conditional volatility

this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener- alized

-frequency models, we find that our simple Gaussian VAR forecasts generally produce superior forecasts. Furthermore, we show that, given the theoretically motivated and empirically plausible assumption of normally distributed returns conditional on the realized volatilities, the resulting lognormal-normal mixture

The asymptotic distribution of maximum likelihood estimators is derived for a class of exponential generalized autoregressive condi-tional heteroskedasticity (EGARCH) models. The result carries over to models for duration and realised volatility that use an exponen-tial link function. A key feature

The relation between information flow and asset prices behavior is one of the key issues of modern finance. Our study investigates more closely the link between frequency of information arrivals and stock return volatility. It aims precisely to test empirically the mixture of distribution

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(will be inserted by the editor) Selection criteria in regime switching conditional volatility models Thomas CHUFFART* I thank Anne Peguin-Feissolle and Emmanuel Flachaire for many valuable comments. I am responsible for all errors and mistakes.

It is well known that one of the obstacles to e ective forecasting of exchange rates is heteroscedasticity (input dependent conditional variance). The autoregressive conditional heteroscedastic (ARCH) model and its variants have been used to estimate a time dependent

The three most popular univariate conditional volatility models are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the exponential GARCH (or EGARCH