There exists a vast literature on modeling and estimating aggregate stock market volatility over the past decade [e.g., Choudhry (1996); Mecagni and Sourial (1999) and Kabir, et al. (2000)]. Motivations for undertaking this exercise have been varied. Many value-at-risk models for measuring market risk require the estimation of volatility parameter. Portfolio diversifications and hedging strategies also require information on volatility as a key input. Volatility is defined as tendency of the assets price to fluctuate either up or down. Increased volatility is perceived as indicating a rise in financial risk which can adversely affect investor assets and wealth. It is observed that when stock market exhibit increased volatility there is a tendency on part of the investors to lose confidence in the market and they tend to exit the market. The nexus between volatility and economic fundamentals is still a moot point. Stock prices reflect information and quicker they are in absorbing accurately new information, more efficient is the stock market in allocating resources. The increase in volatility can be attributed to absorption of new information about economic fundamentals or some expectations about them. This kind of volatility is not harmful as there is no social cost associated with it. But if increased volatility is not explained by the level indicated by the fundamental economic factors, there is a tendency that stocks will be mispriced and this will lead to misallocation of resources [Karmaka (2006)].