Global financial stress index and long-term volatility forecast for international stock markets
Author
Abstract
Suggested Citation
DOI: 10.1016/j.intfin.2023.101825
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
- Dario Caldara & Matteo Iacoviello, 2022.
"Measuring Geopolitical Risk,"
American Economic Review, American Economic Association, vol. 112(4), pages 1194-1225, April.
- Matteo Iacoviello, 2018. "Measuring Geopolitical Risk," 2018 Meeting Papers 79, Society for Economic Dynamics.
- Dario Caldara & Matteo Iacoviello, 2018. "Measuring Geopolitical Risk," International Finance Discussion Papers 1222r1, Board of Governors of the Federal Reserve System (U.S.), revised 23 Mar 2022.
- Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017.
"Rolling window selection for out-of-sample forecasting with time-varying parameters,"
Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
- Atsushi Inoue & Lu Jin & Barbara Rossi, 2014. "Rolling Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters," Working Papers 768, Barcelona School of Economics.
- Atsushi Inoue & Lu Jin & Barbara Rossi, 2014. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Economics Working Papers 1435, Department of Economics and Business, Universitat Pompeu Fabra, revised Apr 2016.
- Fernandes, Marcelo & Medeiros, Marcelo C. & Scharth, Marcel, 2014.
"Modeling and predicting the CBOE market volatility index,"
Journal of Banking & Finance, Elsevier, vol. 40(C), pages 1-10.
- Marcelo Fernandes & Marcelo Cunha Medeiros & MArcelo Scharth, 2007. "Modeling and predicting the CBOE market volatility index," Textos para discussão 548, Department of Economics PUC-Rio (Brazil).
- Fernandes, Marcelo & Medeiros, Marcelo C. & Scharth, Marcel, 2013. "Modeling and predicting the CBOE market volatility index," Textos para discussão 342, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
- West, Kenneth D, 1996.
"Asymptotic Inference about Predictive Ability,"
Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
- West, K.D., 1994. "Asymptotic Inference About Predictive Ability," Working papers 9417, Wisconsin Madison - Social Systems.
- Kenneth D. West, 1994. "Asymptotic Inference About Predictive Ability," Macroeconomics 9410002, University Library of Munich, Germany.
- Bekierman, Jeremias & Manner, Hans, 2018. "Forecasting realized variance measures using time-varying coefficient models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 276-287.
- Louzis, Dimitrios P. & Vouldis, Angelos T., 2012. "A methodology for constructing a financial systemic stress index: An application to Greece," Economic Modelling, Elsevier, vol. 29(4), pages 1228-1241.
- Francesco Audrino & Yujia Hu, 2016.
"Volatility Forecasting: Downside Risk, Jumps and Leverage Effect,"
Econometrics, MDPI, vol. 4(1), pages 1-24, February.
- Audrino, Francesco & Hu, Yujia, 2011. "Volatility Forecasting: Downside Risk, Jumps and Leverage Effect," Economics Working Paper Series 1138, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
- Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020.
"Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss,"
Journal of International Money and Finance, Elsevier, vol. 104(C).
- Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss," Working Papers 201903, University of Pretoria, Department of Economics.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Roberto S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, 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.
- Wen, Fenghua & Gong, Xu & Cai, Shenghua, 2016. "Forecasting the volatility of crude oil futures using HAR-type models with structural breaks," Energy Economics, Elsevier, vol. 59(C), pages 400-413.
- 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.
- 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.
- Degiannakis, Stavros, 2018.
"Multiple days ahead realized volatility forecasting: Single, combined and average forecasts,"
Global Finance Journal, Elsevier, vol. 36(C), pages 41-61.
- Degiannakis, Stavros, 2018. "Multiple Days Ahead Realized Volatility Forecasting: Single, Combined and Average Forecasts," MPRA Paper 96272, University Library of Munich, Germany.
- Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
- Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015.
"Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes,"
Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
- Kevin Sheppard & Lily Liu & Andrew J. Patton, 2013. "Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes," Economics Series Working Papers 645, University of Oxford, Department of Economics.
- Ke Yang & Langnan Chen & Fengping Tian, 2015. "Realized Volatility Forecast of Stock Index Under Structural Breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(1), pages 57-82, January.
- Niels Joachim Gormsen & Ralph S J Koijen & Nikolai Roussanov, 0.
"Coronavirus: Impact on Stock Prices and Growth Expectations,"
The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(4), pages 574-597.
- Koijen, Ralph & Gormsen, Niels Joachim, 2020. "Coronavirus: Impact on Stock Prices and Growth Expectations," CEPR Discussion Papers 14875, C.E.P.R. Discussion Papers.
- Niels J. Gormsen & Ralph S. J. Koijen, 2020. "Coronavirus: Impact on Stock Prices and Growth Expectations," NBER Working Papers 27387, National Bureau of Economic Research, Inc.
- Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
- Illing, Mark & Liu, Ying, 2006. "Measuring financial stress in a developed country: An application to Canada," Journal of Financial Stability, Elsevier, vol. 2(3), pages 243-265, October.
- Dan Pang & Feng Ma & M. I. M. Wahab & Bo Zhu, 2023. "Financial stress and oil market volatility: new evidence," Applied Economics Letters, Taylor & Francis Journals, vol. 30(1), pages 1-6, January.
- Ole E. Barndorff‐Nielsen & Neil Shephard, 2002.
"Econometric analysis of realized volatility and its use in estimating stochastic volatility models,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
- Ole E. Barndorff-Nielsen & Neil Shephard, 2000. "Econometric analysis of realised volatility and its use in estimating stochastic volatility models," Economics Papers 2001-W4, Economics Group, Nuffield College, University of Oxford, revised 05 Jul 2001.
- Neil Shephard & Ole E. Barndorff-Nielsen & University of Aarhus, 2001. "Econometric Analysis of Realised Volatility and Its Use in Estimating Stochastic Volatility Models," Economics Series Working Papers 71, University of Oxford, Department of Economics.
- 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.
- 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.
- 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.
- 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.
- 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.
- Yao, Xingzhi & Izzeldin, Marwan & Li, Zhenxiong, 2019. "A novel cluster HAR-type model for forecasting realized volatility," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1318-1331.
- 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.
- Jonathan Brogaard & Andrew Detzel, 2015. "The Asset-Pricing Implications of Government Economic Policy Uncertainty," Management Science, INFORMS, vol. 61(1), pages 3-18, January.
- Ma, Feng & Liao, Yin & Zhang, Yaojie & Cao, Yang, 2019. "Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 40-55.
- 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.
- Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020.
"Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection,"
Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
- Tong Fang & Tae-Hwy Lee & Zhi Su, 2020. "Predicting the Long-term Stock Market Volatility: A GARCH-MIDAS Model with Variable Selection," Working Papers 202009, University of California at Riverside, Department of Economics.
- Bouri, Elie & Gupta, Rangan & Lau, Chi Keung Marco & Roubaud, David & Wang, Shixuan, 2018.
"Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles,"
The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 297-307.
- Elie Bouri & Rangan Gupta & Chi Keung Marco Lau & David Roubaud & Shixuan Wang, 2017. "Bitcoin and Global Financial Stress: A Copula-Based Approach to Dependence and Causality-in-Quantiles," Working Papers 201750, University of Pretoria, Department of Economics.
- Yaojie Zhang & Feng Ma & Tianyi Wang & Li Liu, 2019. "Out‐of‐sample volatility prediction: A new mixed‐frequency approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(7), pages 669-680, November.
- 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).
- Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
- Evgenidis, Anastasios & Tsagkanos, Athanasios, 2017. "Asymmetric effects of the international transmission of US financial stress. A threshold-VAR approach," International Review of Financial Analysis, Elsevier, vol. 51(C), pages 69-81.
- Gupta, Rangan & Kanda, Patrick & Tiwari, Aviral Kumar & Wohar, Mark E., 2019.
"Time-varying predictability of oil market movements over a century of data: The role of US financial stress,"
The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
- Rangan Gupta & Patrick Kanda & Aviral Kumar Tiwari & Mark E. Wohar, 2018. "Time-Varying Predictability of Oil Market Movements Over a Century of Data: The Role of US Financial Stress," Working Papers 201848, University of Pretoria, Department of Economics.
- Park, Cyn-Young & Mercado, Rogelio V., 2014.
"Determinants of financial stress in emerging market economies,"
Journal of Banking & Finance, Elsevier, vol. 45(C), pages 199-224.
- Park, Cyn-Young & Mercado, Jr., Rogelio V., 2013. "Determinants of Financial Stress in Emerging Market Economies," ADB Economics Working Paper Series 356, Asian Development Bank.
- Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
- Wang, Jiqian & Lu, Xinjie & He, Feng & Ma, Feng, 2020. "Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU?," International Review of Financial Analysis, Elsevier, vol. 72(C).
- Patton, Andrew J., 2011.
"Volatility forecast comparison using imperfect volatility proxies,"
Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
- Andrew Patton, 2006. "Volatility Forecast Comparison using Imperfect Volatility Proxies," Research Paper Series 175, Quantitative Finance Research Centre, University of Technology, Sydney.
- Wang, Yudong & Wei, Yu & Wu, Chongfeng & Yin, Libo, 2018. "Oil and the short-term predictability of stock return volatility," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 90-104.
- 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.
- Zhang, Yaojie & Ma, Feng & Liao, Yin, 2020. "Forecasting global equity market volatilities," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1454-1475.
- 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.
- Balcilar, Mehmet & Gupta, Rangan & Kim, Won Joong & Kyei, Clement, 2019. "The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 150-163.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Yan, Xiang & Han, Zhiyong & Zou, Chen & Cheng, Changgao, 2024. "Assessing the role of emerging green technology transfer in sustainable development and identification of key regions in Yangtze River Delta region," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
- Sheikh, Umaid A. & Asadi, Mehrad & Roubaud, David & Hammoudeh, Shawkat, 2024. "Global uncertainties and Australian financial markets: Quantile time-frequency connectedness," International Review of Financial Analysis, Elsevier, vol. 92(C).
- Cui, Jinxin & Maghyereh, Aktham, 2024. "Unveiling interconnectedness: Exploring higher-order moments among energy, precious metals, industrial metals, and agricultural commodities in the context of geopolitical risks and systemic stress," Journal of Commodity Markets, Elsevier, vol. 33(C).
- Ghosh, Indranil & Jana, Rabin K. & David, Roubaud & Grebinevych, Oksana & Wanke, Peter & Tan, Yong, 2024. "Modelling financial stress during the COVID-19 pandemic: Prediction and deeper insights," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 680-698.
- Dong, Xiuliang & Wang, Yiqun & Zhang, Jiaming & Liu, Jianing, 2024. "Sponsor Co-investment, inquiry divergence, and IPO pricing efficiency," Finance Research Letters, Elsevier, vol. 62(PA).
- Fava, Santino Del & Gupta, Rangan & Pierdzioch, Christian & Rognone, Lavinia, 2024.
"Forecasting international financial stress: The role of climate risks,"
Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
- Santino Del Fava & Rangan Gupta & Christian Pierdzioch & Lavinia Rognone, 2023. "Forecasting International Financial Stress: The Role of Climate Risks," Working Papers 202329, University of Pretoria, Department of Economics.
- Ma, Feng & Lyu, Zhichong & Li, Haibo, 2024. "Can ChatGPT predict Chinese equity premiums?," Finance Research Letters, Elsevier, vol. 65(C).
- Wang, Lu & Ruan, Hang & Lai, Xiaodong & Li, Dongxin, 2024. "Economic extremes steering renewable energy trajectories: A time-frequency dissection of global shocks," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
- Pan, Zhigang & Bai, Zhihong & Xing, Xiaochao & Wang, Zhufeng, 2024. "US inflation and global commodity prices: Asymmetric interdependence," Research in International Business and Finance, Elsevier, vol. 69(C).
- 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, vol. 58(PB).
- Zhang, Xincheng, 2024. "Country-level energy-related uncertainties and stock market returns: Insights from the U.S. and China," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
- Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(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, Ping & Han, Wei & Huang, Chengcheng & Duong, Duy, 2022. "Forecasting realised volatility from search volume and overnight sentiment: Evidence from China," Research in International Business and Finance, Elsevier, vol. 62(C).
- Wang, Jiqian & Lu, Xinjie & He, Feng & Ma, Feng, 2020. "Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU?," International Review of Financial Analysis, Elsevier, vol. 72(C).
- Ma, Feng & Wang, Jiqian & Wahab, M.I.M. & Ma, Yuanhui, 2023. "Stock market volatility predictability in a data-rich world: A new insight," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1804-1819.
- 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).
- 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.
- Zhang, Yaojie & Lei, Likun & Wei, Yu, 2020. "Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching," The North American Journal of Economics and Finance, Elsevier, vol. 52(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.
- Zhang, Yaojie & Ma, Feng & Wei, Yu, 2019. "Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches," Energy Economics, Elsevier, vol. 81(C), pages 1109-1120.
- Yi, Yongsheng & He, Mengxi & Zhang, Yaojie, 2022. "Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
- 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.
- Chen, Wang & Ma, Feng & Wei, Yu & Liu, Jing, 2020. "Forecasting oil price volatility using high-frequency data: New evidence," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 1-12.
- 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.
- Chao Liang & Yin Liao & Feng Ma & Bo Zhu, 2022. "United States Oil Fund volatility prediction: the roles of leverage effect and jumps," Empirical Economics, Springer, vol. 62(5), pages 2239-2262, May.
- 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.
- Wang, Lu & Ma, Feng & Hao, Jianyang & Gao, Xinxin, 2021. "Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?," International Review of Financial Analysis, Elsevier, vol. 76(C).
- Chen, Zhonglu & Liang, Chao & Umar, Muhammad, 2021. "Is investor sentiment stronger than VIX and uncertainty indices in predicting energy volatility?," Resources Policy, Elsevier, vol. 74(C).
- Chao Liang & Yan Li & Feng Ma & Yaojie Zhang, 2022. "Forecasting international equity market volatility: A new approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1433-1457, November.
- 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.
- Mei, Dexiang & Zhao, Chenchen & Luo, Qin & Li, Yan, 2022. "Forecasting the Chinese low-carbon index volatility," Resources Policy, Elsevier, vol. 77(C).
- Ding, Hui & Huang, Yisu & Wang, Jiqian, 2023. "Have the predictability of oil changed during the COVID-19 pandemic: Evidence from international stock markets," International Review of Financial Analysis, Elsevier, vol. 87(C).
More about this item
Keywords
COVID-19; Global financial stress index; VIX; EPU; Volatility forecast;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:intfin:v:88:y:2023:i:c:s1042443123000938. 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/intfin .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.