Measuring Asymmetric Volatility of Bank Nifty Index using Egarch Model

Abstract

The paper examines the significance of volatility models in forecasting future volatility for effective portfolio allocation and risk reduction. It compares the performance of symmetric and asymmetric models in estimating conditional variance, and linear versus non-linear GARCH models. Using secondary data from the National Stock Exchange's Nifty Bank index, the study applies Exponential GARCH (1,1) to measure asymmetric volatility and conducts various tests to confirm the suitability of the data for analysis. The results indicate clustering of volatility in Nifty Bank returns over a four-year period, with the presence of asymmetrical effects and leverage constants. The study concludes that negative information has a greater impact on volatility than positive surprises, and that market fluctuations are inversely related to stock market performance. This research provides valuable insights for portfolio selection, risk management, and asset pricing in the context of increasing volatility across various markets and industries.