This paper analyses how outliers affect the identification of conditional heteroscedasticity and the estimation of generalized autoregressive conditionally heteroscedastic (GARCH) models. First, we derive the asymptotic biases of the sample autocorrelations of squared observations generated by...

We propose recent functional data analysis techniques to study the intra-daily volatility. In particular, the volatility extraction is based on functional principal components and the volatility prediction on functional AR(1) models. The estimation of the corresponding parameters is carried out...

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...

We review several procedures for estimating and backtesting two of the most important measures of risk, the Value at Risk (VaR) and the Expected Shortfall (ES). The alternative estimators differ in the way the specify and estimate the conditional mean and variance and the conditional...

In this paper, we partially review probabilistic and time series models in finance. Both discrete and continuous .time models are described. The characterization of the No- Arbitrage paradigm is extensively studied in several financial market contexts. As the probabilistic models become more and...

This paper reviews the literature on GARCH-type models proposed to represent the dynamic evolution of conditional variances. Effects of level outliers on the diagnostic and estimation of GARCH models are also studied. Both outliers and conditional heteroscedasticity can generate time series with...

Traditional tests for conditional heteroscedasticity are based on testing for significant autocorrelations of squared or absolute observations. In the context of high frequency time series of financial returns, these autocorrelations are often positive and very persistent, although their...

The empirical application of Stochastic Volatility (SV) models has been limited due to the difficulties involved in the evaluation of the likelihood function. However, recently there has been fundamental progress in this area due to the proposal of several new estimation methods that try to...

En este trabajo se hace una revisión de los modelos de series temporales con memoria larga para la media y la varianza condicionada, con especial atención a los modelos ARMA fraccionalmente integrados (ARFIMA) y a los modelos GARCH y SV fraccionalmente integrados. Se estudian sus propiedades...