Description: This article addresses issues of importance to HMO excess reinsurance purchasers when negotiating a reinsurance treaty.

Description: The influenza pandemic of 1918-20 is recognized as having generally taken place in three waves, starting in the northern spring and summer of 1918. This pattern of three waves, however, was not universal: in some locations influenza seems to have persisted into or returned in 1920. The recorded statistics of influenza morbidity and mortality are likely to be a significant understatement. Limitations of these data can include nonregistration, missing records, misdiagnosis, and nonmedical certification, and may also vary greatly between locations. Further research has seen the consistent upward revision of the estimated global mortality of the pandemic, which a 1920s calculation put in the vicinity of 21.5 million. A 1991 paper revised the mortality as being in the range 24.7-39.3 million. This paper suggests that it was of the order of 50 million. However, it must be acknowledged that even this vast figure may be substantially lower than the real toll, perhaps as much as 100 percent understated.

Description: We consider Libor market model and calibration process. We estimate some interest rate derivatives such as Caps and Swaptions. We use Monte Carlo methods with appropriate dynamics under spot measure and forward measure. Imposing martingale discretization property, the same quantities are estimated again to see a contrast or discrepancy in light of confidence interval. Based on algorithms discussed in the paper, “Arbitrage-free discretizations of lognormal forward Libor and swap rate models” by Glasserman, P and Zhao, X 2000, the algorithms are implemented in excel VBA. Starting with current market data such as FRAs and Bond Prices, we describe steps involved to estimate the price of interest rate derivatives such as Caps and Swaptions, which would be beneficial to the practitioners in the industry.

Description: Excel file from Health Pricing Boot Camp Session 1: Basic Trend Forecasting Model Case Study

Description: The purpose of this discussion is to introduce a few additional points and discuss how different assumptions will alter the results in some cases.

Description: One-page overview of monograph contents. From Actuarial Considerations in Insurance Mergers and Acquisitions, M-FI03-1.

Description: Role of the Actuary: One of the most complicated tasks an actuary can perform is large-scale corporate modeling in an attempt to shed light on the relationship between value, performance and risk. In specialized situations such as mergers and acquisitions, it may be appropriate to have external actuaries perform the work rather than attempt the task with internal resources. From Actuarial Considerations in Insurance Mergers and Acquisitions, M-FI03-1.

Description: Bibliography. From Actuarial Considerations in Insurance Mergers and Acquisitions, M-FI03-01.

Description: This paper develops a model of annuity mortality that facilitates a more thorough evaluation of the issues of policyholder antiselection and insurance company response. Aging occurs as an annuitant's health state changes. Death occurs either as terminal health state or by accident. By knowing the current health state, the annuitant or insurance company can improve its knowledge about life expectancy.

Description: Today, a plethora of tools and approaches exist to develop lifetime projections of long-term care (LTC) business. Within these tools lie two distinct approaches to project mortality; namely, by using assumptions that are applied: (1) to an all-lives exposure base, or (2) separately for disabled- versus active-lives exposure bases. Using disabled versus active mortality allows for more granular modeling of the two different cohorts that exhibit dramatically different mortality. And so, when using an all-lives mortality assumption, is the projection missing important details about the appropriate mortality level? This article seeks to not only answer this question, but also answer how a “traditional” all-lives mortality assumption can be impacted when you introduce a combination of the two modeling techniques into the development of a mortality assumption. To answer these questions, we examined the experience and results developed from one c ompany as an illustrative case study.