A Monte Carlo Simulation Approach to Assessing the Risk-Return Trade-off in ESG Investment Portfolios
This study asks whether environmental, social, and governance (ESG) screening changes the risk-return profile of an Indian large-cap equity portfolio. The Nifty 100 ESG index is compared with its unscreened parent, the Nifty 100, so the only systematic difference is the ESG screen and reweighting applied to a common constituent universe; the Nifty 50 is a secondary reference. Daily data from January 2021 to May 2026 show significant excess kurtosis and ARCH effects, so a constant-conditional-correlation GARCH(1,1) model with Student's t innovations generates correlated one-year price paths, with tail dependence imposed through a Cholesky-factorized multivariate-t shock. Headline tail-risk figures average five independent 100,000-path runs. A deterministic grid of 1,001 portfolios spanning 0% to 100% ESG weight is assessed on the Sharpe ratio, annualized volatility, Value at Risk and Conditional Value at Risk at 99%, the Sortino ratio, and downside deviation, at a risk-free rate and minimum acceptable return of 6.5%. Against the matched parent, daily returns correlate at 0.978, annualized volatility is 14.5% for both, and mean daily returns are identical to three decimal places (0.0453%). The optimal ESG weight is therefore interior, ranging from 50.7% to 68.1%, and annualized return holds at 11.4% across the efficient set. A Jobson-Korkie test with the Memmel correction finds no difference in Sharpe ratios (z = 0.0045, p = 0.996), corroborated by paired t and Newey-West HAC tests. Against the properly matched benchmark, ESG screening had no measurable effect on return, risk, or tail risk over this sample.