Forecasting Volatility – Evidence from Indian Stock and Forex Markets

Abstract

Volatility forecasting is an important area of research in financial markets and lot of effort has been expended in improving volatility models since better forecasts translate in to better pricing of options and better risk management. In this direction this paper attempts to evaluate the ability of ten different statistical and econometric volatility forecasting models in the context of Indian stock and forex markets. These competing models are evaluated on the basis of two categories of evaluation measures – symmetric and asymmetric error statistics. Based on an out of the sample forecasts and a majority of evaluation measures we find that GARCH (4, 1) and EWMA methods will lead to better volatility forecasts in the Indian stock market and the GARCH (5, 1) will achieve the same in the forex market. The same models perform better on the basis of asymmetric error statistics also.