Macroeconomic risk, asymmetric volatility and long-memory dynamics in Indian equity markets: Implications for financial project and risk management

This study tries to assess whether aggregated domestic and global macro-financial conditions can assist to explicate daily equity-market returns and volatility in India and considers the implications for financial risk and project-related decision making. Daily BSE and NSE index returns are analysed for January 2000–March 2024 using principal component analysis (PCA), EGARCH and FIEGARCH specifications, with event-period indicators for the Global Financial Crisis, demonetization, COVID-19 and the Russia–Ukraine war. The PCA results point out that the retained components summarize a large share of the common variation in the underlying macro-financial variables. In the conditional-mean equations, the domestic and global PCA factors are usually statistically insignificant, whereas market uncertainty measured by India VIX is significant in selected specifications. Event-period indicators divulge strong conditional-mean effects for the Global Financial Crisis and COVID-19, while demonetization is associated with a negative return effect. In the variance equations, the models identify substantial conditional heteroskedasticity and asymmetric volatility. The Student-t EGARCH specifications provide lower in-sample AIC values than the normal EGARCH and FIEGARCH alternatives. FIEGARCH estimates indicate statistically significant fractional integration, with a larger estimated long-memory parameter for BSE than NSE; however, the available model-comparison evidence does not establish that fractional persistence is superior to asymmetric short-memory modelling. The findings suggest that financial project managers, treasury functions and investment decision-makers should treat market uncertainty and crisis regimes as central risk-management inputs, while avoiding the assumption that low-frequency macroeconomic indicators have an immediate daily effect on equity risk.