Description: This article introduces the JRMS library, how one can access it and what books are added this year.

Description: From a simple mathematical model of the underlying economics, I first predicted this crisis in July of 2004. An economic dynamic relating very low interest rates to the structure of the demand curve in the housing market made this outcome foreseeable, indeed inevitable. The current crisis had a mathematical cause. Essay on the collapse of the housing bubble.

Description: In this article, the editor of the newsletter addresses the readership about changes to the newsletter.

Description: SOA sponsored research tackled potential low-interest-rate scenarios. This article is based on findings in the report but does not mirror the report with regard to content as it looks at the interest rate impact on assets, liabilities and their interactions. Potential drivers of low rates are identified, along with risk management recommendations for insurers and regulators. These drivers include demographics, behavioral responses to the economy, environmental costs and changes to the economic landscape.

Description: The article explores options for defined-benefit retirement plan sponsors to transfer pension risk, focusing on recent trends in the United Kingdom.

Description: This article discusses the impact of increasing activity with respect to Solvency II in the United States.

Description: The Joint Risk Management Section conducted its fourth survey of emerging risks in fall 2010 in light of the BP oil spill which brought the impact of various risk combinations to the fore.

Description: Review and Preview of this year's Continuing Ed opportunities via the webinar medium for risk practitioners

Description: The IFRS Foundation’s establishment of the International Sustainability Standards Board is a major step toward achieving relevant, decision-useful sustainability information for market participants that is comparable across industries and jurisdictions.

Description: Financial institutions have and continue to suffer substantial financial losses because of their exposures to various operational risks and insufficient risk management. Quantifying operational risk is a critical component of an institution’s ability to manage risk. The model described in the article provides a numerical measure for operational risks existing in a business entity through statistical methods. It takes experience data to reflect both the general features and company specifics of the defined operational risks, while also allowing for managerial discretion. An important achievement is to suggest a level of capital to hold against potential losses arising from operational risks.