We apply copula functions to evaluate counterpart risk in swap transactions. Using copulas allows to generalise the approach proposed by Sorensen and Bollier (1994), allowing for dependence between swap rates and counterparty default.. Counterpart risk is represented by a sequence of vulnerable swaptions, which are priced using Cherubini and Luciano (2002) approach. Using copulas grants maximum...

The copula function is a multivariate distribution whose marginal distributions are uniformly distributed on the interval [0,1], this function called copula that ties the joint and the margins together. One important class of copula models is that of semiparametric copula models. In this paper, a semiparametric copula and its properties are introduced also a test of symmetry for semiparametric ...

In this paper I identify a condition on the finite dimensional copulas of a univariate time series that ensures the series is weakly dependent in the sense of Doukhan and Louhichi (1999). This condition relates to the Kolmogorov-Smirnov distance between the joint copula of a group of variables in the past and a group of variables in the future, and the copula that would obtain if the past and f...

In designing coastal and nearshore structures, the joint probability of wave heights storm surges is essential in determining possible highest total water level. The key elements to accurately estimate are appropriate sampling extreme values selection functions for analysis. This study provide a full assessment performance different methods employed bivariate height surge samples analysed using...

Goodness-of-Fit Testing for Copula-based models with Applications in Atmospheric Science by Albert Rapp The University of Wisconsin–Milwaukee, 2017 Under the Supervision of Professor Vincent E. Larson Every elementary probability course discusses how to construct joint distribution functions of independent random variables but joint distribution functions of dependent random variables are usual...

A maximum entropy copula is the copula associated with the joint distribution, with prescribed marginal distributions on [0, 1], which maximizes the Tsallis–Havrda–Chavát entropy with q = 2. We find necessary and sufficient conditions for each maximum entropy copula to be a copula in the class introduced in Rodríguez-Lallena and Úbeda-Flores (2004), and we also show that each copula in that cla...

Multivariate GARCH (MGARCH) models are usually estimated under multivariate normality. In this paper, for non-elliptically distributed financial returns, we propose copula-based multivariate GARCH (C-MGARCH) model with uncorrelated dependent errors, which are generated through a linear combination of dependent random variables. The dependence structure is controlled by a copula function. Our ne...

The catastrophic nature of seismic risk is attributed to spatiotemporal correlation of seismic losses of buildings and infrastructure. For seismic risk management, such correlated seismic effects must be adequately taken into account, since they affect the probability distribution of aggregate seismic losses of spatially distributed structures significantly, and its upper tail behavior can be o...