Description: Risk Management and Actuaries—SOA Risk Management Task Force Update

Description: A central problem for regulators and risk managers concerns the risk assessment of an aggregate portfolio defined as the sum of d individual dependent risks Xi. This problem is mainly a numerical issue once the joint distribution of (X1,X2, . . . ,Xd) is fully specified. Unfortunately, while the marginal distributions of the risks Xi are often known, their interaction (dependence) is usually either unknown or only partially known, implying that any computed risk measure of the portfolio is subject to model uncertainty. Previous academic research has focused on the maximum and minimum possible values of a given risk measure of the portfolio, in the case in which only the marginal distributions are known. This approach leads to wide bounds, as all information on the dependence is ignored. In this paper, we integrate in a natural way available information on the multivariate dependence and provide easy-to-compute bounds for the risk measure at hand. We observe that incorporating the information of a well-fitted multivariate model may, or may not, lead to much tighter bounds, a feature that also depends on the risk measure used. We illustrate this point by showing that the Value-at-Risk at a very high confidence level (as used in Basel II) is typically prone to very high model risk, even if one knows the multivariate distribution almost completely. Our results make it possible to determine which risk measures can benefit from adding dependence information (i.e., leading to narrower bounds when used to assess portfolio risk), and, hence, to identify those models for which it would be meaningful to develop accurate multivariate models.

Description: Presentation from the Actuarial Research Conference describing risk management at a leading Canadian Bank.

Description: The author makes 10 predictions of how risk management will change over the next decade.

Description: The articles discusses how Principles-based approaches PBA to setting reserves and capital can be combined with Enterprise Risk Management to create a competitive advantage for an insurance company. Those companies can leverage these models by combining unique risks and strategies to measure and manage risk.

Description: Learn how modernization of data, models, and technology are influencing workflows. This webcast will address how workflows are changing in response to new regulatory requirements and the impacts the changes have on modeling and end-to-end future state operating models. Industry leading actuaries will discuss: How team collaboration can increase quality and speed of reporting to internal and external stakeholders How to strengthen controls and compliance frameworks across finance and actuarial departments How to modernize actuarial input/output for strategic analysis and insights How to attract and retain top talent by improving opportunities for actuaries There will also be dedicated time to ask questions of those who have made the transition.

Description: Equity-Based Insurance Guarantee Webcast Series

Description: Reinsurance can be used as a tool to improve profitability and competitiveness on in force and new business for companies. Emerging reinsurance solutions will help organizations better manage their capital in different ways. During this webcast, the following solutions will be covered: Use of reinsurance to manage and improve capital and profitability Turning spread-based business into fee-based business Strategic use of affiliate and sidecar reinsurance

Description: Panelists at this session of the SOA 1998 Maui I Spring Meeting presents their approach to an integrated risk management framework that assesses the level of risk in the organization and where it lies on the balance sheet.

Description: Regime switching models have become a popular tool in econometric time series modeling since their introduction in Hamilton [1989].These models have been widely applied for equity-returns for investment guarantees. A prominent risk management challenge is hedging these guarantees, given the equity regime switching process. This presentation develops some methodologies for regime-switching portfolio replication and use real world data to demonstrate and evaluate their effects. A Bayesian approach for regime switching model fitting is used, which allows for the examination of the robustness of each of the optimization methods when parameter uncertainty is present.