For many applications, analyzing multiple response variables jointly is desirable because of their dependency, and valuable information about the distribution can be retrieved by estimating quantiles. In this paper, we propose a multi-task quantile regression method that exploits the potential...

This paper contributes to model the industry interconnecting structure in a network context. General predictive model (Rapach et al. 2016) is extended to quantile LASSO regression so as to incorporate tail risks in the construction of industry interdependency networks. Empirical results show a...

We analyse a sample of funds and other securities each assigned a total rating score by an unknown expert entity. The scores are based on a number of risk and complexity factors, each assigned a category (factor score) of Low, Medium, or High by the expert entity. A principal component analysis...

In the present paper we study the dynamics of penalization parameter ? of the least absolute shrinkage and selection operator (Lasso) method proposed by Tibshirani (1996) and extended into quantile regression context by Li and Zhu (2008). The dynamic behaviour of the parameter ? can be observed...

We propose a semiparametric measure to estimate systemic interconnectedness across financial institutions based on tail-driven spill-over effects in a ultra-high dimensional framework. Methodologically, we employ a variable selection technique in a time series setting in the context of a...

In this paper, we propose a multivariate quantile regression method which enables localized analysis on conditional quantiles and global comovement analysis on conditional ranges for high-dimensional data. The proposed method, hereafter referred to as FActorisable Sparse Tail Event Curves, or...