Description: In this article, the author explains what a CAT model is.

Description: European insurance regulation pretends to capture and rely on the 1-year 1-in-200 VaR. But there cannot exist any scientific calibration of such measure. Why?

Description: The ORSA should be more than a compliance requirement -- its process should be an integral element of a company's governance and risk and strategic planning process used by both management and the board on a regular feedback loop basis. Similar to that used for ERM, it along with a carefully constructed set of metrics should serve as the springboard to be used to identify, control and mitigate short- and long-term risks, as well as to enable exploitation of opportunities to add shareholder and policyholder value. Risk can represent an opportunity from which insurers can leverage their knowledge and risk management capabilities to find and maintain a competitive edge. Part of the essay collection "Risk Metrics for Decision Making and ORSA"

Description: This paper presents actuarial (mathematical) models for cyber breach probability as function of the level and allocations of security spending by a firm.

Description: The editors provide highlights of the articles included in the April 2016 issue of Risk Management.

Description: Presented at August 2011 46th Actuarial Research Conference. This paper focuses on mainly focus on the case in which the risk variable follows a distribution function from a max-domain of attraction.

Description: People naturally observe risk in the form of the range of experienced gains and losses which is usually represented in statistical terms by standard deviation. In most cases, there are not enough observed values to reliably discern the true amount of losses that are possible in extreme situations. Statistical techniques that have long been applied to insurance company risks are being used to extrapolate from observed risk to extreme tails of the distribution of gains and losses. These processes are essentially an extrapolation from the “known” risk of volatility near the mean to “unknown” risk of extreme losses. “Fat tails” is a term often applied to risks where the likely losses in rare situations significantly exceeds the predictions of a Gaussian (“thin tailed”) distribution. This paper will look at the relationship between the observed risk and the extreme risk in statistical insurance r isk models and discuss how we might create a discussion among model users about the appropriateness of that relationship for specific types of risks.

Description: From a session at the Spring meeting of the Society of Actuaries held in San Antonio, Texas, June 14-15, 2004 Speakers discuss the approaches to quantifying investment risk, including the credit risk subcomponent and mismatch risk sub-component in the three different types of risk-based capital RBC framework commonly used, such as regulatory, rating agency and company-specific economic-based formulas.

Description: Highlight of the ERM Symposium

Description: This article highlights some of OSFI’s expectations regarding the ORSA process, and provides general feedback and observations from OSFI’s reading of 2014 ORSA reports (including KMR) and discussions with insurers.