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Purpose

The study examines the comparative dynamics of the betas of two thematic sector-specific indices, one representing the green sector (proxied by Nifty ESG index) and the other representing carbon-intensive sectors (proxied by the Metals and Oil & Gas indices), with the broader benchmark, Nifty 500. The yardstick of comparison comprises parameters of volatility, sensitivity, stability and risk-adjusted performance.

Design/methodology/approach

The study collects data for a ten-year period (daily closing prices, January 1, 2016–December 31, 2025) and applies the Kalman Filter to understand the time-varying characteristics of the indices, followed by an estimate of time-varying beta sensitivity using rolling regressions and a test of variability using rolling standard deviations. Parameter stability is tested using structural breaks, while risk-adjusted performance is evaluated using Sharpe and Treynor ratios and co-movement using the nonlinear autoregressive distributed lag (NARDL) methodology.

Findings

The results reveal higher rolling beta sensitivity, less variability and superior risk-adjusted performance of the green sector index as compared to carbon-intensive sector indices. With respect to stability, the green sector index was more stable than the Oil & Gas index but matched the stability of the Metals Index. However, cointegration among the indices was not proved. The study concludes that the green sector index performed better than carbon-intensive sector indices on almost all yardsticks, namely, sensitivity, variability and stability.

Research limitations/implications

The results reinforce green indices as a relatively less risky and highly stable investment option. The absence of cointegration presents significant diversification opportunities for investors, while for policymakers, the results point to a need for sector-specific policies rather than a uniform policy.

Originality/value

The novelty lies in making a time-varying comparative analysis of green and carbon-intensive sector indices with respect to parameters of sensitivity, stability and volatility in the Indian context.

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