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News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons

Citations

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Cited by:

  1. Yen-Ju Hsu & Yang-Cheng Lu & J. Jimmy Yang, 2021. "News sentiment and stock market volatility," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 1093-1122, October.
  2. Kruse, Robinson & Leschinski, Christian & Will, Michael, 2016. "Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting," Hannover Economic Papers (HEP) dp-571, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  3. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Annals of Economics and Statistics, GENES, issue 123-124, pages 135-174.
  4. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
  5. repec:hum:wpaper:sfb649dp2012-045 is not listed on IDEAS
  6. Emre Alper, C. & Fendoglu, Salih & Saltoglu, Burak, 2012. "MIDAS volatility forecast performance under market stress: Evidence from emerging stock markets," Economics Letters, Elsevier, vol. 117(2), pages 528-532.
  7. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
  8. Karim, Muhammad Mahmudul & Kawsar, Najmul Haque & Ariff, Mohamed & Masih, Mansur, 2022. "Does implied volatility (or fear index) affect Islamic stock returns and conventional stock returns differently? Wavelet-based granger-causality, asymmetric quantile regression and NARDL approaches," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
  9. Liu, Jing & Ma, Feng & Yang, Ke & Zhang, Yaojie, 2018. "Forecasting the oil futures price volatility: Large jumps and small jumps," Energy Economics, Elsevier, vol. 72(C), pages 321-330.
  10. Metin Tetik, 2021. "Comparison of News Impacts on Sectoral Stock Returns during the COVID-19 Pandemic in Turkey," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 7(2), pages 35-46, December.
  11. Elena Andreou, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," University of Cyprus Working Papers in Economics 03-2016, University of Cyprus Department of Economics.
  12. Jensen, Mark J. & Maheu, John M., 2014. "Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture," Journal of Econometrics, Elsevier, vol. 178(P3), pages 523-538.
  13. Clements, Adam & Preve, Daniel P.A., 2021. "A Practical Guide to harnessing the HAR volatility model," Journal of Banking & Finance, Elsevier, vol. 133(C).
  14. Mei, Dexiang & Liu, Jing & Ma, Feng & Chen, Wang, 2017. "Forecasting stock market volatility: Do realized skewness and kurtosis help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 153-159.
  15. Seo, Sung Won & Kim, Jun Sik, 2015. "The information content of option-implied information for volatility forecasting with investor sentiment," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 106-120.
  16. 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.
  17. Li, Hong & Shi, Yanlin, 2021. "A new unique information share measure with applications on cross-listed Chinese banks," Journal of Banking & Finance, Elsevier, vol. 128(C).
  18. Chevallier, Julien & Sévi, Benoît, 2012. "On the volatility–volume relationship in energy futures markets using intraday data," Energy Economics, Elsevier, vol. 34(6), pages 1896-1909.
  19. Yaojie Zhang & Mengxi He & Danyan Wen & Yudong Wang, 2022. "Forecasting Bitcoin volatility: A new insight from the threshold regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 633-652, April.
  20. 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.
  21. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
  22. Lin, Tiantian & Liu, Dehong & Zhang, Lili & Lung, Peter, 2019. "The information content of realized volatility of sector indices in China’s stock market," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 625-640.
  23. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
  24. Chou, Ke-Hsin & Day, Min-Yuh & Chiu, Chien-Liang, 2023. "Do bitcoin news information flow and return volatility fit the sequential information arrival hypothesis and the mixture of distribution hypothesis?," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 365-385.
  25. Zhu (Drew) Zhang & Jie Yuan & Amulya Gupta, 2024. "Let the Laser Beam Connect the Dots: Forecasting and Narrating Stock Market Volatility," INFORMS Journal on Computing, INFORMS, vol. 36(6), pages 1400-1416, December.
  26. Georgiana-Denisa Banulescu & Bertrand Candelon & Christophe Hurlin & Sébastien Laurent, 2014. "Do We Need Ultra-High Frequency Data to Forecast Variances?," Working Papers halshs-01078158, HAL.
  27. repec:dau:papers:123456789/6887 is not listed on IDEAS
  28. Murat Körs & Mehmet Baha Karan, 2023. "Stock exchange volatility forecasting under market stress with MIDAS regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 295-306, January.
  29. Ahmed BenSaïda, 2021. "The Good and Bad Volatility: A New Class of Asymmetric Heteroskedastic Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 540-570, April.
  30. Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
  31. Buncic, Daniel & Gisler, Katja I.M., 2016. "Global equity market volatility spillovers: A broader role for the United States," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1317-1339.
  32. Cláudia Duarte, 2016. "A Mixed Frequency Approach to Forecast Private Consumption with ATM/POS Data," Working Papers w201601, Banco de Portugal, Economics and Research Department.
  33. Jain, Pawan & Jiang, Christine, 2014. "Predicting future price volatility: Empirical evidence from an emerging limit order market," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 72-93.
  34. Hiroyuki Kawakatsu, 2022. "Local projection variance impulse response," Empirical Economics, Springer, vol. 62(3), pages 1219-1244, March.
  35. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
  36. Liu, Yuanyuan & Niu, Zibo & Suleman, Muhammad Tahir & Yin, Libo & Zhang, Hongwei, 2022. "Forecasting the volatility of crude oil futures: The role of oil investor attention and its regime switching characteristics under a high-frequency framework," Energy, Elsevier, vol. 238(PA).
  37. Julien Chevallier & Benoît Sévi, 2011. "On the volatility-volume relationship in energy futures markets using intraday data," Working Papers hal-04140997, HAL.
  38. Andrew Harvey & Rutger-Jan Lange, 2015. "Modeling the Interactions between Volatility and Returns," Cambridge Working Papers in Economics 1518, Faculty of Economics, University of Cambridge.
  39. González-Pla, Francisco & Lovreta, Lidija, 2022. "Modeling and forecasting firm-specific volatility: The role of asymmetry and long-memory," Finance Research Letters, Elsevier, vol. 48(C).
  40. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," Journal of Econometrics, Elsevier, vol. 193(2), pages 367-389.
  41. Bumho Son & Yunyoung Lee & Seongwan Park & Jaewook Lee, 2023. "Forecasting global stock market volatility: The impact of volatility spillover index in spatial‐temporal graph‐based model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1539-1559, November.
  42. Lu, Fei & Ma, Feng & Bouri, Elie & Liao, Yin, 2024. "Do commodity futures have a steering effect on the spot stock market in China? New evidence from volatility forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
  43. Agata Kliber, 2016. "The leverage effect puzzle: the case of European sovereign credit default swap market," Review of Derivatives Research, Springer, vol. 19(3), pages 217-235, October.
  44. Shi, Yanlin & Ho, Kin-Yip & Liu, Wai-Man, 2016. "Public information arrival and stock return volatility: Evidence from news sentiment and Markov Regime-Switching Approach," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 291-312.
  45. Xu Gong & Boqiang Lin, 2022. "Predicting the volatility of crude oil futures: The roles of leverage effects and structural changes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 610-640, January.
  46. Astorino, Eduardo & Chague, Fernando & Giovannetti, Bruno Cara & da Silva, Marcos Eugênio, 2017. "Variance Premium and Implied Volatility in a Low-Liquidity Option Market," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 71(1), May.
  47. Niu, Zibo & Liu, Yuanyuan & Gao, Wang & Zhang, Hongwei, 2021. "The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China," Resources Policy, Elsevier, vol. 73(C).
  48. Cláudia Duarte & Sónia Cabral, 2016. "Nowcasting Portuguese tourism exports," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
  49. Ding, Lili & Zhao, Zhongchao & Han, Meng, 2021. "Probability density forecasts for steam coal prices in China: The role of high-frequency factors," Energy, Elsevier, vol. 220(C).
  50. Gong, Xu & Lin, Boqiang, 2018. "The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market," Energy Economics, Elsevier, vol. 74(C), pages 370-386.
  51. Byun, Suk Joon & Kim, Jun Sik, 2013. "The information content of risk-neutral skewness for volatility forecasting," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 142-161.
  52. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2020. "News and return volatility of Chinese bank stocks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 1095-1105.
  53. Cai, Mei-Ling & Chen, Zhang-HangJian & Li, Sai-Ping & Xiong, Xiong & Zhang, Wei & Yang, Ming-Yuan & Ren, Fei, 2022. "New volatility evolution model after extreme events," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
  54. Borjigin, Sumuya & Gao, Ting & Sun, Yafei & An, Biao, 2020. "For evil news rides fast, while good news baits later?—A network based analysis in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
  55. Dehua Shen & Andrew Urquhart & Pengfei Wang, 2020. "Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks," European Financial Management, European Financial Management Association, vol. 26(5), pages 1294-1323, November.
  56. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," CEPR Discussion Papers 11307, C.E.P.R. Discussion Papers.
  57. Vuong, Giang Thi Huong & Nguyen, Manh Huu & Huynh, Anh Ngoc Quang, 2022. "Volatility spillovers from the Chinese stock market to the U.S. stock market: The role of the COVID-19 pandemic," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
  58. Pan, Zhiyuan & Liu, Li, 2018. "Forecasting stock return volatility: A comparison between the roles of short-term and long-term leverage effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 168-180.
  59. Zhang, Chuanhai & Zhang, Zhengjun & Xu, Mengyu & Peng, Zhe, 2023. "Good and bad self-excitation: Asymmetric self-exciting jumps in Bitcoin returns," Economic Modelling, Elsevier, vol. 119(C).
  60. Alina Synyavska & Numan Ülkü, 2015. "'Leverage Effect' in country betas and volatilities?," Applied Economics Letters, Taylor & Francis Journals, vol. 22(11), pages 848-853, July.
  61. Lin, Hai & Lo, Ingrid & Qiao, Rui, 2021. "Macroeconomic news announcements and market efficiency: Evidence from the U.S. Treasury market," Journal of Banking & Finance, Elsevier, vol. 133(C).
  62. BenSaïda, Ahmed, 2019. "Good and bad volatility spillovers: An asymmetric connectedness," Journal of Financial Markets, Elsevier, vol. 43(C), pages 78-95.
  63. Yarovaya, Larisa & Brzeszczyński, Janusz & Lau, Chi Keung Marco, 2017. "Asymmetry in spillover effects: Evidence for international stock index futures markets," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 94-111.
  64. Ornthanalai, Chayawat, 2014. "Lévy jump risk: Evidence from options and returns," Journal of Financial Economics, Elsevier, vol. 112(1), pages 69-90.
  65. repec:ipg:wpaper:2014-053 is not listed on IDEAS
  66. Pyun, Sungjune, 2019. "Variance risk in aggregate stock returns and time-varying return predictability," Journal of Financial Economics, Elsevier, vol. 132(1), pages 150-174.
  67. Peter Reinhard Hansen & Chen Tong, 2022. "Option Pricing with Time-Varying Volatility Risk Aversion," Papers 2204.06943, arXiv.org, revised Aug 2024.
  68. Ma, Feng & Li, Yu & Liu, Li & Zhang, Yaojie, 2018. "Are low-frequency data really uninformative? A forecasting combination perspective," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 92-108.
  69. Alessio Brini & Jimmie Lenz, 2024. "A comparison of cryptocurrency volatility-benchmarking new and mature asset classes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-38, December.
  70. Dilip B. Madan & Wim Schoutens, 2020. "Self‐similarity in long‐horizon returns," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1368-1391, October.
  71. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
  72. Kim, Jun Sik & Ryu, Doojin, 2015. "Are the KOSPI 200 implied volatilities useful in value-at-risk models?," Emerging Markets Review, Elsevier, vol. 22(C), pages 43-64.
  73. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
  74. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
  75. Sheng, Lin Wen & Uddin, Gazi Salah & Sen, Ding & Hao, Zhu Shi, 2024. "The asymmetric volatility spillover across Shanghai, Hong Kong and the U.S. stock markets: A regime weighted measure and its forecast inference," International Review of Financial Analysis, Elsevier, vol. 91(C).
  76. Siddique, Md Abubakar & Nobanee, Haitham & Karim, Sitara & Naz, Farah, 2023. "Do green financial markets offset the risk of cryptocurrencies and carbon markets?," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 822-833.
  77. Mammen, Enno & Park, Byeong U. & Schienle, Melanie, 2012. "Additive models: Extensions and related models," SFB 649 Discussion Papers 2012-045, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  78. Xianning WANG & Jingrong DONG & Zhi XIAO & Guanjie HE, 2019. "A novel spatial mixed frequency forecasting model with application to Chinese regional GDP," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 54-77, June.
  79. Xu Gong & Boqiang Lin, 2021. "Effects of structural changes on the prediction of downside volatility in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(7), pages 1124-1153, July.
  80. Zhang, Hongwei & Demirer, Riza & Huang, Jianbai & Huang, Wanjun & Tahir Suleman, Muhammad, 2021. "Economic policy uncertainty and gold return dynamics: Evidence from high-frequency data," Resources Policy, Elsevier, vol. 72(C).
  81. Palandri, Alessandro, 2015. "Do negative and positive equity returns share the same volatility dynamics?," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 486-505.
  82. Caglayan, Mustafa Onur & Xue, Wenjun & Zhang, Liwen, 2020. "Global investigation on the country-level idiosyncratic volatility and its determinants," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 143-160.
  83. Gong, Xu & Lin, Boqiang, 2017. "Forecasting the good and bad uncertainties of crude oil prices using a HAR framework," Energy Economics, Elsevier, vol. 67(C), pages 315-327.
  84. Souropanis, Ioannis & Vivian, Andrew, 2023. "Forecasting realized volatility with wavelet decomposition," Journal of Empirical Finance, Elsevier, vol. 74(C).
  85. Ryan T. Ball & Eric Ghysels, 2018. "Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?," Management Science, INFORMS, vol. 64(10), pages 4936-4952, October.
  86. Feng Ma & Yu Wei & Wang Chen & Feng He, 2018. "Forecasting the volatility of crude oil futures using high-frequency data: further evidence," Empirical Economics, Springer, vol. 55(2), pages 653-678, September.
  87. Konstantinos Skindilias & Chia Lo, 2015. "Local volatility calibration during turbulent periods," Review of Quantitative Finance and Accounting, Springer, vol. 44(3), pages 425-444, April.
  88. Nasreen, Samia & Tiwari, Aviral Kumar & Goodell, John W. & Tedeschi, Marco, 2024. "Asymmetric and frequency-domain spillover effects among industrial metals, precious metals, and energy futures markets," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 1556-1592.
  89. 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.
  90. Mun, Kyung-Chun, 2012. "The joint response of stock and foreign exchange markets to macroeconomic surprises: Using US and Japanese data," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 383-394.
  91. Yi-Chiuan Wang & Yi-hao Lai & Jyh-Lin Wu, 2024. "Asymmetries in risk spillovers between currency and stock markets: Evidence from the CoVaR-copula approach," Review of Quantitative Finance and Accounting, Springer, vol. 63(3), pages 1083-1119, October.
  92. Alessio Brini & Jimmie Lenz, 2024. "A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes," Papers 2404.04962, arXiv.org.
  93. Byounghyun Jeon & Sung Won Seo & Jun Sik Kim, 2020. "Uncertainty and the volatility forecasting power of option‐implied volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(7), pages 1109-1126, July.
  94. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
  95. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
  96. 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.
  97. 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.
  98. 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.
  99. Shi, Yanlin & Liu, Wai-Man & Ho, Kin-Yip, 2016. "Public news arrival and the idiosyncratic volatility puzzle," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 159-172.
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