We provide a new approach to understanding systemic risk by analysing complex linkages in finance and insurance sectors. The analysis is achieved by using a recently proposed method for quantifying causal coupling strength, which identifies the existence of causal dependencies between two...

This study defines a multivariate Self--Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The conditions for stationarity of the nonlinear MSETARX models is provided. In particular, the efficiency of an adaptive...

The recent financial crisis has lead to a need for regulators and policy makers to understand and track systemic linkages. We provide a new approach to understanding systemic risk tomography in finance and insurance sectors. The analysis is achieved by using a recently proposed method on...

The turmoil in the sovereign debt markets in Europe has raised concerns on the usefulness of sovereign credit default swaps and government bond yields in periods of distress. In addressing this issue, we introduce a novel nonlinear approach for the analysis of non-stationary multivariate data...

Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency....

Due to the hyper technology associated to Big Data, data availability and computing power, most banks or lending financial institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision making and...