Volatility Modeling and Forecasting for Banking Stock Returns
Published: 2013
Author(s) Name: Krishna Murari |
Author(s) Affiliation: Assistant Prof., Department of Commerce, Mody Institute of Tech. & Science, Sikar, Rajasthan, India
Locked
Subscribed
Available for All
Abstract
In this paper, an attempt has been made to model and
forecast the short term volatility of the Indian banking
sector. A popular banking sector CNX bank index of
national stock exchange of India (NSE) which includes
12 most liquid and large capitalized Indian banking
stocks is used as a time series. Data have been
collected since the inception of the index i.e. January
2000; a total of 3122 observations up to the period of
June 2013, are used in modeling the volatility of the
banking stock returns using univariate Box-Jenkins or
ARIMA model. ADF test and unit root testing is done to
know the stationarity of the series, later the AR(p) and
MA(q) orders are identified with the help of minimum
information criterion as suggested by Hannan-
Rissanen. As per the analysis, ARIMA (1,0,2) model
was found to be the best fit to forecast the volatility of
bank stock returns. The final equation for the model is
which can be helpful to the investors and speculators
in taking their short run buying and selling decisions for
bank stocks.
Keywords: CNX Bank Index, Bank Stock Returns, Stationarity, Volatility, ARIMA Modeling, Forecasti
View PDF