This paper develops a multivariate statistical model for the analysis of credit default swap spreads. Given the large excess kurtosis of the univariate marginal distributions, it is proposed to model them by means of a mixture of distributions. However, the multivariate extension of this...

Approximate Maximum Likelihood Estimation (AMLE) is a simple and general method recently proposed for approximating MLEs without evaluating the likelihood function. The only requirement is the ability to simulate the model to be estimated. Thus, the method is quite appealing for spatial models...

<title>Abstract</title> Stationarity in space presents two aspects: homogeneity and isotropy. They correspond respectively to stationarity under translations and stationarity under rotations. Testing the hypothesis of isotropy is a common practice in many fields of application of spatial statistics where...

In this paper, we propose a generalization of importance sampling, called Adaptive Importance Sampling, to approximate simulation of copula-based distributions. Unlike existing methods for copula simulation that have appeared in the literature, this algorithm is broad enough to be used for any...

We question the claim that the largest US cities are Pareto distributed. We show that results of multiple tests on real data are similar to those obtained when the true distribution is lognormal, and largely depend on sample sizes.

Continuous-time stochastic volatility models are becoming increasingly popular in finance because of their flexibility in accommodating most stylized facts of financial time series. However, their estimation is difficult because the likelihood function does not have a closed-form expression. A...