Abstract Return is the major attribute of an investment asset that can be considered as a random variable. The variability in return can be expressed as volatility. Forecasting volatility and modelling are the most prolifc areas for the research. Volatility and Leverage effect are the two crucial stipulations to study market contradictions and trends that prevail for a drawn-out period. It is observed that when volatility beams the markets soar and when markets roar the volatility fades away. Leverage has a larger scope in managing volatility when investors tend to shuffe their positions. This literature aims to identify the volatility clustering and leverage effect caused to NSE NIFTY 50 index. The study contrasts volatility clustering using symmetric model of i.e., GARCH (1,1). Leverage effects is studied and compared using TGARCH and EGARCH models. Keywords: Asymmetric Volatility, GARCH Models, Leverage Effect, Volatility Clustering JEL Classifcation: C01, C22, C5, E22, E27, G1, G14 A Study on Unfolding Volatility and Leverage Effect in Indian Stock Market Shabarisha Narayan*, Madegowda J.** Introducton Volatlity Volatility (Conditional Variance) is a key structure for pricing of fnancial instruments. Volatility forecasting and modelling is decisive for option pricing, management of risk and portfolio management. Volatility refers to the spread of all likely outcomes of an uncertain variable (Poon, 2005). Statistically, volatility is frequently measured as the sample standard deviation ∑ = − − = T t t r T 1 2 ) ( 1 1 ˆ µ σ Where r t is the return on day t, and μ is the average return over the T-day period. Sometimes, variance, σ 2 , can also * Assistant Professor, School of Business Studies and Social Sciences, Christ (Deemed to be University), Bengaluru, Karnataka, India. Email: shabarishanarayan1388@gmail.com ** Dean (Academic), Akshara Institute of Management Studies, Shimoga, Karnataka, India. Email: madegowdaj@gmail.com be used as a volatility measure. As variance is simply the square of standard deviation, there is no difference that which measure we use for comparing volatility of two investment assets. However, variance is much less invariable and less enviable than standard deviation as an object for volatility forecasting and modelling. Hence, Standard deviation is more expedient and instinctive when we talk about volatility. The ARCH (Engle, 1982) and GARCH (Bollerslev, 1986) models portray the phenomenon of volatility clustering to be more accurate measure of risk. ARCH model elucidated the constancy of the return in the time series data. GARCH model explained the heteroskedasticity of the Return Sequence residuals. Leverage Efect The existing return and potential volatility have negative correlation among themselves, which designates bad news will cause aggressive fuctuations as compare to good news and hence called as Leverage Effects (Black, 1976). In other words, positive and negative information lead to diverse level of effect to volatility in stock returns. Asymmetric models like EGARCH, TGARCH and PGARCH EGARCH analyzes the effect on stock volatility caused by asymmetric conditional heteroskedasticity on absorbing different information in the market. Review of Literature Modelling volatility of a fnancial time series has become obvious an important area for research. The time series are found to depend on their own past value (autoregressive), depending on past information (conditional) and exhibit non-constant variance (heteroskedasticity). It has been found that the stock market volatility changes with International Journal of Financial Management 8 (4) 2018, 07-14 http://publishingindia.com/ijfm/