma, Anil K., 2023, "Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104515.
• Jin, Daxiang & Yu, Jize, 2023, "Predicting cryptocurrency market volatility: Novel evidence from climate policy uncertainty," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104520.
• Zhu, Jiaji & Han, Wei & Zhang, Junchao, 2023, "Does climate risk matter for gold price volatility?," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104544.
• Coita, Ioana-Florina & Belbe, Stefana (Ștefana) & Mare, Codruta (Codruța) & Osterrieder, Joerg & Hopp, Christian, 2023, "Modelling taxpayers’ behaviour based on prediction of trust using sentiment analysis," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104549.
• Galil, Koresh & Hauptman, Ami & Rosenboim, Rosit Levy, 2023, "Prediction of corporate credit ratings with machine learning: Simple interpretative models," Finance Research Letters, Elsevier, volume 58, issue PD, DOI: 10.1016/j.frl.2023.104648.
• Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2023, "Climate risks and realized volatility of major commodity currency exchange rates," Journal of Financial Markets, Elsevier, volume 62, issue C, DOI: 10.1016/j.finmar.2022.100760.
• Alexandridis, Antonios K. & Apergis, Iraklis & Panopoulou, Ekaterini & Voukelatos, Nikolaos, 2023, "Equity premium prediction: The role of information from the options market," Journal of Financial Markets, Elsevier, volume 64, issue C, DOI: 10.1016/j.finmar.2022.100801.
• Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2023, "Climate risks and state-level stock market realized volatility," Journal of Financial Markets, Elsevier, volume 66, issue C, DOI: 10.1016/j.finmar.2023.100854.
• Engel, Charles & Wu, Steve Pak Yeung, 2023, "Forecasting the U.S. Dollar in the 21st Century," Journal of International Economics, Elsevier, volume 141, issue C, DOI: 10.1016/j.jinteco.2023.103715.
• Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023, "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, volume 145, issue C, DOI: 10.1016/j.jinteco.2023.103773.
• Dang, Ou & Feng, Mingbin & Hardy, Mary R., 2023, "Two-stage nested simulation of tail risk measurement: A likelihood ratio approach," Insurance: Mathematics and Economics, Elsevier, volume 108, issue C, pages 1-24, DOI: 10.1016/j.insmatheco.2022.10.002.
• Ugarte Montero, Andrey & Wagner, Joël, 2023, "On potential information asymmetries in long-term care insurance: A simulation study using data from Switzerland," Insurance: Mathematics and Economics, Elsevier, volume 111, issue C, pages 230-241, DOI: 10.1016/j.insmatheco.2023.04.003.
• Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2023, "Intergenerational actuarial fairness when longevity increases: Amending the retirement age," Insurance: Mathematics and Economics, Elsevier, volume 113, issue C, pages 161-184, DOI: 10.1016/j.insmatheco.2023.08.007.
• Li, Johnny Siu-Hang & Liu, Yanxin & Chan, Wai-Sum, 2023, "Hedging longevity risk under non-Gaussian state-space stochastic mortality models: A mean-variance-skewness-kurtosis approach," Insurance: Mathematics and Economics, Elsevier, volume 113, issue C, pages 96-121, DOI: 10.1016/j.insmatheco.2023.08.001.
• Naimoli, Antonio, 2023, "The information content of sentiment indices in forecasting Value at Risk and Expected Shortfall: a Complete Realized Exponential GARCH-X approach," International Economics, Elsevier, volume 176, issue C, DOI: 10.1016/j.inteco.2023.100459.
• Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023, "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 82, issue C, DOI: 10.1016/j.intfin.2022.101684.
• Liang, Chao & Luo, Qin & Li, Yan & Huynh, Luu Duc Toan, 2023, "Global financial stress index and long-term volatility forecast for international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 88, issue C, DOI: 10.1016/j.intfin.2023.101825.
• Liang, Chao & Huynh, Luu Duc Toan & Li, Yan, 2023, "Market momentum amplifies market volatility risk: Evidence from China’s equity market," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 88, issue C, DOI: 10.1016/j.intfin.2023.101856.
• Algaba, Andres & Borms, Samuel & Boudt, Kris & Verbeken, Brecht, 2023, "Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence," International Journal of Forecasting, Elsevier, volume 39, issue 1, pages 266-278, DOI: 10.1016/j.ijforecast.2021.11.005.
• Fortin, Alain-Philippe & Simonato, Jean-Guy & Dionne, Georges, 2023, "Forecasting expected shortfall: Should we use a multivariate model for stock market factors?," International Journal of Forecasting, Elsevier, volume 39, issue 1, pages 314-331, DOI: 10.1016/j.ijforecast.2021.11.010.
• Bańbura, Marta & Bobeica, Elena, 2023, "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, volume 39, issue 1, pages 364-390, DOI: 10.1016/j.ijforecast.2021.12.001.
• Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023, "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, volume 39, issue 2, pages 570-586, DOI: 10.1016/j.ijforecast.2022.01.003.
• Haase, Felix & Neuenkirch, Matthias, 2023, "Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US," International Journal of Forecasting, Elsevier, volume 39, issue 2, pages 587-605, DOI: 10.1016/j.ijforecast.2022.01.004.
• Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023, "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, volume 39, issue 2, pages 606-622, DOI: 10.1016/j.ijforecast.2022.01.005.
• Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023, "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, volume 39, issue 2, pages 841-868, DOI: 10.1016/j.ijforecast.2022.02.010.
• Olivares, Kin G. & Challu, Cristian & Marcjasz, Grzegorz & Weron, Rafał & Dubrawski, Artur, 2023, "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," International Journal of Forecasting, Elsevier, volume 39, issue 2, pages 884-900, DOI: 10.1016/j.ijforecast.2022.03.001.
• Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023, "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, volume 39, issue 3, pages 1145-1162, DOI: 10.1016/j.ijforecast.2022.04.009.
• Knotek, Edward S. & Zaman, Saeed, 2023, "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, volume 39, issue 4, pages 1736-1760, DOI: 10.1016/j.ijforecast.2022.04.007.
• Čapek, Jan & Crespo Cuaresma, Jesús & Hauzenberger, Niko & Reichel, Vlastimil, 2023, "Macroeconomic forecasting in the euro area using predictive combinations of DSGE models," International Journal of Forecasting, Elsevier, volume 39, issue 4, pages 1820-1838, DOI: 10.1016/j.ijforecast.2022.09.002.
• Magnus, Jan R. & Vasnev, Andrey L., 2023, "On the uncertainty of a combined forecast: The critical role of correlation," International Journal of Forecasting, Elsevier, volume 39, issue 4, pages 1895-1908, DOI: 10.1016/j.ijforecast.2022.10.002.
• Matsumoto, Akito & Pescatori, Andrea & Wang, Xueliang, 2023, "Commodity prices and global economic activity," Japan and the World Economy, Elsevier, volume 66, issue C, DOI: 10.1016/j.japwor.2023.101177.
• komaki, Yasuyuki, 2023, "Why is the forecast error of quarterly GDP in Japan so large? – From an international comparison of quarterly GDP forecast situation," Japan and the World Economy, Elsevier, volume 66, issue C, DOI: 10.1016/j.japwor.2023.101192.
• Oh, Hyungna & Lee, Jae Yoon & Jeong, Eunmi & Kim, Jee Young, 2023, "Simulated effects of carbon pricing on industrial sector energy use," Japan and the World Economy, Elsevier, volume 68, issue C, DOI: 10.1016/j.japwor.2023.101222.
• Krivorotov, George, 2023, "Machine learning-based profit modeling for credit card underwriting - implications for credit risk," Journal of Banking & Finance, Elsevier, volume 149, issue C, DOI: 10.1016/j.jbankfin.2023.106785.
• Arai, Natsuki, 2023, "The FOMC’s new individual economic projections and macroeconomic theories," Journal of Banking & Finance, Elsevier, volume 151, issue C, DOI: 10.1016/j.jbankfin.2023.106845.
• Lohmann, Christian & Möllenhoff, Steffen, 2023, "Dark premonitions: Pre-bankruptcy investor attention and behavior," Journal of Banking & Finance, Elsevier, volume 151, issue C, DOI: 10.1016/j.jbankfin.2023.106853.
• Ellwanger, Reinhard & Snudden, Stephen, 2023, "Forecasts of the real price of oil revisited: Do they beat the random walk?," Journal of Banking & Finance, Elsevier, volume 154, issue C, DOI: 10.1016/j.jbankfin.2023.106962.
• Du, Zaichao & Escanciano, Juan Carlos & Zhu, Guangwei, 2023, "The case for CASE: Estimating heterogeneous systemic effects," Journal of Banking & Finance, Elsevier, volume 157, issue C, DOI: 10.1016/j.jbankfin.2023.107022.
• Ferri, Piero & Cristini, Annalisa & Tramontana, Fabio, 2023, "Meta-models of the Phillips curve and income distribution," Journal of Economic Behavior & Organization, Elsevier, volume 213, issue C, pages 215-232, DOI: 10.1016/j.jebo.2023.07.020.
• Hall, Stephen G. & Tavlas, George S. & Wang, Yongli, 2023, "Drivers and spillover effects of inflation: The United States, the euro area, and the United Kingdom☆," Journal of International Money and Finance, Elsevier, volume 131, issue C, DOI: 10.1016/j.jimonfin.2022.102776.
• Boucher, C. & Jasinski, A. & Tokpavi, S., 2023, "Conditional mean reversion of financial ratios and the predictability of returns," Journal of International Money and Finance, Elsevier, volume 137, issue C, DOI: 10.1016/j.jimonfin.2023.102907.
• Glas, Alexander & Heinisch, Katja, 2023, "Conditional macroeconomic survey forecasts: Revisions and errors," Journal of International Money and Finance, Elsevier, volume 138, issue C, DOI: 10.1016/j.jimonfin.2023.102927.
• Tafuro, Andrea, 2023, "Labour market rigidity and expansionary austerity," Journal of Macroeconomics, Elsevier, volume 75, issue C, DOI: 10.1016/j.jmacro.2022.103495.
• Demirel, Ufuk Devrim & Otterson, James, 2023, "Quantifying the uncertainty of long-term macroeconomic projections," Journal of Macroeconomics, Elsevier, volume 75, issue C, DOI: 10.1016/j.jmacro.2023.103501.
• Arin, K. Peren & Devereux, Kevin & Mazur, Mieszko, 2023, "Taxes and firm investment," Journal of Macroeconomics, Elsevier, volume 76, issue C, DOI: 10.1016/j.jmacro.2023.103517.
• Garcia, Pablo & Jacquinot, Pascal & Lenarčič, Črt & Lozej, Matija & Mavromatis, Kostas, 2023, "Global models for a global pandemic: The impact of COVID-19 on small euro area economies," Journal of Macroeconomics, Elsevier, volume 77, issue C, DOI: 10.1016/j.jmacro.2023.103551.
• Schade, Philipp & Schuhmacher, Monika C., 2023, "Predicting entrepreneurial activity using machine learning," Journal of Business Venturing Insights, Elsevier, volume 19, issue C, DOI: 10.1016/j.jbvi.2022.e00357.
• Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023, "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, volume 30, issue C, DOI: 10.1016/j.jcomm.2022.100285.
• Salisu, Afees A. & Adediran, Idris & Omoke, Philip C. & Tchankam, Jean Paul, 2023, "Gold and tail risks," Resources Policy, Elsevier, volume 80, issue C, DOI: 10.1016/j.resourpol.2022.103154.
• Swamy, Vighneswara & Lagesh, M.A., 2023, "Does happy Twitter forecast gold price?," Resources Policy, Elsevier, volume 81, issue C, DOI: 10.1016/j.resourpol.2023.103299.
• Fasanya, Ismail O. & Oyewole, Oluwatomisin J., 2023, "On the connection between international REITs and oil markets: The role of economic policy uncertainty," Resources Policy, Elsevier, volume 81, issue C, DOI: 10.1016/j.resourpol.2023.103335.
• Salisu, Afees A. & Ndako, Umar B. & Vo, Xuan Vinh, 2023, "Transition risk, physical risk, and the realized volatility of oil and natural gas prices," Resources Policy, Elsevier, volume 81, issue C, DOI: 10.1016/j.resourpol.2023.103383.
• Salisu, Afees A. & Ndako, Umar B. & Vo, Xuan Vinh, 2023, "Oil price and the Bitcoin market," Resources Policy, Elsevier, volume 82, issue C, DOI: 10.1016/j.resourpol.2023.103437.
• Karmakar, Sayar & Gupta, Rangan & Cepni, Oguzhan & Rognone, Lavinia, 2023, "Climate risks and predictability of the trading volume of gold: Evidence from an INGARCH model," Resources Policy, Elsevier, volume 82, issue C, DOI: 10.1016/j.resourpol.2023.103438.
• Salisu, Afees A. & Ogbonna, Ahamuefula E. & Vo, Xuan Vinh, 2023, "Oil tail risks and the realized variance of consumer prices in advanced economies," Resources Policy, Elsevier, volume 83, issue C, DOI: 10.1016/j.resourpol.2023.103755.
• Ayinde, Taofeek O. & Olaniran, Abeeb O. & Abolade, Onomeabure C. & Ogbonna, Ahamuefula Ephraim, 2023, "Technology shocks - Gold market connection: Is the effect episodic to business cycle behaviour?," Resources Policy, Elsevier, volume 84, issue C, DOI: 10.1016/j.resourpol.2023.103771.
• Peng, Lijuan & Liang, Chao, 2023, "Sustainable development during the post-COVID-19 period: Role of crude oil," Resources Policy, Elsevier, volume 85, issue PA, DOI: 10.1016/j.resourpol.2023.103843.
• Liu, Zhenya & Teka, Hanen & You, Rongyu, 2023, "Conditional autoencoder pricing model for energy commodities," Resources Policy, Elsevier, volume 86, issue PA, DOI: 10.1016/j.resourpol.2023.104060.
• Hardy, Nicolás & Ferreira, Tiago & Quinteros, Maria J. & Magner, Nicolás S., 2023, "“Watch your tone!”: Forecasting mining industry commodity prices with financial report tone," Resources Policy, Elsevier, volume 86, issue PA, DOI: 10.1016/j.resourpol.2023.104251.
• Andres–Escayola, Erik & Berganza, Juan Carlos & Campos, Rodolfo G. & Molina, Luis, 2023, "A BVAR toolkit to assess macrofinancial risks in Brazil and Mexico," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 4, issue 1, DOI: 10.1016/j.latcb.2022.100079.
• Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023, "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 4, issue 2, DOI: 10.1016/j.latcb.2023.100087.
• Gómez-Puig, Marta & Pieterse-Bloem, Mary & Sosvilla-Rivero, Simón, 2023, "Dynamic connectedness between credit and liquidity risks in euro area sovereign debt markets," Journal of Multinational Financial Management, Elsevier, volume 68, issue C, DOI: 10.1016/j.mulfin.2023.100800.
• Lu, Yueliang (Jacques) & Tian, Weidong, 2023, "An on-line machine learning return prediction," Pacific-Basin Finance Journal, Elsevier, volume 79, issue C, DOI: 10.1016/j.pacfin.2023.102049.
• Umar, Zaghum & Riaz, Yasir & Shahab, Yasir & Teplova, Tamara, 2023, "Network connectedness of the term structure of yield curve and global Sukuks," Pacific-Basin Finance Journal, Elsevier, volume 80, issue C, DOI: 10.1016/j.pacfin.2023.102056.
• Narayan, Shivani & Kumar, Dilip & Bouri, Elie, 2023, "Systemically important financial institutions and drivers of systemic risk: Evidence from India," Pacific-Basin Finance Journal, Elsevier, volume 82, issue C, DOI: 10.1016/j.pacfin.2023.102155.
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• Cronin, David & McInerney, Niall, 2023, "Official fiscal forecasts in EU member states under the European Semester and Fiscal Compact – An empirical assessment," European Journal of Political Economy, Elsevier, volume 76, issue C, DOI: 10.1016/j.ejpoleco.2022.102227.
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• Zhang, Li & Wang, Lu & Peng, Lijuan & Luo, Keyu, 2023, "Measuring the response of clean energy stock price volatility to extreme shocks," Renewable Energy, Elsevier, volume 206, issue C, pages 1289-1300, DOI: 10.1016/j.renene.2023.02.066.
• Yao, Youfu & Hong, Yun, 2023, "Can comment letters impact excess cash holdings? Evidence from China," International Review of Economics & Finance, Elsevier, volume 83, issue C, pages 900-922, DOI: 10.1016/j.iref.2022.11.003.
• Hong, Yanran & Yu, Jize & Su, Yuquan & Wang, Lu, 2023, "Southern oscillation: Great value of its trends for forecasting crude oil spot price volatility," International Review of Economics & Finance, Elsevier, volume 84, issue C, pages 358-368, DOI: 10.1016/j.iref.2022.11.023.
• Chen, Zhonglu & Zhang, Li & Weng, Chen, 2023, "Does climate policy uncertainty affect Chinese stock market volatility?," International Review of Economics & Finance, Elsevier, volume 84, issue C, pages 369-381, DOI: 10.1016/j.iref.2022.11.030.
• Liu, Xiaoqun & Zhang, Yuchen & Tian, Mengqiao & Chao, Youcong, 2023, "Financial distress and jump tail risk: Evidence from China's listed companies," International Review of Economics & Finance, Elsevier, volume 85, issue C, pages 316-336, DOI: 10.1016/j.iref.2023.01.007.
• Li, Zepei & Huang, Haizhen, 2023, "Challenges for volatility forecasts of US fossil energy spot markets during the COVID-19 crisis," International Review of Economics & Finance, Elsevier, volume 86, issue C, pages 31-45, DOI: 10.1016/j.iref.2023.02.004.
• Wu, Xinyu & He, Qizhi & Xie, Haibin, 2023, "Forecasting VIX with time-varying risk aversion," International Review of Economics & Finance, Elsevier, volume 88, issue C, pages 458-475, DOI: 10.1016/j.iref.2023.06.034.
• Song, Yixuan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2023, "Forecasting crude oil prices: A reduced-rank approach," International Review of Economics & Finance, Elsevier, volume 88, issue C, pages 698-711, DOI: 10.1016/j.iref.2023.07.001.
• Pirayesh Neghab, Davood & Bradrania, Reza & Elliott, Robert, 2023, "Deliberate premarket underpricing: New evidence on IPO pricing using machine learning," International Review of Economics & Finance, Elsevier, volume 88, issue C, pages 902-927, DOI: 10.1016/j.iref.2023.07.008.
• Li, Xiafei & Guo, Qiang & Liang, Chao & Umar, Muhammad, 2023, "Forecasting gold volatility with geopolitical risk indices," Research in International Business and Finance, Elsevier, volume 64, issue C, DOI: 10.1016/j.ribaf.2022.101857.
• Liu, Yujun & Li, Zhongfei & Nekhili, Ramzi & Sultan, Jahangir, 2023, "Forecasting cryptocurrency returns with machine learning," Research in International Business and Finance, Elsevier, volume 64, issue C, DOI: 10.1016/j.ribaf.2023.101905.
• González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023, "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, volume 64, issue C, DOI: 10.1016/j.ribaf.2023.101907.
• Ghosh, Bikramaditya & Bouri, Elie & Wee, Jung Bum & Zulfiqar, Noshaba, 2023, "Return and volatility properties: Stylized facts from the universe of cryptocurrencies and NFTs," Research in International Business and Finance, Elsevier, volume 65, issue C, DOI: 10.1016/j.ribaf.2023.101945.
• Bouras, Christos & Christou, Christina & Gupta, Rangan & Lesame, Keagile, 2023, "Forecasting state- and MSA-level housing returns of the US: The role of mortgage default risks," Research in International Business and Finance, Elsevier, volume 65, issue C, DOI: 10.1016/j.ribaf.2023.101952.
• He, Mengxi & Wang, Yudong & Zeng, Qing & Zhang, Yaojie, 2023, "Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index," Research in International Business and Finance, Elsevier, volume 65, issue C, DOI: 10.1016/j.ribaf.2023.101983.
• Duan, Kun & Wang, Rui & Chen, Shun & Ge, Lei, 2023, "Exploring the predictability of attention mechanism with LSTM: Evidence from EU carbon futures prices," Research in International Business and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.ribaf.2023.102020.
• Grudniewicz, Jan & Ślepaczuk, Robert, 2023, "Application of machine learning in algorithmic investment strategies on global stock markets," Research in International Business and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.ribaf.2023.102052.
• Li, Zhao-Chen & Xie, Chi & Wang, Gang-Jin & Zhu, You & Long, Jian-You & Zhou, Yang, 2023, "Forecasting stock market volatility under parameter and model uncertainty," Research in International Business and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.ribaf.2023.102084.
• Bitetto, Alessandro & Cerchiello, Paola & Filomeni, Stefano & Tanda, Alessandra & Tarantino, Barbara, 2023, "Machine learning and credit risk: Empirical evidence from small- and mid-sized businesses," Socio-Economic Planning Sciences, Elsevier, volume 90, issue C, DOI: 10.1016/j.seps.2023.101746.
• Mitze, Timo & Makkonen, Teemu, 2023, "Can large-scale RDI funding stimulate post-crisis recovery growth? Evidence for Finland during COVID-19," Technological Forecasting and Social Change, Elsevier, volume 186, issue PB, DOI: 10.1016/j.techfore.2022.122073.
• Miguel Herculano & Punnoose Jacob, 2023, "Financial Condition Indices in an Incomplete Data Environment," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2023-42, Aug.
• Leo Krippner, 2023, "Estimating and Applying Autoregression Models via Their Eigensystem Representation," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2023-47, Oct.
• Roshen Fernando & Caterina Lepore, 2023, "Global Economic Impacts of Physical Climate Risks," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2023-50, Oct.
• Felix Chan & Laurent Pauwels, 2023, "Optimal Forecast Combination with Mean Absolute Error Loss," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2023-59, Nov.
• Roshen Fernando, 2023, "Impact of Demographic Trends on Antimicrobial Resistance," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2023-60, Nov.
• Roshen Fernando, 2023, "Impact of Physical Climate Risks on Antimicrobial Resistance," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2023-61, Nov.
• Osmar Bolivar & Christian Huanto, 2023, "Geopolitical Risk Shocks: Macroeconomic Effects in Bolivia, Chile, and Peru," Cuadernos de Investigación Económica Boliviana, Ministerio de Economía y Finanzas Públicas de Bolivia, volume 6, issue 2, pages 62-83, December.
• Zhang, Ning & Gong, Yujing & Xue, Xiaohan, 2023, "Less disagreement, better forecasts: adjusted risk measures in the energy futures market," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 118451, Oct.
• Ibarra, Raul, 2023, "The yield spread as a predictor of economic activity in Mexico: the role of the term premium," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 120717, Oct.
• Julia del Amo Valor & Marcos Martín Mateos & Javier J. Pérez, 2023, "La evaluación de las políticas públicas desde una perspectiva macroeconómica en el contexto europeo," EKONOMIAZ. Revista vasca de Economía, Gobierno Vasco / Eusko Jaurlaritza / Basque Government, volume 103, issue 01, pages 285-299.
• Alain Hecq & Elisa Voisin, 2023, "Predicting Crashes in Oil Prices During The Covid-19 Pandemic with Mixed Causal-Noncausal Models," Advances in Econometrics, Emerald Group Publishing Limited, "Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications", DOI: 10.1108/S0731-90532023000045B010.
• 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), Economic 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.
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• 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.
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• 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.
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• 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 Качества Прогнозов Цен На Природные Ресурсы Методами К," 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
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• Konstantin S. Rybak, 2023, "Evaluating the Role of Global Factors in GDP Nowcasting
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• 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.
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• 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.
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• 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.
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