How do stock prices respond to the leading economic indicators? Analysis of large and small shocks
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DOI: 10.1016/j.frl.2022.103430
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- Corsi, Fulvio & Pirino, Davide & Renò, Roberto, 2010.
"Threshold bipower variation and the impact of jumps on volatility forecasting,"
Journal of Econometrics, Elsevier, vol. 159(2), pages 276-288, December.
- Fulvio Corsi & Davide Pirino & Roberto Reno', 2010. "Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting," LEM Papers Series 2010/11, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Fulvio Corsi & Davide Pirino & Roberto Renò, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Post-Print hal-00741630, HAL.
- Valeriy Gavrishchaka & Supriya Banerjee, 2006. "Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting," Computational Management Science, Springer, vol. 3(2), pages 147-160, April.
- Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
- Dimson, Elroy & Marsh, Paul, 1990. "Volatility forecasting without data-snooping," Journal of Banking & Finance, Elsevier, vol. 14(2-3), pages 399-421, August.
- 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.
- Mei, Dexiang & Ma, Feng & Liao, Yin & Wang, Lu, 2020. "Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models," Energy Economics, Elsevier, vol. 86(C).
- repec:hal:journl:peer-00741630 is not listed on IDEAS
- Li, Yan & Liang, Chao & Ma, Feng & Wang, Jiqian, 2020. "The role of the IDEMV in predicting European stock market volatility during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 36(C).
- Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
- Liu, Jing & Ma, Feng & Tang, Yingkai & Zhang, Yaojie, 2019. "Geopolitical risk and oil volatility: A new insight," Energy Economics, Elsevier, vol. 84(C).
- Peter R. Hansen & Asger Lunde & James M. Nason, 2011.
"The Model Confidence Set,"
Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2010. "The Model Confidence Set," CREATES Research Papers 2010-76, Department of Economics and Business Economics, Aarhus University.
- Zhifeng Dai & Tingyu Li & Mi Yang, 2022. "Forecasting stock return volatility: The role of shrinkage approaches in a data‐rich environment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 980-996, August.
- Christian Conrad & Onno Kleen, 2020. "Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 19-45, January.
- Gonzalo, Jesus & Martinez, Oscar, 2006. "Large shocks vs. small shocks. (Or does size matter? May be so.)," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 311-347.
- Clark, Todd E. & West, Kenneth D., 2007.
"Approximately normal tests for equal predictive accuracy in nested models,"
Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
- Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
- Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
- Dejun Xie & Yu Cui & Yujian Liu, 2021. "How does investor sentiment impact stock volatility? New evidence from Shanghai A-shares market," China Finance Review International, Emerald Group Publishing Limited, vol. 13(1), pages 102-120, May.
- Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
- Ma, Feng & Guo, Yangli & Chevallier, Julien & Huang, Dengshi, 2022. "Macroeconomic attention, economic policy uncertainty, and stock volatility predictability," International Review of Financial Analysis, Elsevier, vol. 84(C).
- Xiao, Jihong & Chen, Xian & Li, Yang & Wen, Fenghua, 2022. "Oil price uncertainty and stock price crash risk: Evidence from China," Energy Economics, Elsevier, vol. 112(C).
- Chkili, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014.
"Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory,"
Energy Economics, Elsevier, vol. 41(C), pages 1-18.
- Walid Chkili & Shawkat Hammoudeh & Duc Khuong Nguyen, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Working Papers 2014-389, Department of Research, Ipag Business School.
- Walid Chkili & Shawkat Hammoudeh & Duc Khuong Nguyen, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Working Papers 2014-325, Department of Research, Ipag Business School.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003.
"Modeling and Forecasting Realized Volatility,"
Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," Center for Financial Institutions Working Papers 01-01, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Anderson, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Labys, Paul, 2002. "Modeling and Forecasting Realized Volatility," Working Papers 02-12, Duke University, Department of Economics.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," NBER Working Papers 8160, National Bureau of Economic Research, Inc.
- John Y. Campbell & Samuel B. Thompson, 2008.
"Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?,"
The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
- Campbell, John & Thompson, Samuel P., 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Scholarly Articles 2622619, Harvard University Department of Economics.
- Liang, Chao & Li, Yan & Ma, Feng & Wei, Yu, 2021. "Global equity market volatilities forecasting: A comparison of leverage effects, jumps, and overnight information," International Review of Financial Analysis, Elsevier, vol. 75(C).
- Ghulam Abbas & Shouyang Wang, 2020. "Does macroeconomic uncertainty really matter in predicting stock market behavior? A comparative study on China and USA," China Finance Review International, Emerald Group Publishing Limited, vol. 10(4), pages 393-427, May.
- Chao Liang & Yu Wei & Xiafei Li & Xuhui Zhang & Yifeng Zhang, 2020. "Uncertainty and crude oil market volatility: new evidence," Applied Economics, Taylor & Francis Journals, vol. 52(27), pages 2945-2959, May.
- Chesney, Marc & Reshetar, Ganna & Karaman, Mustafa, 2011. "The impact of terrorism on financial markets: An empirical study," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 253-267, February.
- Thomas C. Chiang, 2021. "Geopolitical risk, economic policy uncertainty and asset returns in Chinese financial markets," China Finance Review International, Emerald Group Publishing Limited, vol. 11(4), pages 474-501, March.
- Conghua Wen & Fei Jia & Jianli Hao, 2020. "Does VPIN provide predictive information for realized volatility forecasting: evidence from Chinese stock index futures market," China Finance Review International, Emerald Group Publishing Limited, vol. 13(2), pages 285-303, November.
- Stavros Degiannakis, 2004.
"Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model,"
Applied Financial Economics, Taylor & Francis Journals, vol. 14(18), pages 1333-1342.
- Degiannakis, Stavros, 2004. "Volatility Forecasting: Evidence from a Fractional Integrated Asymmetric Power ARCH Skewed-t Model," MPRA Paper 96330, University Library of Munich, Germany.
- Sun, Zhaojun & Xu, Xiaoguang & Yang, Wen, 2022. "Capital account liberalization, external shocks and economic fluctuations of China," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 220-240.
- Janis Becker & Christian Leschinski, 2021.
"Estimating the volatility of asset pricing factors,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 269-278, March.
- Becker, Janis & Leschinski, Christian, 2018. "Estimating the Volatility of Asset Pricing Factors," Hannover Economic Papers (HEP) dp-631, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
- Wang, Jiqian & He, Xiaofeng & Ma, Feng & Li, Pan, 2022. "Uncertainty and oil volatility: Evidence from shrinkage method," Resources Policy, Elsevier, vol. 75(C).
- Qiaoqi Lang & Jiqian Wang & Feng Ma & Dengshi Huang & Mohamed Wahab Mohamed Ismail, 2021. "Is Baidu index really powerful to predict the Chinese stock market volatility? New evidence from the internet information," China Finance Review International, Emerald Group Publishing Limited, vol. 13(2), pages 263-284, July.
- Liang, Chao & Umar, Muhammad & Ma, Feng & Huynh, Toan L.D., 2022. "Climate policy uncertainty and world renewable energy index volatility forecasting," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
- Long, Huaigang & Zaremba, Adam & Zhou, Wenyu & Bouri, Elie, 2022. "Macroeconomics matter: Leading economic indicators and the cross-section of global stock returns," Journal of Financial Markets, Elsevier, vol. 61(C).
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Keywords
Leading economic indicators; Shock sizes; Volatility forecasting; GARCH-MIDAS;All these keywords.
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