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Which fear index matters for predicting US stock market volatilities: Text-counts or option based measurement?

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  • Zhu, Sha
  • Liu, Qiuhong
  • Wang, Yan
  • Wei, Yu
  • Wei, Guiwu

Abstract

Although VIX has long been recognized as an index to measure fear sentiment in US stock markets, a set of similar measurements called Equity Market Volatility (EMV) trackers are newly created based on the text-counts of newspaper articles including several keywords related to US economy or stock market volatility. In this paper, we use GARCH-MIDAS method to quantify the in-sample explanatory and out-of-sample predictive powers of these two kinds of fear indices in US stock markets. Our empirical results show that VIX has larger in-sample impacts on US stock market volatility than EMV trackers. However, the out-of-sample volatility predictive performances of EMV trackers are generally superior to VIX across different US stock indices and prediction time horizons. In addition, policy-related EMV tracker acts better than VIX and other EMV trackers in predicting volatilities of US stock markets.

Suggested Citation

  • Zhu, Sha & Liu, Qiuhong & Wang, Yan & Wei, Yu & Wei, Guiwu, 2019. "Which fear index matters for predicting US stock market volatilities: Text-counts or option based measurement?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    • Handle: RePEc:eee:phsmap:v:536:y:2019:i:c:s0378437119314645
    DOI: 10.1016/j.physa.2019.122567
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    as
    1. 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.
    2. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    3. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    4. 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.
    5. 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.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Filippo Petroni & Maurizio Serva, 2015. "Observability of Market Daily Volatility," Papers 1503.08032, arXiv.org.
    8. Hossein Asgharian & Ai Jun Hou & Farrukh Javed, 2013. "The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(7), pages 600-612, November.
    9. Liu, Zhicao & Ye, Yong & Ma, Feng & Liu, Jing, 2017. "Can economic policy uncertainty help to forecast the volatility: A multifractal perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 181-188.
    10. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    11. Pratap Chandra Pati & Parama Barai & Prabina Rajib, 2018. "Forecasting stock market volatility and information content of implied volatility index," Applied Economics, Taylor & Francis Journals, vol. 50(23), pages 2552-2568, May.
    12. James Kozyra & Camillo Lento, 2011. "Using VIX data to enhance technical trading signals," Applied Economics Letters, Taylor & Francis Journals, vol. 18(14), pages 1367-1370.
    13. Petroni, Filippo & Serva, Maurizio, 2016. "Observability of market daily volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 838-842.
    14. Fang, Libing & Qian, Yichuo & Chen, Ying & Yu, Honghai, 2018. "How does stock market volatility react to NVIX? Evidence from developed countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 490-499.
    15. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Kyle J. Kost, 2019. "Policy News and Stock Market Volatility," NBER Working Papers 25720, National Bureau of Economic Research, Inc.
    16. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    17. Wei, Yu & Yu, Qianwen & Liu, Jing & Cao, Yang, 2018. "Hot money and China’s stock market volatility: Further evidence using the GARCH–MIDAS model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 923-930.
    18. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    19. Lahmiri, Salim, 2017. "On fractality and chaos in Moroccan family business stock returns and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 29-39.
    20. Wei-Xing Zhou, 2008. "Multifractal detrended cross-correlation analysis for two nonstationary signals," Papers 0803.2773, arXiv.org.
    21. Wei, Yu & Qin, Songkun & Li, Xiafei & Zhu, Sha & Wei, Guiwu, 2019. "Oil price fluctuation, stock market and macroeconomic fundamentals: Evidence from China before and after the financial crisis," Finance Research Letters, Elsevier, vol. 30(C), pages 23-29.
    22. Yu, Miao, 2019. "Forecasting Bitcoin volatility: The role of leverage effect and uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    23. 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.
    24. Conrad, Christian & Loch, Karin, 2015. "The variance risk premium and fundamental uncertainty," Economics Letters, Elsevier, vol. 132(C), pages 56-60.
    25. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    26. Mahmod Qadan & Joseph Yagil, 2012. "Fear sentiments and gold price: testing causality in-mean and in-variance," Applied Economics Letters, Taylor & Francis Journals, vol. 19(4), pages 363-366, March.
    27. 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.
    28. 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.
    29. Mo, Bin & Mu, Jinqi & Zhang, Bilin, 2019. "The relationship between news-based implied volatility and volatility of US stock market: What can we learn from multiscale perspective?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    30. Xu, Weiju & Wang, Jiqian & Ma, Feng & Lu, Xinjie, 2019. "Forecast the realized range-based volatility: The role of investor sentiment and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    31. Chen, Hongtao & Liu, Li & Li, Xiaolei, 2018. "The predictive content of CBOE crude oil volatility index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 837-850.
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