This study investigates the relative performance of linear versus nonlinear methods to predict volatility and return in equity markets. The study is performed on the EAGLEs and NEST markets, including China, India, Indonesia Pakistan, Bangladesh and Malaysia by using daily data of equity markets from the period January 4, 2000 to December 30, 2010. Nonlinear and asymmetric ARCH effects have been test by Lagrange Multiplier test. A range of models from random walk model to multifaceted ARCH class models are used to predict volatility. The results reveal that MA (1) model ranks first with use of RMSE criterion in linear models. With regards to nonlinear models for predicating stock return volatility, the ARCH, GARCH-in-Mean (1, 1) model and EGARCH (1, 1) model perform well. GARCH-in- Mean model outperforms on the basis of AIC, SIC and Log Likelihood method. It is concluded that GARCH specification is best in performance to capture the volatility. GARCH in mean model is extended with the macroeconomic variables in the variance equation for SS, BSE, JCI, KSE, KLSE and DSE. The macroeconomic variables include CPI, Term Structure of interest rate, industrial production and oil prices.Data for Macroeconomic variable is on monthly basis for the period Jan 2000 to Dec 2010.For SS, BSE, JCI, DSE, KLSE and KSE markets the conditional mean is significant and models the persistency in long run scenario and suggests for an integrated process. The model indicates that oil price have positive impact on volatility for SS. For BSE change in industrial production index and interest rate change have negative coefficients which indicate that industrial growth and increase in interest rate change has negative relationship with the volatility for this economy. For JCI the model indicates that change in growth in industrial production has positive impact on volatility. For KSE, ARCH and GARCH terms are not significant but growth rate in real sector and oil price has significant impact on volatility. However DSE has no significant results. For KSE the model indicates that inflation has positive impact on volatility but change in oil price has negative effect on volatility.Bullish market effect is quite significant in explaining the volatility capturing ability for all the equity markets. The TGARCH(1,1) model isestimated for SS, BSE, JCI, DSE KLSE and KSE returns series and results indicate that asymmetric effect exists for all the equity markets which indicates the presence of leverage effect. Study concludes that TGARCH (1,1) model is a potential envoy of the asymmetric conditional volatility procedure for the daily frequency of the data regarding to equity markets of SS, BSE, JCI, DSE, KLSE, and KLSE. Further GARCH-in-mean model is extended with value at risk that indicates the variables for variance equation are statistically significant and the VaR have significant impact on all equity markets in explaining the conditional volatility. In Last GARCH-in-Mean Model is extended with the semi-variance and results indicate that the downside risk causes rise in the volatility. It has ability to capture the asymmetric behavior of equity returns and reports the fat tails of the returns. It is concluded that volatility plays a significant role in asset price determination. Keywords: Conditional volatility, linear, nonlinear, Asymmetric effect, Macroeconomic Variables, Bullish, Value at Risk, Semi-Variance.