Range-Based Approach To Volatility Modelling And Forecasting Value-At-Risk

Abstract

The purpose of this thesis is to model and forecast value-at-risk based on range-measuring rather than the commonly acknowledged volatility models that are based on closing prices. The use of close-to-close prices in modelling and forecasting value-at-risk might not capture important intra-day information about the price movement. As a result, crucial price movement information is lost and consequently the model becomes less e cient. This thesis recommends the inclusion or range-measuring, described as the di erence between the highest and lowest prices of an underlying stock within a time interval, a day, to compute Value-at-Risk. The project uses data of an NSE-listed and trading company, SASN, between November 2009 and November 2019 on which the predictability of range-based and close-to-close estimates was established. It was observed that the values obtained by range-based models were more accurate than when only the daily closing prices are used. The range-based models successfully capture dynamics of the volatility and achieves improve performance relative to the GARCH-type models. These ndings are fairly consistent and can be extended to applications like portfolio optimization.