India VIX and Forecasting Ability of Symmetric and Asymmetric GARCH Models

Abstract

Volatility forecasting plays an important role in decisions concerning risk assessment, asset valuation and monetary policy formulation. Forecasting implied volatility is a key parameter in pricing of options. Thus, through this paper we attempt to model and test the predictive ability of symmetric GARCH(1,1) and asymmetric TGARCH(1,1) and EGARCH(1,1) models in forecasting the India Volatility Index (VIX). The estimated results confirm the dependency of volatility on its past behavior. It discloses that conditional variance takes longer to disintegrate and the innovations to it are highly persistent in nature. The predictive ability of these models to forecast the direction of the VIX series is evaluated by employing a standard (symmetric) loss function, such as the root mean square error (RMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE) and Theil’s inequality coefficient. The results show that the GARCH(1,1) provides superior forecasts compared to other models.