We construct a Generalized Empirical Likelihood estimator for a GARCH(1,1) model with a possibly heavy tailed error. The estimator imbeds tail-trimmed estimating equations allowing for over-identifying conditions, asymptotic normality, efficiency and empirical likelihood based confidence regions...

We develop methods of non-parametric estimation for the Expected Shortfall of possibly heavy tailed asset returns that leads to asymptotically standard inference. We use a tail-trimming indicator to dampen extremes negligibly, ensuring standard Gaussian inference, and a higher rate of...

Two time series of financial losses may be observed in different overlapping windows, serially dependent, heteroscedastic, and cross-sectionally dependent. Fitting a regression model to each of the two time series, we construct an improved least squares estimator in one series exploiting the...