This article deals with methods for identifying as well as stressing risk concentrations in credit portfolios, in particular concentrations caused by large exposures to a single sector or to several highly correlated sectors. We present a general and yet computationally efficient framework for implementing stress scenarios in a multifactor credit portfolio model and illustrate the proposed methodology by stressing a large investment banking portfolio.

This presentation covers the technical detail of derivatives within a low volitality and spike environment, hedging market impact for life insurance liabilities and hedging solutions.

Given that the insurance market is characterized by asymmetric information, its efficiency has traditionally been based to a large extent on risk classification. In certain regulations, however, we can find restrictions on these differentiations, primarily the ban on those considered to be “discriminatory”. In 2011, following the European Union Directive 2004/113/EC, the European Court of Justice concluded that any gender-based discrimination was prohibited, meaning that gender equality in the European Union had to be ensured from 21 December 2012.

This article is about defining risks at the proper level. To do so one must distinguish between risks and the outcome of other risks. The so-called reputation risk is an example of something that is not a source of risk, but rather an outcome of other risks. In addition to defining risks at the source, is also important to categorize risks with consistent levels of granularity.

This article will discuss the use of results from popular Property Catastrophe models. It will explain common terms like Occurrence Exceedance Probability (OEP) and Aggregate Exceedance Probability (AEP) and show how these are related to event count and event size ideas. Simulation and the use of multiple models (blending) will also be discussed.

This article presents three different methods of the risk analysis as well as highlighting their disadvantages, advantages and primary areas of application (selection or pre-estimation). These methods differ in their methodology from each other. The areas of application and analytical capacity of the listed methods are illustrated with the short examples, simultaneously outlining their characteristics from the analysis. The research problems, which are the canvas of application of the discussed methods are not mutually interrelated.