Forecasting the Chinese low-carbon index volatility
Author
Abstract
Suggested Citation
DOI: 10.1016/j.resourpol.2022.102732
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Feng Ma & Yaojie Zhang & M. I. M. Wahab & Xiaodong Lai, 2019. "The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(5), pages 400-414, August.
- Feng Ma & M. I. M. Wahab & Jing Liu & Li Liu, 2018. "Is economic policy uncertainty important to forecast the realized volatility of crude oil futures?," Applied Economics, Taylor & Francis Journals, vol. 50(18), pages 2087-2101, April.
- Pesaran, M Hashem & Timmermann, Allan, 1992.
"A Simple Nonparametric Test of Predictive Performance,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
- Pesaran, M.H. & Timmermann, A., 1990. "A Simple, Non-Parametric Test Of Predictive Performance," Cambridge Working Papers in Economics 9021, Faculty of Economics, University of Cambridge.
- Pesaran, M.H. & Timmermann, A., 1990. "A Simple Non-Parametric Test Of Predictive Performance," Papers 29, California Los Angeles - Applied Econometrics.
- Zhu Liu & Dabo Guan & Douglas Crawford-Brown & Qiang Zhang & Kebin He & Jianguo Liu, 2013. "A low-carbon road map for China," Nature, Nature, vol. 500(7461), pages 143-145, August.
- Zhang, ZhongXiang, 2010.
"China in the transition to a low-carbon economy,"
Energy Policy, Elsevier, vol. 38(11), pages 6638-6653, November.
- ZhongXiang Zhang, 2010. "China in the Transition to a Low-Carbon Economy," Working Papers 2010.76, Fondazione Eni Enrico Mattei.
- ZhongXiang Zhang, 2010. "China in the Transition to a Low-Carbon Economy," Economics Study Area Working Papers 109, East-West Center, Economics Study Area.
- Zhang, ZhongXiang, 2010. "China in the Transition to a Low-Carbon Economy," Sustainable Development Papers 91009, Fondazione Eni Enrico Mattei (FEEM).
- Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005.
"A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data,"
Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
- Lan Zhang & Per A. Mykland & Yacine Ait-Sahalia, 2003. "A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High Frequency Data," NBER Working Papers 10111, National Bureau of Economic Research, Inc.
- Degiannakis, Stavros & Filis, George, 2017.
"Forecasting oil price realized volatility using information channels from other asset classes,"
Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
- Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," MPRA Paper 96276, University Library of Munich, Germany.
- 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.
- Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303.
- Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008.
"Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise,"
Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
- Ole E Barndorff-Nielsen & Peter Hansen & Asger Lunde & Neil Shephard, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," OFRC Working Papers Series 2006fe05, Oxford Financial Research Centre.
- Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," Economics Papers 2006-W03, Economics Group, Nuffield College, University of Oxford.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016.
"Measuring Economic Policy Uncertainty,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2015. "Measuring Economic Policy Uncertainty," Economics Working Papers 15111, Hoover Institution, Stanford University.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2015. "Measuring Economic Policy Uncertainty," NBER Working Papers 21633, National Bureau of Economic Research, Inc.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2015. "Measuring Economic Policy Uncertainty," CEP Discussion Papers dp1379, Centre for Economic Performance, LSE.
- Baker, Scott R. & Bloom, Nicholas & Davis, Steven J., 2015. "Measuring economic policy uncertainty," LSE Research Online Documents on Economics 64986, London School of Economics and Political Science, LSE Library.
- Davis, Steven & Bloom, Nicholas & Baker, Scott, 2015. "Measuring Economic Policy Uncertainty," CEPR Discussion Papers 10900, C.E.P.R. Discussion Papers.
- Fang, Libing & Yu, Honghai & Li, Lei, 2017. "The effect of economic policy uncertainty on the long-term correlation between U.S. stock and bond markets," Economic Modelling, Elsevier, vol. 66(C), pages 139-145.
- Bekaert, Geert & Hoerova, Marie, 2014.
"The VIX, the variance premium and stock market volatility,"
Journal of Econometrics, Elsevier, vol. 183(2), pages 181-192.
- Geert Bekaert & Marie Hoerova, 2013. "The VIX, the Variance Premium and Stock Market Volatility," NBER Working Papers 18995, National Bureau of Economic Research, Inc.
- Hoerova, Marie & Bekaert, Geert, 2014. "The VIX, the variance premium and stock market volatility," Working Paper Series 1675, European Central Bank.
- Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
- 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.
- Yan‐ran Ma & Qiang Ji & Jiaofeng Pan, 2019. "Oil financialization and volatility forecast: Evidence from multidimensional predictors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(6), pages 564-581, September.
- 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.
- Jonathan Brogaard & Andrew Detzel, 2015. "The Asset-Pricing Implications of Government Economic Policy Uncertainty," Management Science, INFORMS, vol. 61(1), pages 3-18, January.
- Christoffersen, Peter & Feunou, Bruno & Jacobs, Kris & Meddahi, Nour, 2014.
"The Economic Value of Realized Volatility: Using High-Frequency Returns for Option Valuation,"
Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(3), pages 663-697, June.
- Peter Christoffersen & Bruno Feunou & Kris Jacobs & Nour Meddahi, 2012. "The Economic Value of Realized Volatility: Using High-Frequency Returns for Option Valuation," Staff Working Papers 12-34, Bank of Canada.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Jian Chen & Fuwei Jiang & Guoshi Tong, 2017. "Economic policy uncertainty in China and stock market expected returns," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57(5), pages 1265-1286, December.
- Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
- Barbara Rossi & Atsushi Inoue, 2012.
"Out-of-Sample Forecast Tests Robust to the Choice of Window Size,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
- Rossi, Barbara & Inoue, Atsushi, 2011. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," CEPR Discussion Papers 8542, C.E.P.R. Discussion Papers.
- Atsushi Inoue & Barbara Rossi, 2011. "Out-of-sample forecast tests robust to the choice of window size," Working Papers 11-31, Federal Reserve Bank of Philadelphia.
- Barbara Rossi & Atsushi Inoue, 2012. "Out-of-sample forecast tests robust to the choice of window size," Economics Working Papers 1404, Department of Economics and Business, Universitat Pompeu Fabra.
- 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.
- Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2015. "GARCH modeling of five popular commodities," Empirical Economics, Springer, vol. 48(4), pages 1691-1712, June.
- Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
- Jiqian Wang & Feng Ma & M.I.M. Wahab & Dengshi Huang, 2021. "Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 921-941, August.
- Francis X. Diebold & Jose A. Lopez, 1995.
"Forecast evaluation and combination,"
Research Paper
9525, Federal Reserve Bank of New York.
- Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
- Dobrislav Dobrev & Pawel J. Szerszen, 2010. "The information content of high-frequency data for estimating equity return models and forecasting risk," Finance and Economics Discussion Series 2010-45, Board of Governors of the Federal Reserve System (U.S.).
- 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.
- Dou, Yue & Li, Yiying & Dong, Kangyin & Ren, Xiaohang, 2022. "Dynamic linkages between economic policy uncertainty and the carbon futures market: Does Covid-19 pandemic matter?," Resources Policy, Elsevier, vol. 75(C).
- Sharma, Prateek & Vipul,, 2016. "Forecasting stock market volatility using Realized GARCH model: International evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 59(C), pages 222-230.
- Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
- 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.
- Dobrislav Dobrev & Pawel J. Szerszen, 2010. "The information content of high-frequency data for estimating equity return models and forecasting risk," International Finance Discussion Papers 1005, Board of Governors of the Federal Reserve System (U.S.).
- Aït-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2011.
"Ultra high frequency volatility estimation with dependent microstructure noise,"
Journal of Econometrics, Elsevier, vol. 160(1), pages 160-175, January.
- Ait-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2005. "Ultra high frequency volatility estimation with dependent microstructure noise," Discussion Paper Series 1: Economic Studies 2005,30, Deutsche Bundesbank.
- Yacine Ait-Sahalia & Per A. Mykland & Lan Zhang, 2005. "Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise," NBER Working Papers 11380, National Bureau of Economic Research, Inc.
- Shahbaz, Muhammad & Destek, Mehmet Akif & Dong, Kangyin & Jiao, Zhilun, 2021.
"Time-varying impact of financial development on carbon emissions in G-7 countries: Evidence from the long history,"
Technological Forecasting and Social Change, Elsevier, vol. 171(C).
- Shahbaz, Muhammad & Destek, Mehmet Akif & Dong, Kangyin & Jiao, Zhilun, 2021. "Time-Varying Impact of Financial Development on Carbon Emissions in G-7 Countries: Evidence from the Long History," MPRA Paper 108375, University Library of Munich, Germany, revised 18 Jun 2021.
- Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
- Mohsin, Muhammad & Taghizadeh-Hesary, Farhad & Panthamit, Nisit & Anwar, Saba & Abbas, Qaiser & Vo, Xuan Vinh, 2021. "Developing Low Carbon Finance Index: Evidence From Developed and Developing Economies," Finance Research Letters, Elsevier, vol. 43(C).
- Zhao, Jun & Shahbaz, Muhammad & Dong, Xiucheng & Dong, Kangyin, 2021. "How does financial risk affect global CO2 emissions? The role of technological innovation," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
- Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006.
"Predicting volatility: getting the most out of return data sampled at different frequencies,"
Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," NBER Working Papers 10914, National Bureau of Economic Research, Inc.
- Steven J. Cochran & Iqbal Mansur & Babatunde Odusami, 2016. "Conditional higher order moments in metal asset returns," Quantitative Finance, Taylor & Francis Journals, vol. 16(1), pages 151-167, January.
- Dima Alberg & Haim Shalit & Rami Yosef, 2008.
"Estimating stock market volatility using asymmetric GARCH models,"
Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
- Dima Alberg & Haim Shalit & Rami Yosef, 2006. "Estimating Stock Market Volatility Using Asymmetric GARCH Models," Working Papers 0610, Ben-Gurion University of the Negev, 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).
- Yudong Wang & Zhiyuan Pan & Chongfeng Wu, 2017. "Time‐Varying Parameter Realized Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 566-580, August.
- Peter Reinhard Hansen & Zhuo Huang & Howard Howan Shek, 2012. "Realized GARCH: a joint model for returns and realized measures of volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 877-906, September.
- Mo, Di & Gupta, Rakesh & Li, Bin & Singh, Tarlok, 2018. "The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets," Economic Modelling, Elsevier, vol. 70(C), pages 543-560.
- Vipul & Joshy Jacob, 2007. "Forecasting performance of extreme‐value volatility estimators," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(11), pages 1085-1105, November.
- Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
- Feng Ma & Yu Wei & Li Liu & Dengshi Huang, 2018. "Forecasting realized volatility of oil futures market: A new insight," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(4), pages 419-436, July.
- Bridge, Gavin & Bouzarovski, Stefan & Bradshaw, Michael & Eyre, Nick, 2013. "Geographies of energy transition: Space, place and the low-carbon economy," Energy Policy, Elsevier, vol. 53(C), pages 331-340.
- Chao Liang & Yu Wei & Yaojie Zhang, 2020. "Is implied volatility more informative for forecasting realized volatility: An international perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1253-1276, December.
- Daniel Borup & Johan S. Jakobsen, 2019. "Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model," Quantitative Finance, Taylor & Francis Journals, vol. 19(11), pages 1839-1855, November.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Amrita Goldar & Diya Dasgupta, 2023. "Beyond the Stocktake (Part II): Clean Energy Technologies," Indian Council for Research on International Economic Relations (ICRIER) Policy Paper 14, Indian Council for Research on International Economic Relations (ICRIER), New Delhi, India.
- Kumar, Tharun Roshan & Beiron, Johanna & Biermann, Maximilian & Harvey, Simon & Thunman, Henrik, 2023. "Plant and system-level performance of combined heat and power plants equipped with different carbon capture technologies," Applied Energy, Elsevier, vol. 338(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).
- Huang, Yisu & Xu, Weiju & Huang, Dengshi & Zhao, Chenchen, 2023. "Chinese crude oil futures volatility and sustainability: An uncertainty indices perspective," Resources Policy, Elsevier, vol. 80(C).
- Li, Tao & Ma, Feng & Zhang, Xuehua & Zhang, Yaojie, 2020. "Economic policy uncertainty and the Chinese stock market volatility: Novel evidence," Economic Modelling, Elsevier, vol. 87(C), pages 24-33.
- Chen, Zhonglu & Zhang, Li & Weng, Chen, 2023. "Does climate policy uncertainty affect Chinese stock market volatility?," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 369-381.
- Xiafei Li & Yu Wei & Xiaodan Chen & Feng Ma & Chao Liang & Wang Chen, 2022. "Which uncertainty is powerful to forecast crude oil market volatility? New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4279-4297, October.
- Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.
- Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
- Chao Liang & Feng Ma & Lu Wang & Qing Zeng, 2021. "The information content of uncertainty indices for natural gas futures volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1310-1324, November.
- Zhang, Lixia & Luo, Qin & Guo, Xiaozhu & Umar, Muhammad, 2022. "Medium-term and long-term volatility forecasts for EUA futures with country-specific economic policy uncertainty indices," Resources Policy, Elsevier, vol. 77(C).
- Chao Liang & Yaojie Zhang & Xiafei Li & Feng Ma, 2022. "Which predictor is more predictive for Bitcoin volatility? And why?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1947-1961, April.
- Lu Wang & Feng Ma & Guoshan Liu & Qiaoqi Lang, 2023. "Do extreme shocks help forecast oil price volatility? The augmented GARCH‐MIDAS approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2056-2073, April.
- Xiafei Li & Dongxin Li & Xuhui Zhang & Guiwu Wei & Lan Bai & Yu Wei, 2021. "Forecasting regular and extreme gold price volatility: The roles of asymmetry, extreme event, and jump," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1501-1523, December.
- Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.
- Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
- Yusui Tang & Feng Ma & Yaojie Zhang & Yu Wei, 2022. "Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4770-4783, October.
- Guo, Xiaozhu & Huang, Yisu & Liang, Chao & Umar, Muhammad, 2022. "Forecasting volatility of EUA futures: New evidence," Energy Economics, Elsevier, vol. 110(C).
- Tong Fang & Deyu Miao & Zhi Su & Libo Yin, 2023. "Uncertainty‐driven oil volatility risk premium and international stock market volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 872-904, July.
- 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).
- Chen, Wang & Lu, Xinjie & Wang, Jiqian, 2022. "Modeling and managing stock market volatility using MRS-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 625-635.
- Liu, Guangqiang & Guo, Xiaozhu, 2022. "Forecasting stock market volatility using commodity futures volatility information," Resources Policy, Elsevier, vol. 75(C).
More about this item
Keywords
GARCH model; China'S economic policy uncertainty; Realized measure; The Chinese low-carbon index;All these keywords.
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:77:y:2022:i:c:s0301420722001805. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.