Macroeconomic information, global economic policy uncertainty and gold futures return predictability
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DOI: 10.1016/j.frl.2023.103789
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- Ivo Welch & Amit Goyal, 2008.
"A Comprehensive Look at The Empirical Performance of Equity Premium Prediction,"
The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
- Amit Goyal & Ivo Welch, 2004. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction," Yale School of Management Working Papers amz2412, Yale School of Management, revised 01 Jan 2006.
- Amit Goyal & Ivo Welch & Athanasse Zafirov, 2021. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction II," Swiss Finance Institute Research Paper Series 21-85, Swiss Finance Institute.
- Amit Goval & Ivo Welch, 2004. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction," NBER Working Papers 10483, National Bureau of Economic Research, Inc.
- Wu, Shan & Tong, Mu & Yang, Zhongyi & Derbali, Abdelkader, 2019. "Does gold or Bitcoin hedge economic policy uncertainty?," Finance Research Letters, Elsevier, vol. 31(C), pages 171-178.
- Simon Huang, 2022. "The Momentum Gap and Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 35(7), pages 3303-3336.
- Salisu, Afees A. & Raheem, Ibrahim D. & Vo, Xuan Vinh, 2021.
"Assessing the safe haven property of the gold market during COVID-19 pandemic,"
International Review of Financial Analysis, Elsevier, vol. 74(C).
- Salisu, Afees & Raheem, Ibrahim & Vo, Xuan, 2021. "Assessing the safe haven property of the gold market during COVID-19 pandemic," MPRA Paper 105353, University Library of Munich, Germany.
- Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021. "Corrigendum: Bond Risk Premiums with Machine Learning [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1090-1103.
- Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2014.
"Forecasting stock returns under economic constraints,"
Journal of Financial Economics, Elsevier, vol. 114(3), pages 517-553.
- Timmermann, Allan & Pettenuzzo, Davide & Valkanov, Rossen, 2013. "Forecasting Stock Returns under Economic Constraints," CEPR Discussion Papers 9377, C.E.P.R. Discussion Papers.
- Davide Pettenuzzo & Allan Timmermann & Rossen Valkanov, 2013. "Forecasting Stock Returns under Economic Constraints," Working Papers 57, Brandeis University, Department of Economics and International Business School.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
- Mensi, Walid & Sensoy, Ahmet & Vo, Xuan Vinh & Kang, Sang Hoon, 2020. "Impact of COVID-19 outbreak on asymmetric multifractality of gold and oil prices," Resources Policy, Elsevier, vol. 69(C).
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020.
"Empirical Asset Pricing via Machine Learning,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
- Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
- Bouoiyour, Jamal & Selmi, Refk & Wohar, Mark E., 2018.
"Measuring the response of gold prices to uncertainty: An analysis beyond the mean,"
Economic Modelling, Elsevier, vol. 75(C), pages 105-116.
- Jamal Bouoiyour & Refk Selmi & Mark Wohar, 2018. "Measuring the response of gold prices to uncertainty: An analysis beyond the mean," Post-Print hal-01817067, HAL.
- Jamal Bouoiyour & Refk Selmi & Mark Wohar, 2018. "Measuring the response of gold prices to uncertainty: An analysis beyond the mean," Papers 1806.07623, arXiv.org.
- Libing Fang & Baizhu Chen & Honghai Yu & Yichuo Qian, 2018. "The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH‐MIDAS approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 413-422, March.
- Balcilar, Mehmet & Gupta, Rangan & Pierdzioch, Christian, 2016.
"Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test,"
Resources Policy, Elsevier, vol. 49(C), pages 74-80.
- Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2015. "Does Uncertainty Move the Gold Price? New Evidence from a Nonparametric Causality-in-Quantiles Test," Working Papers 201592, University of Pretoria, Department of Economics.
- Raza, Syed Ali & Shah, Nida & Shahbaz, Muhammad, 2018. "Does economic policy uncertainty influence gold prices? Evidence from a nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 57(C), pages 61-68.
- Newey, Whitney K & West, Kenneth D, 1987. "Hypothesis Testing with Efficient Method of Moments Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 28(3), pages 777-787, October.
- Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021. "Bond Risk Premiums with Machine Learning [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1046-1089.
- Dangl, Thomas & Halling, Michael, 2012. "Predictive regressions with time-varying coefficients," Journal of Financial Economics, Elsevier, vol. 106(1), pages 157-181.
- Steven J. Davis, 2016. "An Index of Global Economic Policy Uncertainty," NBER Working Papers 22740, National Bureau of Economic Research, Inc.
- Wen, Fenghua & Tong, Xi & Ren, Xiaohang, 2022. "Gold or Bitcoin, which is the safe haven during the COVID-19 pandemic?," International Review of Financial Analysis, Elsevier, vol. 81(C).
- Ma, Feng & Lu, Fei & Tao, Ying, 2022. "Geopolitical risk and excess stock returns predictability: New evidence from a century of data," Finance Research Letters, Elsevier, vol. 50(C).
- Huynh, Thanh D. & Xia, Ying, 2021. "Climate Change News Risk and Corporate Bond Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 56(6), pages 1985-2009, September.
- Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier, 2021. "Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1070-1085, September.
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Keywords
Gold futures excess returns; Macroeconomic variables; Global economic policy uncertainty; Out-of-sample evaluation;All these keywords.
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