Topics: Stock market, Autoregressive conditional heteroskedasticity, Stochastic volatility Pages: 297 (11236 words) Published: October 4, 2013
Puja Padhi*


An attempt has been made in this paper to explain the stock market volatility at the individual script level and at the aggregate indices level. The empirical analysis has been done by using Autoregressive conditional heteroscedasticity model (ARCH), Generalised autoregressive conditional heteroscedasticity (GARCH) model and ARCH in Mean model and it is based on daily data for the time period from January 1990 to November 2004. The analysis reveals the same trend of volatility in the case of aggregate indices and five different sectors such as electrical, machinery, mining, non-metalic and power plant sector. The GARCH (1,1) model is persistent for all the five aggregate indices and individual company.


Lecturer, Dept of Economics, Pondicherry University, Pondicherry 605 014, India. The author is thankful to S.V.Seshaiah for his suggestion and comments in the earlier draft of the paper.


Section I: Introduction

In general terms, volatility may be described as a phenomenon, which characterizes changeableness of a variable under consideration. Volatility is associated with unpredictability and uncertainty. In literature on stock market, the term is synonymous with risk, and hence high volatility is thought of as a symptom of market disruption whereby securities are not being priced fairly and the capital market not functioning as well as it should be. As a concept volatility is simple and intuitive. It measures the variability or dispersion about a central tendency. However, there are some subtleties that make volatility challenging to analyse and implement. Since volatility is a standard measure of financial vulnerability, it plays a key role in assessing the risk/return tradeoffs. Policy makers rely on market estimates of volatility as a barometer of the vulnerability of the financial markets. The existence of excessive volatility or “noise” also undermines the usefulness of stock prices as a “signal” about the true intrinsic value of a firm, a concept that is core to the paradigm of international efficiency of the markets.

Considerable research effort has already gone into modeling time–varying conditional heteroskedastic asset returns. It is important because if both returns and volatility can be forecasted, then it is possible to construct dynamic asset allocation models that use time dependent mean–variance optimization over each period. Financial econometrics suggests the use of non- linear time series structures to model the attitude of investors toward risk and expected return. In this context, Bera and Higgins (1993) remarked, “a major contribution of the ARCH literature is the finding that apparent changes in the volatility of the economic time series may be predictable, and result from a specific type of non-linear dependence rather than exogenous structural changes in variables.” When the variance is not constant, it is more likely that there are more outliers than expected


from the normal distribution, i.e., when a process is heteroskedastic it will follow heavy– tailed or outlier–prone probability distribution. According to Mc Nees (1979), “the inherent uncertainty or randomness associated with different forecast periods seem to vary over time, and large and small errors tend to cluster together”.

Although there is a plethora of research concerning stock market volatility, most of the studies have been done for stock market of the developed country as a whole, making use of aggregate information data. There are varying few studies, which have gone into the volatility issues at the level of specific industries or the companies in an industry. More specifically, this paper is an attempt towards explaining the stock market volatility at the individual script level and at the aggregate indices level. In the light of the above, the...

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