Implied Volatility and Equity Market Segmentation: Evidence on Volatility Spillovers in India Using Hybrid Deep Learning Models
Authors
Associate Professor, NSHM Business School NSHM Knowledge Campus, Kolkata-Group of Institutions, West Bengal (India)
Article Information
Publication Timeline
Submitted: 2026-03-09
Accepted: 2026-03-14
Published: 2026-03-27
Abstract
Periods of heightened uncertainty have become increasingly frequent in modern financial markets, intensifying the need for forward-looking measures that can anticipate volatility rather than merely describe it ex post. Implied volatility indices have emerged as prominent proxies for market fear, yet empirical evidence on how such fear propagates across different segments of equity markets remains limited, particularly in emerging economies. Against this backdrop, the present study examines whether India VIX functions as a leading indicator of volatility spillovers across Indian equity market capitalization tiers and whether such spillovers are heterogeneous and regime-dependent.
Keywords
India VIX; Volatility Spillovers; CNN–LSTM; Market Capitalization
Downloads
References
1. Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(4), 1593–1636. [Google Scholar] [Crossref]
2. Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and LSTM. PLoS ONE, 12(7), e0180944. [Google Scholar] [Crossref]
3. Bali, T. G., & Zhou, H. (2016). Risk, uncertainty, and expected returns. Journal of Financial and Quantitative Analysis, 51(3), 707–735. [Google Scholar] [Crossref]
4. Bekaert, G., Ehrmann, M., Fratzscher, M., & Mehl, A. (2014). The global crisis and equity market contagion. Journal of Finance, 69(6), 2597–2649. [Google Scholar] [Crossref]
5. Bhowmik, R., & Wang, S. (2020). Stock market volatility and return analysis: Evidence from India VIX. Economic Modelling, 86, 337–351. [Google Scholar] [Crossref]
6. Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623–685. [Google Scholar] [Crossref]
7. Dash, R., & Dash, P. K. (2021). Efficient stock price prediction using deep learning. Applied Soft Computing, 108, 107446. [Google Scholar] [Crossref]
8. Diebold, F. X., & Yılmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. [Google Scholar] [Crossref]
9. Engle, R. (2002). Dynamic conditional correlation. Journal of Business & Economic Statistics, 20(3), 339–350. [Google Scholar] [Crossref]
10. Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. [Google Scholar] [Crossref]
11. Fleming, J., Ostdiek, B., & Whaley, R. (1995). Predicting stock market volatility. Journal of Futures Markets, 15(1), 1–24. [Google Scholar] [Crossref]
12. Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence. Journal of Finance, 57(5), 2223–2261. [Google Scholar] [Crossref]
13. Ghosh, S., & Kanjilal, K. (2016). Co-movement of Indian stock market with global indices. Emerging Markets Review, 28, 59–71. [Google Scholar] [Crossref]
14. Giot, P. (2005). Relationships between implied volatility indices and stock returns. Journal of Portfolio Management, 31(3), 92–100. [Google Scholar] [Crossref]
15. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series. Econometrica, 57(2), 357–384. [Google Scholar] [Crossref]
16. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. [Google Scholar] [Crossref]
17. Kim, H. Y., & Won, C. H. (2018). Forecasting the volatility of stock price index. Expert Systems with Applications, 91, 1–11. [Google Scholar] [Crossref]
18. Kritzman, M., Li, Y., Page, S., & Rigobon, R. (2011). Principal components as a measure of systemic risk. Journal of Portfolio Management, 37(4), 112–126. [Google Scholar] [Crossref]
19. Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning. Philosophical Transactions of the Royal Society A, 379(2194), 20200209. [Google Scholar] [Crossref]
20. Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning. Applied Soft Computing, 90, 106181. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Financial Technology (Fintech): Current Research at The Cutting Edge
- Reforming Corporate Governance in Malaysia to Address Fraudulent Financial Reporting Cases
- Stock Market Efficiency and Economic Diversification in Nigeria and South Africa
- Financial Stability and Financial Performance of Small and Medium Tiered Deposit Taking Savings and Credit Cooperatives in Kenya.
- Regulator Sandboxes for DeFi: A Comparative Analysis of Policy Effectiveness in the EU, US, and Asia Pacific