International Journal of Research and Scientific Innovation (IJRSI)
Implied Volatility and Equity Market Segmentation: Evidence on Volatility Spillovers in India Using Hybrid Deep Learning Models
Published March 27, 2026 • Vol. 13, Issue 3, pp. 477–500Open Access
DOI: 10.51244/IJRSI.2026.1303000043
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
| Journal | International Journal of Research and Scientific Innovation (IJRSI) |
|---|---|
| ISSN | 2321-2705 |
| Volume / Issue | Volume 13, Issue 3 |
| Pages | 477–500 |
| Publication date | March 27, 2026 |
| DOI | 10.51244/IJRSI.2026.1303000043 |
| Publisher | RSIS International |
| License | Open Access |
How to cite this article
Rajib Bhattacharya (2026). Implied Volatility and Equity Market Segmentation: Evidence on Volatility Spillovers in India Using Hybrid Deep Learning Models. International Journal of Research and Scientific Innovation (IJRSI), 13(3), 477-500. https://doi.org/10.51244/IJRSI.2026.1303000043
BibTeX
@article{Rajib2026,
title = {Implied Volatility and Equity Market Segmentation: Evidence on Volatility Spillovers in India Using Hybrid Deep Learning Models},
author = {Rajib Bhattacharya},
journal = {International Journal of Research and Scientific Innovation (IJRSI)},
volume = {13},
number = {3},
pages = {477--500},
year = {2026},
doi = {10.51244/IJRSI.2026.1303000043},
publisher = {RSIS International}
}