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June 7, 2026Iconic Research and Engineering Journals0 citations

Volatility and Return Dynamics of Indian Stock Market Indices

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AGArun Kr. GiriMAMehak Arora

Key Points

  • This research aims to explore the volatility and return dynamics of major Indian stock market indices during turbulent financial periods.
  • Analyzed six major Indian stock market indices from January 2020 to March 2026.
  • Employed GARCH(1,1) and EGARCH modelling, along with descriptive statistics and cross-index correlation matrices.
  • Examined market periods including the COVID-19 crash, bull market recovery, monetary tightening, and growth phases.
  • All indices showed negatively skewed, fat-tailed daily return distributions.
  • GARCH(1,1) volatility persistence coefficients ranged from 0.9855 to 0.9890, indicating near-integrated GARCH behaviour.
  • Cumulative returns varied from 53.5% for Nifty Bank to 205.1% for Nifty Midcap 150.

Abstract

This research paper investigates the volatility and return dynamics of six major Indian stock market indices BSE Sensex, NSE Nifty 50, Nifty Bank, Nifty IT, Nifty Midcap 150, and Nifty Small cap 250 — over the period January 2020 to March 2026. The study spans one of the most turbulent and structurally rich financial periods in modern Indian market history, encompassing the global COVID-19 pandemic crash of March 2020, the historic bull-market recovery of April 2020 to December 2021, the global monetary tightening and correction phase of 2022, a period of domestic resilience and consolidation in 2023–2024, and a phase of cautious but broadening growth in 2025–2026. Employing a combination of descriptive statistics, sub-period analysis, GARCH (1,1) and EGARCH modelling, rolling-window realised volatility, cross-index correlation matrices, and India VIX dynamics, this paper provides a multi-dimensional empirical characterisation of risk-return behaviour across distinct market regimes and sectoral segments. Key findings include: (i) all indices exhibit highly non-normal, negatively skewed, fat-tailed daily return distributions; (ii) GARCH(1,1) volatility persistence coefficients (α + β) range from 0.9855 to 0.9890 across all indices, confirming near-integrated GARCH behaviour; (iii) EGARCH models document significant leverage effects for all indices, with Nifty Bank exhibiting the strongest asymmetry (γ = –0.1147); (iv) cumulative returns over the study period range from 53.5% (Nifty Bank) to 205.1% (Nifty Midcap 150); and (v) cross-index correlations spike dramatically during market crisis episodes, eroding in-crisis diversification benefits. The paper derives actionable implications for portfolio risk management, asset allocation strategy, and regulatory oversight in the Indian capital markets.

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Cite This Study

Giri et al. (2026) studied this question.

synapsesocial.com/papers/6a250cd27def13d035e1d0d5https://doi.org/10.64388/irev9i12-1718653
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