Volatility forecasting: the role of internet search activity and implied volatility

被引:1
|
作者
Basistha, Arabinda [1 ]
Kurov, Alexander [2 ]
Wolfe, Marketa [3 ]
机构
[1] West Virginia Univ, John Chambers Coll Business & Econ, Dept Econ, 1601 Univ Ave, Morgantown, WV 26506 USA
[2] West Virginia Univ, John Chambers Coll Business & Econ, Dept Finance, 1601 Univ Ave, Morgantown, WV 26506 USA
[3] Skidmore Coll, Dept Econ, 815 North Broadway, Saratoga Springs, NY 12866 USA
来源
JOURNAL OF RISK MODEL VALIDATION | 2020年 / 14卷 / 01期
关键词
volatility forecasting; realized volatility; implied volatility; internet search activity; Google Trends search volume index; information; INVESTOR ATTENTION; STOCK; EXCHANGE; PERFORMANCE; INFORMATION; PREDICTION; VOLUME; TESTS; JUMPS;
D O I
10.21314/JRMV.2018.218
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
Recent empirical literature shows that internet search activity is closely associated with volatility prediction in financial and commodity markets. In this study, we search for a benchmark model with available market-based predictors to evaluate the net contribution of internet search activity data in forecasting volatility. We conduct in-sample analysis and out-of-sample forecasting analysis robust to window size in multiple markets for robust model validation. The predictive power of internet search activity data disappears in the financial markets and substantially diminishes in the commodity markets once the model includes implied volatility. A further common component analysis shows that most of the predictive information contained in internet search activity data is also present in implied volatility, while implied volatility has additional predictive information that is not contained in the internet search activity data.
引用
收藏
页码:35 / 63
页数:29
相关论文
共 50 条
  • [1] Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility
    Kambouroudis, Dimos S.
    McMillan, David G.
    Tsakou, Katerina
    [J]. JOURNAL OF FUTURES MARKETS, 2021, 41 (10) : 1618 - 1639
  • [2] Intraday volatility forecasting from implied volatility
    Byun, Suk Joon
    Rhee, Dong Woo
    Kim, Sol
    [J]. INTERNATIONAL JOURNAL OF MANAGERIAL FINANCE, 2011, 7 (01) : 83 - +
  • [3] Volatility Forecasting : Model-Free Implied Volatility
    Cheng, Jing Fei
    Lu, Gui Bin
    [J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON EDUCATION, MANAGEMENT, COMMERCE AND SOCIETY, 2015, 17 : 498 - 501
  • [4] Comparison of Realized Measure and Implied Volatility in Forecasting Volatility
    Han, Heejoon
    Park, Myung D.
    [J]. JOURNAL OF FORECASTING, 2013, 32 (06) : 522 - 533
  • [5] The Information Content of Intraday Implied Volatility for Volatility Forecasting
    Wang, Yaw-Huei
    Wang, Yun-Yi
    [J]. JOURNAL OF FORECASTING, 2016, 35 (02) : 167 - 178
  • [6] Forecasting stock market volatility using implied volatility
    He, Peng
    Shing-Toung, Stephen
    [J]. 2007 AMERICAN CONTROL CONFERENCE, VOLS 1-13, 2007, : 2648 - +
  • [7] Uncertainty and the volatility forecasting power of option-implied volatility
    Jeon, Byounghyun
    Seo, Sung Won
    Kim, Jun Sik
    [J]. JOURNAL OF FUTURES MARKETS, 2020, 40 (07) : 1109 - 1126
  • [8] Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models
    Kambouroudis, Dimos S.
    McMillan, David G.
    Tsakou, Katerina
    [J]. JOURNAL OF FUTURES MARKETS, 2016, 36 (12) : 1127 - 1163
  • [9] Implied correlation indices and volatility forecasting
    Fink, Holger
    Geppert, Sabrina
    [J]. APPLIED ECONOMICS LETTERS, 2017, 24 (09) : 584 - 588
  • [10] Is implied volatility more informative for forecasting realized volatility: An international perspective
    Liang, Chao
    Wei, Yu
    Zhang, Yaojie
    [J]. JOURNAL OF FORECASTING, 2020, 39 (08) : 1253 - 1276