Modelling and analysis of volatility in time series data

Abstract

The comprehension of volatility is a crucial concept in analysing data. It is of greater importance for financial data since it furnishes key aspects such as return on investments and helps with effective hedging. The unpredictable nature of volatility causes heteroskedasticity which leads to difficulty in modelling. Consequently, time series models are desirable to predict volatility. An illustration of the same has been shown through an example of fitting time series models on the volatility of a listing from the National Stock Exchange (NSE). This paper also attempts to treat heteroskedasticity using Box-Cox transformations to achieve equal error variances prior to the modelling.