Asymmetry, realised volatility and stock return risk estimates

Abstract

In this paper we estimate minimum capital risk requirements for short and long positions with three investment horizons, using the traditional GARCH model and two other GARCH-type models that incorporate the possibility of asymmetric responses of volatility to price changes. We also address the problem of the extremely high estimated persistence of the GARCH model to generate observed volatility patterns by including realised volatility as an explanatory variable into the model's variance equation. The results suggest that the inclusion of realised volatility improves the GARCH forecastability as well as its ability to calculate accurate minimum capital risk requirements and makes it quite competitive when compared with asymmetric conditional heteroscedastic models such as the GJR and the EGARCH.

Are Realized Volatility Models Good Candidates for Alternative Value at Risk Prediction Strategies?

“…An alternative approach for accessing the informational content of realized volatility and realized power variation in VaR forecasting is to use them as explanatory variables in a GARCH model as in Fuertes et al (2009), Grané and Veiga (2007) and Koopman et al (2005) Fuertes et al, 2009;Koopman et al 2005). However, there is limited empirical evidence on the performance of the Augmented GARCH model in VaR forecasting applications (Grané and Veiga, 2007).…”

“…However, there is limited empirical evidence on the performance of the Augmented GARCH model in VaR forecasting applications (Grané and Veiga, 2007).…”