Copulas have proven to be very successful tools for the flexible modeling of cross-sectional
dependence. In this paper we express the dependence structure of continuous-valued time …

Almost all existing nonlinear multivariate time series models remain linear, conditional on a
point in time or latent regime. Here, an alternative is proposed, where nonlinear serial and …

In the current paper, we investigate the problem of how do crude oil futures hedge crude oil
spot risk after the COVID-19 outbreak. Specifically, given that noise, conditional higher …

The study explores whether China's national carbon market can hedge the risks of light and
medium crude oil compared with the European carbon market, through the diversified and …

Copula function is a class of functions that use marginal probability density function to obtain
joint distribution. It is used to model multivariate joint distribution and is suitable to apply in …

In this paper, we present an alternative method for the spectral analysis of a univariate,
strictly stationary time series {Y_t\}_t∈Z. We define a “new” spectrum as the Fourier …

Most existing reduced-form macroeconomic multivariate time series models employ elliptical
disturbances, so that the forecast densities produced are symmetric. In this article, we use a …

Copula modeling for serial dependence has been extensively discussed in a time series
context. However, fitting copula-based Markov models for serially dependent survival data is …

Vine copulae provide a graphical framework in which multiple bivariate copulae may be
combined in a consistent fashion to yield a more complex multivariate copula. In this article …

We propose parametric copulas that capture serial dependence in stationary
heteroskedastic time series. We suggest copulas for first‐order Markov series, and then …