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...

Prediction intervals in state-space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, with the true parameters substituted by consistent estimates. This approach has two limitations. First, it does not incorporate the uncertainty...

The main goal when fitting GARCH models to conditionally heteroscedastic time series is to estimate the underlying volatilities. It is well known that outliers affect the estimation of the GARCH parameters. However, little is known about their effects when estimating volatilities. In this paper,...

The objective of this paper is to analyze the finite sample performance of two variants of the likelihood ratio test for detecting a level shift in uncorrelated conditionally heteroscedastic time series. We show that the behavior of the likelihood ratio test is not appropriate in this context...

This paper analyzes the effects caused by outliers on the identification and estimation of GARCH models. We show that outliers can lead to detect spurious conditional heteroscedasticity and can also hide genuine ARCH effects. First, we derive the asymptotic biases caused by outliers on 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 case of partial observations with asymmetric errors (non-Gaussian) in dynamic systems is studied and an approximation for the solution is given. Controlled quadratic linear dynamic systems are considered. We also consider the situation with outliers, and a robust approximation for this case...

Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, where the true parameters are substituted by consistent estimates. This approach has two limitations. First, it does not incorporate the...

The reduced form of the local level model with conditionally heteroscedastic GARCH(1,1) noises is analyzed. We show that the IMA-GARCH model is a good alternative but its conditional heteroscedasticity is weaker than this of the unobserved disturbances.

This article addresses the problem of forecasting portfolio value-at-risk (VaR) with multivariate GARCH models vis-à-vis univariate models. Existing literature has tried to answer this question by analyzing only small portfolios and using a testing framework not appropriate for ranking VaR...