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Role of the Global Volatility Indices in Predicting the Volatility Index of the Indian Economy

Author

Listed:
  • Akhilesh Prasad

    (IFMR Graduate School of Business, Krea University, Sri City 517646, India)

  • Priti Bakhshi

    (S. P. Jain School of Global Management, Mumbai 400070, India)

Abstract

Movements in the volatility index of the Indian economy are influenced by global volatility indices (fear index). This study evaluates the influence of various global implied volatility indices in forecasting the day-to-day binary movements in the implied volatility index of India, denoted by the symbol ‘India VIX’. Historical daily data from 18 September, 2009, to 2 December, 2021, was acquired, and the target labels were created from changes in the India VIX. A set of classifiers, consisting of Logistic Regression, Random Forest and Extreme Gradient Boosting (XG Boost), were applied to rank the feature variables according to their importance. This study revealed that India’s VIX was impacted most by the previous day’s changes in the closing value of the US implied volatility indices, except for the Chicago Board Options Exchange (CBOE) Eurocurrency volatility index. Additionally, the Eurozone implied volatility index was also important. However, the implied volatility indices of Australian Hang Seng and Japan were the least important. This study’s outcomes help Indian traders in creating a watch list of important volatility indices.

Suggested Citation

  • Akhilesh Prasad & Priti Bakhshi, 2022. "Role of the Global Volatility Indices in Predicting the Volatility Index of the Indian Economy," Risks, MDPI, vol. 10(12), pages 1-18, November.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:12:p:223-:d:980767
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    References listed on IDEAS

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    1. Suparna Nandy (Pal) & Arup Kr. Chattopadhyay, 2019. "‘Indian Stock Market Volatility’: A Study of Inter-linkages and Spillover Effects," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 18(2_suppl), pages 183-212, August.
    2. Onan, Mustafa & Salih, Aslihan & Yasar, Burze, 2014. "Impact of macroeconomic announcements on implied volatility slope of SPX options and VIX," Finance Research Letters, Elsevier, vol. 11(4), pages 454-462.
    3. Edson Kambeu, 2019. "Trading volume as a predictor of market movement: An application of Logistic regression in the R environment," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 8(2), pages 57-69, April.
    4. Bhuyan, Rafiqul & Robbani, Mohammad G. & Talukdar, Bakhtear & Jain, Ajeet, 2016. "Information transmission and dynamics of stock price movements: An empirical analysis of BRICS and US stock markets," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 180-195.
    5. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    6. Mensi, Walid & Hammoudeh, Shawkat & Reboredo, Juan Carlos & Nguyen, Duc Khuong, 2014. "Do global factors impact BRICS stock markets? A quantile regression approach," Emerging Markets Review, Elsevier, vol. 19(C), pages 1-17.
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