Predictability of Stock Return Volatility from GARCH Models

This paper focuses on the performance of various GARCH models in terms of their ability of delivering volatility forecasts for stock return data. Volatility forecasts obtained from a variety of mean and variance specifications in GARCH models are compared to a proxy of actual volatility calculated using daily data. In-sample tests suggest that a regression of volatility estimates on actual volatility produces Rs of less than 8%. An interesting by-product is evidence of significantly negative relation between unexpected volatility and stock returns. Finally, out-of-sample tests indicate that a simpler ARMA specification performs better than a GARCH-M model. JEL classification: C22, C55