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This article provides an innovative method for measuring the dependence between pairs of poverty dimensions using a semiparametric copula approach that permits us to account for the importance of extreme low values. The association between pairs of poverty dimensions at the lower tail is easily measured using the parameter estimates of the specified parametric copula, and no further calculations are needed. This approach is used to measure the bivariate lower tail dependence between the dimensions of the AROPE rate in Europe at two time points (2009 and 2018). The findings reveal a statistically significant increase in the lower tail dependence between 2009 and 2018 in several European countries. [ABSTRACT FROM AUTHOR]

In financial research and among risk management practitioners the estimation of a correct measure of the Value-at-Risk still proves interesting. A current approach, the multivariate CAViaR allows to provide an accurate measure of VaR modelling the joint dynamics in the Values-at-Risk by capturing the quantile conditional dependence structure to take into account financial contagion risk. The parameter estimates are based on multiple quantile regressions which assume linear combinations of sample quantiles. In this paper we argue that the analysis of multiple time-series aimed to model the time-varying quantile dependence can require non-linear and flexible estimation procedures. To this end, we examine the conditional quantile behaviour of some assets included in the Eurostoxx50 with respect to the quantile of a portfolio representing the market with a new copula-based quantile Vector AutoRegressive approach, and compare the results with the bivariate CAViaR model. Findings show that the copula approach is highly competitive, providing a time-varying model aimed to give a better specification of the Value-at-Risk, especially in terms of loss functions. [ABSTRACT FROM AUTHOR]

The complex nature of financial markets is the object of many studies. In general, it is crucial to comprehend how the dependence structure changes based on the values of some covariates because one or more covariates can considerably impact the dependence structure between two variables. Here, we propose a new method to investigate the dependence between assets in financial markets. The approach combines Kendall's τ and tail dependence coefficients to account for non-linear relationships and associations between extreme values of the assets. We illustrate the proposed method by analyzing the dependence structure between 44 stocks in the EUROSTOXX50 index, tracking the performance of the 50 largest and most liquid companies in the Eurozone. • Explore the dependence between two financial assets conditionally on the market. • Considering a sample of 46 assets, identify the relative influence. • Identify the most influential sector for a company based on different perspectives. [ABSTRACT FROM AUTHOR]

The description of the dynamic behavior of multiple time series represents an important point of departure to obtain accurate forecasts both in economic and financial analysis. We provide a method for the comparison of the out-of-sample performance of portfolios, respectively, ignoring and exploiting serial and cross dependence in stock returns. The serial and cross dependence is modeled using both the classical linear and easy-to-use Vector AutoRegressive and more sophisticated models making use of copula functions. After deriving the classical and copula-based VAR conditional expected returns and covariance, we construct different portfolios and compare them in terms of Sharpe ratio in an out-of-sample period. [ABSTRACT FROM AUTHOR]