Measuring dependence in a multivariate time series is tantamount to modelling its dynamic structure in space and time. In the context of a multivariate normally distributed time series, the evolution of the covariance (or correlation) matrix over time describes this dynamic. A wide variety of...

This paper features an analysis of cryptocurrencies and the impact of the COVID-19 pandemic on their effectiveness as a portfolio diversification tool and explores the correlations between the continuously compounded returns on Bitcoin, Ethereum and the S&P500 Index using a variety of parametric...

Several studies on heritability in twins aim at understanding the different contribution of environmental and genetic factors to specific traits. Considering the National Merit Twin Study, our purpose is to correctly analyse the influence of the socioeconomic status on the relationship between...

In this paper, we study the kernel estimation of the copula density on unit square [0,1]X[0,1], and demonstrate the implementation of this methodology to equity and bond markets. There are two crucial problems associated with this estimator. First, the kernel estimator is biased at the...

This paper investigates the estimation of semiparametric copula models with data missing at random. The two-step maximum likelihood estimation of Genest, Ghoudi, and Rivest (1995) is infeasible if there are missing data. We propose a class of calibration estimators for the nonparametric marginal...