Abstract
O artigo tem por objetivo modelar a volatilidade diária de cinco dos ativos mais negociados na bolsa de valores de São Paulo. A abordagem utilizada é baseada em dados intra-diários e o estimador conhecido como variância realizada é adotado. As principais conclusões são: em primeiro lugar, os retornos diários padronizados pela volatilidade realizada são aproximadamente normais. Além disso, as log-volatilidades também apresentam distribuições bem próximas da normal. Finalmente, ao contrário da literatura corrente, não há evidências de memória longa na série de volatilidade e um simples modelo de memória curta é suficiente para modelar e prever as séries diárias de volatilidade.
Highlights
Given the rapid growth in financial markets and the continual development of new and more complex financial instruments, there is an ever-growing need for theoretical and empirical knowledge of the volatility in financial time series
Using five of the most actively traded stocks in the Sao Paulo Stock Exchange (Bovespa), this paper shows that the normality assumption commonly used in the risk management area to describe the distributions of returns standardized by volatilities is not compatible with volatilities estimated by exponentially weighted moving averages (EWMA) or GARCH models
In sharp contrast, when we use the information contained in high frequency data to construct the realized volatility measures, we attain the normality of the standardized returns, giving promise to improve on Value at Risk statistics
Summary
Given the rapid growth in financial markets and the continual development of new and more complex financial instruments, there is an ever-growing need for theoretical and empirical knowledge of the volatility in financial time series. Using five of the most actively traded stocks in the Sao Paulo Stock Exchange (Bovespa), this paper shows that the normality assumption commonly used in the risk management area to describe the distributions of returns standardized by volatilities is not compatible with volatilities estimated by EWMA or GARCH models. In sharp contrast, when we use the information contained in high frequency data to construct the realized volatility measures, we attain the normality of the standardized returns, giving promise to improve on Value at Risk statistics.
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