STOCK MARKET VOLATILITY IN INDIA: A CASE OF SELECT SCRIPTS
STOCK MARKET VOLATILITY IN INDIA:
A CASE OF SELECT SCRIPTS
Puja Padhi*
Abstract
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.
STOCK MARKET VOLATILITY IN INDIA:
A CASE OF SELECT SCRIPTS
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
A CASE OF SELECT SCRIPTS
Puja Padhi*
Abstract
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.
STOCK MARKET VOLATILITY IN INDIA:
A CASE OF SELECT SCRIPTS
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
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