GARCH models have been extensively used in risk modeling under the normal distribution. Although they generate highly significant coefficient estimates, these models are known to have poor forecasting power. It is therefore interesting to develop a different approach of risk modeling to improve...

In risk management it is desirable to grasp the essential statistical features of a time series representing a risk factor. This tutorial aims to introduce a number of different stochastic processes that can help in grasping the essential features of risk factors describing different asset...

To verify whether an empirical distribution has a specific theoretical distribution, several tests have been used, for example: Kolmogorov-Smirnov and Kuiper. These tests try to analyze if all parts of the empirical distribution has a specific theoretical shape. But, in a Risk Management...

This paper documents nonlinear cross-sectional dependence in the term structure of U.S. Treasury yields and points out risk management implications. The analysis is based on a Kalman filter estimation of a two-factor affine model which specifies the yield curve dynamics. We then apply a broad...

A common statistical problem in finance is measuring the goodness-of-fit of a given distribution to real world data. This can be done using distances to measure how close an empirical distribution is from a theoretical distribution. The tails of the distribution should receive special importance...

We analyze a sample of dual and single class IPOs to investigate whether empirical estimates of underpricing determinants are consistent across alternative measures of firm size and alternative techniques intended to account for underwriter price stabilization efforts. We find that results from...

We place the asset value credit portfolio model in the larger context of generalized correlation models where the normal distribution assumption of asset returns is replaced by an abstract elliptical distribution. Based on closed-form solutions for homogenous portfolios, we show in particular...

In this paper a new multivariate volatility model is proposed. It combines the appealing properties of the stable Paretian distribution to model the heavy tails with the GARCH model to capture the volatility clustering. We assume that multivariate asset-returns of financial stocks follow a...

In this article, we apply the multivariate Generalized Hyperbolic (mGH) distribution to portfolio modelling, using Conditional Value at Risk as a risk measure. Exploiting the fact that portfolios whose constituents follow an mGH distribution are univariate GH distributed, we prove some results...

The goal of this paper is to estimate time-varying covariance matrices. Since the covariance matrix of financial returns is known to change through time and is an essential ingredient in risk measurement, portfolio selection, and tests of asset pricing models, this is a very important problem in...