Description: This article reviews the sixth survey of Emerging Risks, sponsored by the Joint Risk Management Section and completed in fall 2012.

Description: The U.K. Pension Protection Fund (PPF) was established in April 2005 to protect the pensions of members of U.K. private sector defined benefit pension schemes that have insufficient assets and whose corporate sponsor fails. The fund takes over the pension scheme assets and assumes responsibility for the payment of compensation to the former members of the scheme. PPF is funded by a levy on the population of eligible schemes. The elements of the enterprise risk management of the fund have been developed by reference to practice within proprietary insurance institutions and other pensions funds; their application to this unique financial vehicle is the subject of this paper. The paper draws on references to relevant risk and actuarial work. It is designed to illustrate the application of principles and techniques to a real world example.

Description: Global financial crises like the one recently experienced, affected both large and small institutions.Today, when there is heightened need for enhanced risk management tools, some entities are unable to employ sophisticated mechanisms due to limited data availability. Moreover, from the Basel Committee on Banking Supervision’s point of view via Basel II and Basel III, the internal ratings- based (IRB) approach requires that institutions have some reliable estimates of default probabilities of default for each rating grade. Taking the work of previous researches a step further, this paper intends to propose a new dynamic mechanism to for the risk management industry for to calculateing probabilities of default (PD). Through these estimates for the core model, we generate implied probability of default (PD) through actuarial estimation tools and different probability distributions. This mechanism is specialized to work best for low- defa ult portfolios (LDPs). Furthermore, scenario testing is adopted to validate the model against any model- specific bias

Description: The article provides an update on the 2019 ERM Symposium with regard to date, location and content (General Sessions and Concurrent Sessions).

Description: A critical step in valuing a company for the purpose of financial reporting is the computation of the market value of economic surplus (MVES). This article provides a novel approach to the valuation of MVES that is stable and reasonably immune to “market noise.” Current approaches to the calculation of economic surplus of an insurance company generally define such surplus as the market value of the assets (MVA) supporting the liabilities less the market value of liabilities (MVL). While MVA is observable in the market, MVL is typically computed directly, without regard to the underlying supporting assets. Subtracting MVL from any value of assets that changes with market movements results in an unstable surplus. Consequently, a surplus that is not fully reflective of market sentiment has been rejected by many. This same approach is further used in the calculation of market consistent embedded value (MCEV) when a balance sheet approach is used .

Description: This article reviews the dynamics of surrender risk, briefly discusses current valuations in the Interest Rate option markets and presents some tailored structured hedging instruments that can be used to manage this risk.

Description: Read the March 2017 issue of Risk Management published by the Joint Risk Management Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Read the French language version of the March 2017 issue of Risk Management published by the Joint Risk Management Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Findings from EY Insurance Risk Management Team's 2016 (annual) Insurance Chief Risk Officer Survey.

Description: We analyze how volatility management techniques in VAs affected policyholders and insurance companies