Abstract

Financial markets require an accurate estimate of asset volatility for various purposes such as risk management, decision-making and portfolio selection. Moreover, for risk management, volatility estimation is critical in Value-at-Risk (VaR) calculation models. However, there is still no consensus on a model that performs best in estimating volatility. This study proposes comparing volatility measures based on high-frequency data, such as RV and RRV, with heteroskedastic volatility models that use squared daily returns and daily closing prices. Four GARCH type models were implemented to estimate heteroskedastic volatility for the two most actively traded shares on the Brazilian stock exchange, using skewed generalized t (SGT) distribution and allowing flexibility for modeling the empirical distribution of these asymmetric financial data. Performed tests indicated no differential between the GARCH models and the high-frequency volatility measures used to estimate the VaR, indicating that both measures could be utilized for risk management purposes.