We propose a new variational Bayes method for estimating high-dimensional copulas with discrete, or discrete and continuous, margins. The method is based on a variational approximation to a tractable augmented posterior, and is substantially faster than previous likelihood-based approaches. We...

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...

We investigate how the local fluctuations of the signed traded volumes affect the dependence of demands between stocks. We analyze the empirical dependence of demands using copulas and show that they are well described by a bivariate K copula density function. We find that large local...

The inability of the Gaussian copula to describe the tail dependence among risks has been sharply criticized since the 2008 financial crisis. In this paper, we extend the classic Gaussian copula to the distorted GAB copula with the distorted mix method. Similar with the Gaussian copula, the...

Abstract In this paper we advance a nonlinear optimization problem for hedging wind power variability by using a dispatchable energy source (DES) like gas. The model considers several important aspects such as modeling of wind power production, electricity price, nonlinear penalization scheme...

Financial markets are interconnected and fragile making them vulnerable to systemic contagion, and measuring this risk is crucial for regulatory responsiveness. This study introduces a new set of measures for systemic risk using a copula‐based (CB) estimation method with a focus on U.S. Bank...