Description: From a session at the 2000 Valuation Actuary Symposium held in Washington DC, September 14-15, 2000 Panelists use recent transactions and examples to demonstrate various operational, financial reporting and strategic aspects of insurance company mergers.

Description: Presented at May 1996 Spring Meeting. This teaching session covers course F-585 in the exam materials, which is intended to provide background for the actuary who aspires to become the chief financial officer of a financial institution. It addresses raising capital and financial management.

Description: This is a compilation or risk management terms garnered from a survey of risk professionals supplemented with analysis of firm-level information on significant risks as reported in 10-K financial statements. Results indicate the existence of significance differences in terminology. The dominating pattern observed is a dichotomy between internal and external perceptions. Also observed is a difference between terms used by actuarial professionals and traditional risk managers.

Description: This Chairperson's Corner article discusses the SOA becoming the pre-eminent risk management profession. The Chairman's message is that actuaries have the inside track on risk management positions in the insurance industry and have a basis for competing for similar positions in the broader financial services industry.

Description: This session explores where financial engineering is applied, who is “leading the charge,” and what are the current and future roles of actuaries. From the Record of the Society of Actuaries, Volume 27, No. 2, Session 55PD of the Toronto Spring Meeting.

Description: This panel of session 3PD of the Washington Annual Meeting discusses a range of topics which fall under a new professional label of financial engineer, and how this role relates to the role of the actuary. From the Record of the Society of Actuaries, Vol. 23, No. 3.

Description: This session discussion is about deterioration in claims-paying ability, financial well-being of life insurance companies, benchmark surplus standards, key operation ratios, credit analysis of asset holdings, market view of stock companies, adequacy of capital in the industry, appropriate risk/reward posture in the industry, advice to management, outlook for the various business and views on surplus relief devices. From Record of the Society of Actuaries Vol. 15, No. 3A.

Description: In session 119 CS of the Washington Annual Meeting panelists discuss techniques for analyzing and developing investment strategies based on the mathematical approach of linear programming are presented. From the Record of the Society of Actuaries, Vol. 23, No. 3.

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.