Description: The letter from the editor discusses some recent loss events driven by model error and provides an overview of the articles in this issue of Risk Management.

Description: This paper expands upon current capital analysis by introducing a practical approach that considers the objectives of all stakeholders of an insurance company in setting appropriate capital targets. In addition, the paper introduces a technique for estimating capital adequacy that is based on mapping stochastic distributions of regulatory and rating agency capital measures to observable financial rating transition matrices.

Description: The paper o ers a simple framework for ranking the common reinsurance struc- tures in practice with the theory of stochastic orders. The basic idea is to slice the space of reinsurance structures into groups by expected loss cost to facilitate the comparisons within the group and between groups. Given the standard risk aversion assumption in economics, a spectrum of reinsurance structures with the same expected loss cost can be compared analytically with one another and sequenced based on their risk coverages under the convex order. The paper then expands the dimension of the comparison to groups of reinsurance structures with di erent expected loss costs, which can be ranked under the increasing convex order and the usual stochastic order. As such, the paper maps out the ordering for the entire space of reinsurance structures and presents it in a ma- trix format for quick reference. The implication

Description: A presentation at the Actuarial Research Conference in August 2006 in Montreal. This paper discusses the Basel VaR Value at Risk test of a bank's VaR model. It proposes an alternative, Quality Control of Risk Measures QCRM Hypothesis test. The alternative is more likely to reject an incorrect VaR model.

Description: The editorial cautions that while the development of more sophisticated models may increase efficiency, it is important to consider the very real risk management costs associated with the increased complexity, efficiency and overconfidence in the predictive power of models.

Description: This brief article details my observations as a senior consultant for a leading actuarial consultancy across the Middle East and South Asia regarding ORSA. It highlights key challenges, potential solutions and describes the dichotomy of management and consultant paradigm.

Description: Metadata for entire issue

Description: The paper o ers a simple framework for ranking the common reinsurance struc- tures in practice with the theory of stochastic orders. The basic idea is to slice the space of reinsurance structures into groups by expected loss cost to facilitate the comparisons within the group and between groups. Given the standard risk aversion assumption in economics, a spectrum of reinsurance structures with the same expected loss cost can be compared analytically with one another and sequenced based on their risk coverages under the convex order. The paper then expands the dimension of the comparison to groups of reinsurance structures with di erent expected loss costs, which can be ranked under the increasing convex order and the usual stochastic order. As such, the paper maps out the ordering for the entire space of reinsurance structures and presents it in a ma- trix format for quick reference. The implication of this stochastic ordering to reinsurance pricing is also investigated.

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"