Estimating and Forecasting Volatility Using ARIMA Model: A Study on NSE, India.
Posted: 5 Jun 2019
Date Written: May 10, 2019
Volatility has been used as an indirect means for predicting risk accompanied with an asset. Volatility explains the variations in returns. Forecasting volatility has been a stimulating problem in the financial systems. This study examined the different volatility estimators and determined the most efficient volatility estimator. The study described the accuracy of the forecasting technique with respect to various volatility estimators. The methodology of volatility estimation included Close, Garman-Klass, Parkinson, Roger-Satchell, and Yang-Zhang methods and forecasting was done through the ARIMA technique. The study evaluated the efficiency and bias of various volatility estimators. The comparative analyses based on various error measuring parameters like ME, RMSE, MAE, MPE, MAPE, MASE, and ACF1 gave the accuracy of forecasting with the best volatility estimator. Out of five volatility estimators analyzed over a period of 10 years and after critically examining them for forecasting volatility, the research obtained Parkinson estimator as the most efficient volatility estimator. Based on various error measuring parameters, Parkinson estimator was found to be the most accurate estimator based on RMSE, MPE, and MASE in forecasting through the ARIMA technique. The study suggested that the forecasted values were accurate based on the values of MAE and RMSE. This research was conducted in order to meet the demand of knowing the most efficient volatility estimator for forecasting volatility with high accuracy by traders, option practitioners, and various players of the stock market.
Keywords: NSE, Volatility, Forecasting, CNX Nifty Index, Volatility Estimators, ARIMA
JEL Classification: C22, C53, C58, G17
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