The study of wage dynamics over time is more insightful than the analysis of point-in-time inequality measures. In policy-making, indeed, low wage careers instead of low-wage jobs should be at the center of the debate on long-term wage inequality. Many previous studies tried to estimate the...

In this paper, we consider non-stationary response variables and covariates, where the marginal distributions and the associated copula may be time-dependent. We propose estimators for the unknown parameters and we establish the limiting distribution of the estimators of the copula and the...

In the increasingly connected world, many systems are more or less coupled with each other in various ways. A typical example is the cross-market portfolio management, where the products of heterogeneous markets are selected and configured for investment. In such cross-market problems, one...

This paper describes the modelling of spread risk, in case of missing or illiquid market data, by using a subset of good quality liquid bond/credit default swap (CDS) spread time series. The proposed method links copula simulation to the actual historical spread dynamics. This is important when...

We propose a tail dependence based network approach to study systemic risk in a network of systemically important financial institutions (SIFIs). We utilize a flexible factor copula-based method which allows us to measure the level of extreme risk in a portfolio when dependence is driven by one...

In this paper, we address risk aggregation and capital allocation problems in the presence of dependence between risks. The dependence structure is defined by a mixed Bernstein copula which represents a generalization of the well-known Archimedean copulas. Using this new copula, the probability...

A direct utility approach can handle multiple discrete/continuous choice outcomes. However, there is a trade-off between allowing correlations between unobserved perceived qualities of two alternatives and computational burden. If we allow the correlations, then we have to do numerical...

Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm...