International Journal of Banking, Risk and Insurance

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Volatility Modeling and Forecasting for Banking Stock Returns


Affiliations
1 Department of Commerce, Mody Institute of Technology & Science, Sikar, Rajasthan, India
     

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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 ischolar_main 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, Forecasting
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  • Volatility Modeling and Forecasting for Banking Stock Returns

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Authors

Krishna Murari
Department of Commerce, Mody Institute of Technology & Science, Sikar, Rajasthan, India

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 ischolar_main 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, Forecasting

References