This article summarises the advice and suggestions put forward by EIOPA with regards to changes for the premium risk capital requirement calculation and in particular with regards to the definition of the volume measure, which has always been a controversial aspect of the Standard Formula and could impact the capital requirements of non-life companies.

Since 2008, catastrophic losses and financial turmoil have deeply shaken the insurance and reinsurance industries. Severe difficulties encountered by sector leaders like AIG and Swiss Re have shed light on the potential fragility of the players, and have increased attention on the subject of reinsurance counterparty risk. This corresponds to the exposure of an insurance company to reinsurer failure and is difficult to assess due to a scarcity of reliable measures. It has long been considered as largely auto-regulated by the insurance market.

The aim of this paper is to empirically study reputation risk management in the US and European banking and insurance industry, which has become increasingly important in recent years. We first use a text mining approach and find that the awareness of reputation risk (management) as reflected in annual reports has increased during the last ten years and that it has gained in importance relative to other risks. Furthermore, we provide the first empirical study of the determinants and value of reputation risk management.

Risk is an inherent property of every project. In many cases, project management and risk management are applied quite independently. The traditional tools of project management do not include the notion of risk and the tools of risk management focus on the representation of risks without explicitly representing the project, which leads to implement the risk management process independently of the project management process.

The objectives of this research were to explore risk management practices influencing the success of IT projects. Data were collected from 200 project managers, IT managers, and IT analysts in the IT firms through questionnaires and analysed using the Independent Sample t-test, One-way ANOVA, and Multiple Linear Regression at the statistical significance level of 0.05.

Projects tend to exceed planned timelines and budgets. One reason may be that potential project risks are insufficiently reflected in anticipations of project success. Furthermore, project managers’ overconfidence may lead them to assess risk in a biased manner. The present study examines how risk assessment relates to overall anticipated project success and how overconfidence on the part of project managers influences such assessments. We assume that project managers’ risk awareness serves as a mediator between overconfidence and risk assessment.

The purpose of this research was to study the recognition, application and quantification of the risks associated in managing projects. In this research, the management of risks in an oil and gas project is studied and implemented within a case company in Oman. In this study, at first, the qualitative data related to risks in the project were identified through field visits and extensive interviews. These data were then translated into numerical values based on the expert’s opinion. Further, the numerical data were used as an input to Monte Carlo simulation.

This paper gives an alternative to the capital asset pricing model beta based on the entropy concept of statistical mechanics. This measure is shown to have a higher explanatory power than that used in the typical capital asset pricing model

This paper proposes a new generative uncertainty mechanism based on the Bayesian Inference and Correntropy (BIC) technique for accurately evaluating asset pricing in markets. The key findings reveal that the precise modeling of asset information can estimate price changes in the market effectively.

A novel approach is proposed to measure market risk based on the mathematical technique of wavelets. Noteworthy heterogeneity across frequencies and time are found, highlighting the usefulness of the wavelet approach