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: Economic Scenario Generator - Investment Section Web Series Session #1 - Fundamentals & Hot Topics This session will cover the following points: 1. Introduction: ESG Basics, What is Risk Neutral and Real World? 2. Interest Rate Modeling 3. Equity and Private Equity Modeling 4. Example use cases facing investment professionals and actuaries today

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 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 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: This research report summarizes methodologies used for yield curve extrapolation to value liability cash flows that extend beyond the maximum observavble portion of the yield curve.

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.