Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2023

• Arango-Castillo, Lenin & Orraca, María José & Molina, G. Stefano, 2023, "The global component of headline and core inflation in emerging market economies and its ability to improve forecasting performance," Economic Modelling, Elsevier, volume 120, issue C, DOI: 10.1016/j.econmod.2022.106121.
• Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2023, "Are low frequency macroeconomic variables important for high frequency electricity prices?," Economic Modelling, Elsevier, volume 120, issue C, DOI: 10.1016/j.econmod.2022.106160.
• Ojeda-Joya, Jair & Romero, José Vicente, 2023, "Global uncertainty shocks and exchange-rate expectations in Latin America," Economic Modelling, Elsevier, volume 120, issue C, DOI: 10.1016/j.econmod.2022.106185.
• Yu, Deshui & Huang, Difang & Chen, Li & Li, Luyang, 2023, "Forecasting dividend growth: The role of adjusted earnings yield," Economic Modelling, Elsevier, volume 120, issue C, DOI: 10.1016/j.econmod.2022.106188.
• Yu, Dan & Chen, Chuang & Wang, Yudong & Zhang, Yaojie, 2023, "Hedging pressure momentum and the predictability of oil futures returns," Economic Modelling, Elsevier, volume 121, issue C, DOI: 10.1016/j.econmod.2023.106214.
• Zhang, Qin & Ni, He & Xu, Hao, 2023, "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, volume 122, issue C, DOI: 10.1016/j.econmod.2023.106204.
• Zhao, Shangwei & Xie, Tian & Ai, Xin & Yang, Guangren & Zhang, Xinyu, 2023, "Correcting sample selection bias with model averaging for consumer demand forecasting," Economic Modelling, Elsevier, volume 123, issue C, DOI: 10.1016/j.econmod.2023.106275.
• Qiu, Yue & Zheng, Yuchen, 2023, "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, volume 125, issue C, DOI: 10.1016/j.econmod.2023.106349.
• McKibbin, Warwick & Fernando, Roshen, 2023, "The global economic impacts of the COVID-19 pandemic," Economic Modelling, Elsevier, volume 129, issue C, DOI: 10.1016/j.econmod.2023.106551.
• Hambuckers, J. & Ulm, M., 2023, "On the role of interest rate differentials in the dynamic asymmetry of exchange rates," Economic Modelling, Elsevier, volume 129, issue C, DOI: 10.1016/j.econmod.2023.106554.
• Karaki, Mohamad B. & Rangaraju, Sandeep Kumar, 2023, "The confidence channel of U.S. financial uncertainty: Evidence from industry-level data," Economic Modelling, Elsevier, volume 129, issue C, DOI: 10.1016/j.econmod.2023.106557.
• Yan, Wan-Lin, 2023, "Stock index futures price prediction using feature selection and deep learning," The North American Journal of Economics and Finance, Elsevier, volume 64, issue C, DOI: 10.1016/j.najef.2022.101867.
• Li, Houjian & Zhou, Deheng & Hu, Jiayu & Li, Junwen & Su, Mengying & Guo, Lili, 2023, "Forecasting the realized volatility of Energy Stock Market: A multimodel comparison," The North American Journal of Economics and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.najef.2023.101895.
• Wu, Xinyu & Zhao, An & Liu, Li, 2023, "Forecasting VIX using two-component realized EGARCH model," The North American Journal of Economics and Finance, Elsevier, volume 67, issue C, DOI: 10.1016/j.najef.2023.101934.
• Wu, Xinyu & Yin, Xuebao & Umar, Zaghum & Iqbal, Najaf, 2023, "Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach," The North American Journal of Economics and Finance, Elsevier, volume 67, issue C, DOI: 10.1016/j.najef.2023.101948.
• Caiado, Jorge & Lúcio, Francisco, 2023, "Stock market forecasting accuracy of asymmetric GARCH models during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, volume 68, issue C, DOI: 10.1016/j.najef.2023.101971.
• Cepni, Oguzhan & Christou, Christina & Gupta, Rangan, 2023, "Forecasting national recessions of the United States with state-level climate risks: Evidence from model averaging in Markov-switching models," Economics Letters, Elsevier, volume 227, issue C, DOI: 10.1016/j.econlet.2023.111121.
• Winkelried, Diego, 2023, "Simple interpolations of inflation expectations," Economics Letters, Elsevier, volume 229, issue C, DOI: 10.1016/j.econlet.2023.111230.
• Cavicchioli, Maddalena, 2023, "Impulse response function analysis for Markov switching var models," Economics Letters, Elsevier, volume 232, issue C, DOI: 10.1016/j.econlet.2023.111357.
• Huber, Florian & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2023, "Nowcasting in a pandemic using non-parametric mixed frequency VARs," Journal of Econometrics, Elsevier, volume 232, issue 1, pages 52-69, DOI: 10.1016/j.jeconom.2020.11.006.
• Hounyo, Ulrich & Lahiri, Kajal, 2023, "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, volume 232, issue 2, pages 445-468, DOI: 10.1016/j.jeconom.2021.09.011.
• Ding, Yashuang (Dexter), 2023, "A simple joint model for returns, volatility and volatility of volatility," Journal of Econometrics, Elsevier, volume 232, issue 2, pages 521-543, DOI: 10.1016/j.jeconom.2021.09.012.
• Blasques, F. & Francq, Christian & Laurent, Sébastien, 2023, "Quasi score-driven models," Journal of Econometrics, Elsevier, volume 234, issue 1, pages 251-275, DOI: 10.1016/j.jeconom.2021.12.005.
• Arias, Jonas E. & Rubio-Ramírez, Juan F. & Shin, Minchul, 2023, "Macroeconomic forecasting and variable ordering in multivariate stochastic volatility models," Journal of Econometrics, Elsevier, volume 235, issue 2, pages 1054-1086, DOI: 10.1016/j.jeconom.2022.04.013.
• Lee, Ji Hyung & Park, Byoung G., 2023, "Nonparametric identification and estimation of the extended Roy model," Journal of Econometrics, Elsevier, volume 235, issue 2, pages 1087-1113, DOI: 10.1016/j.jeconom.2022.10.001.
• Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023, "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, volume 235, issue 2, pages 1355-1377, DOI: 10.1016/j.jeconom.2022.09.007.
• Abadir, Karim M. & Luati, Alessandra & Paruolo, Paolo, 2023, "GARCH density and functional forecasts," Journal of Econometrics, Elsevier, volume 235, issue 2, pages 470-483, DOI: 10.1016/j.jeconom.2022.04.010.
• Bennedsen, Mikkel & Lunde, Asger & Shephard, Neil & Veraart, Almut E.D., 2023, "Inference and forecasting for continuous-time integer-valued trawl processes," Journal of Econometrics, Elsevier, volume 236, issue 2, DOI: 10.1016/j.jeconom.2023.105476.
• Diebold, Francis X. & Rudebusch, Glenn D. & Göbel, Maximilian & Goulet Coulombe, Philippe & Zhang, Boyuan, 2023, "When will Arctic sea ice disappear? Projections of area, extent, thickness, and volume," Journal of Econometrics, Elsevier, volume 236, issue 2, DOI: 10.1016/j.jeconom.2023.105479.
• Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023, "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, volume 236, issue 2, DOI: 10.1016/j.jeconom.2023.105490.
• Tu, Yundong & Xie, Xinling, 2023, "Penetrating sporadic return predictability," Journal of Econometrics, Elsevier, volume 237, issue 1, DOI: 10.1016/j.jeconom.2023.105509.
• Andersen, Torben G. & Li, Yingying & Todorov, Viktor & Zhou, Bo, 2023, "Volatility measurement with pockets of extreme return persistence," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2020.11.005.
• Aknouche, Abdelhakim & Francq, Christian, 2023, "Two-stage weighted least squares estimator of the conditional mean of observation-driven time series models," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2021.09.002.
• Odendahl, Florens & Rossi, Barbara & Sekhposyan, Tatevik, 2023, "Evaluating forecast performance with state dependence," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2021.07.015.
• Berrisch, Jonathan & Ziel, Florian, 2023, "CRPS learning," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2021.11.008.
• Zhang, Xiaomeng & Zhang, Xinyu, 2023, "Optimal model averaging based on forward-validation," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2022.03.010.
• Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2023, "A flexible predictive density combination for large financial data sets in regular and crisis periods," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2022.11.004.
• Fan, Rui & Lee, Ji Hyung & Shin, Youngki, 2023, "Predictive quantile regression with mixed roots and increasing dimensions: The ALQR approach," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2022.11.006.
• Cheng, Mingmian & Liao, Yuan & Yang, Xiye, 2023, "Uniform predictive inference for factor models with instrumental and idiosyncratic betas," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2022.11.007.
• Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023, "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2022.12.004.
• Huang, Dashan & Jiang, Fuwei & Li, Kunpeng & Tong, Guoshi & Zhou, Guofu, 2023, "Are bond returns predictable with real-time macro data?," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2022.09.008.
• Lippi, Marco & Deistler, Manfred & Anderson, Brian, 2023, "High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research," Econometrics and Statistics, Elsevier, volume 26, issue C, pages 3-16, DOI: 10.1016/j.ecosta.2022.03.008.
• Hallin, Marc & Trucíos, Carlos, 2023, "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, volume 27, issue C, pages 1-15, DOI: 10.1016/j.ecosta.2021.04.006.
• Crucitti, Francesca & Lazarou, Nicholas-Joseph & Monfort, Philippe & Salotti, Simone, 2023, "Where does the EU cohesion policy produce its benefits? A model analysis of the international spillovers generated by the policy," Economic Systems, Elsevier, volume 47, issue 3, DOI: 10.1016/j.ecosys.2023.101076.
• Borrotti, Matteo & Rabasco, Michele & Santoro, Alessandro, 2023, "Using accounting information to predict aggressive tax location decisions by European groups," Economic Systems, Elsevier, volume 47, issue 3, DOI: 10.1016/j.ecosys.2023.101090.
• Zakharenko, Roman, 2023, "Pricing shared vehicles," Economics of Transportation, Elsevier, volume 33, issue C, DOI: 10.1016/j.ecotra.2022.100296.
• Dibiasi, Andreas & Sarferaz, Samad, 2023, "Measuring macroeconomic uncertainty: A cross-country analysis," European Economic Review, Elsevier, volume 153, issue C, DOI: 10.1016/j.euroecorev.2023.104383.
• Khalfaoui, Rabeh & Hammoudeh, Shawkat & Rehman, Mohd Ziaur, 2023, "Spillovers and connectedness among BRICS stock markets, cryptocurrencies, and uncertainty: Evidence from the quantile vector autoregression network," Emerging Markets Review, Elsevier, volume 54, issue C, DOI: 10.1016/j.ememar.2023.101002.
• Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023, "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, volume 70, issue C, pages 144-164, DOI: 10.1016/j.jempfin.2022.12.001.
• Nonejad, Nima, 2023, "Conditional out-of-sample predictability of aggregate equity returns and aggregate equity return volatility using economic variables," Journal of Empirical Finance, Elsevier, volume 70, issue C, pages 91-122, DOI: 10.1016/j.jempfin.2022.11.009.
• Ferrer Fernández, María & Henry, Ólan & Pybis, Sam & Stamatogiannis, Michalis P., 2023, "Can we forecast better in periods of low uncertainty? The role of technical indicators," Journal of Empirical Finance, Elsevier, volume 71, issue C, pages 1-12, DOI: 10.1016/j.jempfin.2022.12.014.
• Yu, Deshui & Huang, Difang & Chen, Li, 2023, "Stock return predictability and cyclical movements in valuation ratios," Journal of Empirical Finance, Elsevier, volume 72, issue C, pages 36-53, DOI: 10.1016/j.jempfin.2023.02.004.
• Wang, Keli & Liu, Xiaoquan & Ye, Wuyi, 2023, "Intraday VaR: A copula-based approach," Journal of Empirical Finance, Elsevier, volume 74, issue C, DOI: 10.1016/j.jempfin.2023.101419.
• Souropanis, Ioannis & Vivian, Andrew, 2023, "Forecasting realized volatility with wavelet decomposition," Journal of Empirical Finance, Elsevier, volume 74, issue C, DOI: 10.1016/j.jempfin.2023.101432.
• Degiannakis, Stavros & Filis, George, 2023, "Oil price assumptions for macroeconomic policy," Energy Economics, Elsevier, volume 117, issue C, DOI: 10.1016/j.eneco.2022.106425.
• Blazsek, Szabolcs & Escribano, Alvaro, 2023, "Score-driven threshold ice-age models: Benchmark models for long-run climate forecasts," Energy Economics, Elsevier, volume 118, issue C, DOI: 10.1016/j.eneco.2023.106522.
• Galarneau-Vincent, Rémi & Gauthier, Geneviève & Godin, Frédéric, 2023, "Foreseeing the worst: Forecasting electricity DART spikes," Energy Economics, Elsevier, volume 119, issue C, DOI: 10.1016/j.eneco.2023.106521.
• Qu, Hui & Li, Guo, 2023, "Multi-perspective investor attention and oil futures volatility forecasting," Energy Economics, Elsevier, volume 119, issue C, DOI: 10.1016/j.eneco.2023.106531.
• Li, Jingpeng & Umar, Muhammad & Huo, Jiale, 2023, "The spillover effect between Chinese crude oil futures market and Chinese green energy stock market," Energy Economics, Elsevier, volume 119, issue C, DOI: 10.1016/j.eneco.2023.106568.
• Sohag, Kazi & Hassan, M. Kabir & Bakhteyev, Stepan & Mariev, Oleg, 2023, "Do green and dirty investments hedge each other?," Energy Economics, Elsevier, volume 120, issue C, DOI: 10.1016/j.eneco.2023.106573.
• 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.
• Belhadj, Besma, 2023, "New fuzzy multiple regressions for the instantaneous and panel data “The determinants of Poverty in the Countries MENA”," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 615, issue C, DOI: 10.1016/j.physa.2023.128565.
• 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.
• Demirer, Riza & Gupta, Rangan & Salisu, Afees A. & van Eyden, Reneé, 2023, "Firm-level business uncertainty and the predictability of the aggregate U.S. stock market volatility during the COVID-19 pandemic," The Quarterly Review of Economics and Finance, Elsevier, volume 88, issue C, pages 295-302, DOI: 10.1016/j.qref.2023.02.002.
• Salisu, Afees A. & Gupta, Rangan & Bouri, Elie, 2023, "Testing the forecasting power of global economic conditions for the volatility of international REITs using a GARCH-MIDAS approach," The Quarterly Review of Economics and Finance, Elsevier, volume 88, issue C, pages 303-314, DOI: 10.1016/j.qref.2023.02.004.
• 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.