The identification of asymmetric conditional heteroscedasticity is often based on samplecross-correlations between past and squared observations. In this paper we analyse theeffects of outliers on these cross-correlations and, consequently, on the identification ofasymmetric volatilities. We...

The autocorrelation function (acf) of powered absolute returns and their cross-correlations with original returns are derived, for any value of the power parameter, in the context of long-memory stochastic volatility models with leverage effect and Gaussian noises. These autocorrelations and...

The autocorrelations of log-squared, squared, and absolute financial returns are often used to infer the dynamic properties of the underlying volatility. This article shows that, in the context of long-memory stochastic volatility models, these autocorrelations are smaller than the...

This paper provides a review of time series models with long memory in the mean and conditional variance, with special attention to Fractionally Integrated ARMA processes (ARFIMA) and fractionally integrated GARCH and SV processes. Their more important properties are reviewed and its application...

The autocorrelations of log-squared, squared, and absolute financial returns are often used to infer the dynamic properties of the underlying volatility. This article shows that, in the context of long-memory stochastic volatility models, these autocorrelations are smaller than the...

The autocorrelations of log-squared, squared, and absolute financial returns are often used to infer the dynamic properties of the underlying volatility. This article shows that, in the context of long-memory stochastic volatility models, these autocorrelations are smaller than the...