  Volatility prediction in the financial market helps to understand the profit
and involved risks in investment. However, due to irregularities, high
fluctuations, and noise in the time series, predicting volatility poses a
challenging task. In the recent Covid-19 pandemic situation, volatility
prediction using complex intelligence techniques has attracted enormous
attention from researchers worldwide. In this paper, a novel and simple
approach based on the robust least squares method in two approaches a) with
least absolute residuals (LAR) and b) without LAR, have been applied to the
Chicago Board Options Exchange (CBOE) Volatility Index (VIX) for a period of
ten years. For a deeper analysis, the volatility time series has been
decomposed into long-term trends, and seasonal, and random fluctuations. The
data sets have been divided into parts viz. training data set and testing data
set. The validation results have been achieved using root mean square error
(RMSE) values. It has been found that robust least squares method with LAR
approach gives better results for volatility (RMSE = 0.01366) and its
components viz. long term trend (RMSE = 0.10087), seasonal (RMSE = 0.010343)
and remainder fluctuations (RMSE = 0.014783), respectively. For the first time,
generalized prediction equations for volatility and its three components have
been presented. Young researchers working in this domain can directly use the
presented prediction equations to understand their data sets.
