Time Series Modelling of KSE 100-Index

The main objective of the study is to develop the Generalized Auto Regressive Conditional Heterosedasticity (GARCH) model in order to analyze stock returns of 100-Index of Karachi Stock Exchange (KSE) for the selected period. Moreover, after applying different GARCH models, this study also aims to select the best model on the basis of different criterion. The daily values of the 100-Index of Karachi Stock Exchange(KSE) have been collected over the period of 4 years and 9 months, starting from January 2009 to October 2013. we have 1200 total observations in the study. The objective was achieved by estimating different Auto Regressive Conditional Heterosedasticity (ARCH) model and its extension Generalized Auto Regressive Conditional Heterosedasticity (GARCH) model which further of many stages; identification of ARIMA model, estimation of identified of ARIMA and then checking of it. The goodness of ?t is assessed through the Akaike information criteria (AIC), Schwarz information criteria (SIC) and standard error (S.E.). Moreover, the parameters of the model are estimated using maximum likelihood method. The study concludes that GARCH(1,1) is the best model to capture the volatility of stock returns of KSE-100 Index. the This study is important for academician, researchers as well as for econometrician as it is expected that it will provide them knowledge and basis for further study in the same field or other related areas of study. This research article is also helpful for the policy makers in order to know the trend and patterns of Stock exchange thus guide the policy makers.