Density forecasts contain a complete description of the uncertainty associated with a point forecast and are therefore important measures of financial risk. This paper aims to examine if the new more complicated models for financial returns that allow for time variation in higher moments lead to...

We introduce a new fractionally integrated model for covariance matrix dynamics based on the long-memory behavior of daily realized covariance matrix kernels and daily return observations. We account for fat tails in both types of data by appropriate distributional assumptions. The covariance...

In this article, we have tested the volatility of the monthly returns of an equity hedge fund for changing conditional variances by using a log likelihood model. Generalized autoregressive conditional heteroskedastic models, (GARCH) with t-distributed errors, and exponential generalized...

In this paper, we provide a stable limit theorem for the asymptotic distribution of the sample average value-at-risk when the distribution of the underlying random variable X describing portfolio returns is heavy-tailed. We illustrate the convergence rate in the limit theorem assuming that X has...

We provide a new alternative to thin-tailed regression models by introducing robust numerical methods, based on the minimum relative U−entropy (MRUE) principle, to estimate heteroskedastic, fat-tailed, flexible univariate probability density functions, conditioned on a number of explanatory...