The statistical measure of return dispersion for a certain securities or market index is called volatility. Generally speaking, the greater the volatility, the larger the risk attached to the investment. For a number of reasons related to various market participants, volatility estimation is significant Individual and Institutional Investors are focusing more about on returns of their investment at lower risk, even they also equally bother about the risk. In this paper, ARIM (0 1 0) and ARIMA (0 1 1) models have been used for predicting the volatility of various selected sectoral indices of Bombay Stock Exchange (BSE) in India. Monthly closing prices of 10 indices for the last 11 years are considered for the study. The period of 11 years ranging from August 2012 to July 2023 being enveloped for this study. It is analysis descriptive Statistics that found highest-lowest risk returns, were applied ARIMA models for forecasting market volatility of each index, also identified stationarity and non-stationarity of selected sectoral indices of BSE in India.
Descriptive Statistics, stationarity process, non-stationarity process, Forecasting, ARIMA, Time Serie. Market Volatility, BSE
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