O Uso de Dados de Alta Freqüência na Estimação da Volatilidade e do Valor em Risco para o Ibovespa

Abstract

This paper investigates the use of high frequency data in the estimation of daily and intradaily volatility, in order to compute value at risk (VaR) forecasts for the IBOVESPA. GARCH models and deterministic methods for the filtering of seasonal patterns have been used in the computation of intraday volatility and VaR forecasts. For daily VaR two simple methods seek to extract the volatility information conveyed by the high frequency data. The first method is based on the sample standard deviation with a moving window, while the second is based on exponencially wheighted moving average. Both methods tested presented good performance. For intraday VaR the results indicate that the filtering of the seasonal pattern is a fundamental step in obtaining useful forecasts of volatility and VaR.