VOLATILITY MODELLING USING GARCH FAMILY MODELS: A COMPARISON OF NIFTY ESG 100 INDEX AND MSCI EUROPE ESG LEADERS INDEX

Abstract

This study explores the volatility dynamics of the Nifty ESG 100 Index using the ARCH family of models. The Autoregressive Conditional Heteroscedasticity (ARCH) model and its generalizations, particularly the Generalized ARCH (GARCH) model, have proven to be effective in capturing time-varying volatility in financial time series. The Nifty ESG 100 Index, which tracks companies with high environmental, social, and governance (ESG) standards, is becoming increasingly relevant in India’s evolving financial markets. By employing ARCH, GARCH, and extensions like EGARCH and TGARCH, this paper seeks to model and forecast the index’s volatility, considering the persistence and asymmetric behavior of market fluctuations. The analysis aims to provide insights into the volatility patterns that can assist investors and portfolio managers in making informed risk management decisions. Diagnostic tests confirm the validity of the models, and out-of-sample volatility forecasts highlight the robustness of GARCH-type models in capturing volatility clustering and persistence for the Nifty ESG 100 Index.