Description: Introduces the concept of total risk analysis, a methodology used to answer questions like "what are the chances we will lose more than $1 million"

Description: Analyze the market volatility behavior in context with historical extreme events during this webcast. There will be a focus on understanding volatility clustering and relationships, both contemporary and temporal, in addition to key economic and capital market variables. You'll also learn from a comprehensive attribution analysis, which uses economic data, asset data, retail investor data and event data as explanatory variables. Dive into investment-related Reddit comments used to capture features of retail investors and tree-based models, including Random Forests and Gradient Boosting Machines to justify changes in volatility modeling. Expect this webcast to end with an example of liability-driven investment optimization that illustrates the potential impact of increased volatility assumption, how to adopt stochastic volatility models, how to reflect volatility clustering and return jumps.

Description: Come dive into the Introduction to Modeling Summer Boot Camp series, which will provide an overview of modeling topics and techniques aimed at the beginner modeler. This interactive Zoom series will be comprised of five 90-minute sessions (August 24, 25, 30, 31 and September 1), with opportunities to discuss the topics in small break-out groups.

Description: The Student Research Case Study Challenge provides an opportunity for teams of students to apply their actuarial skills on a real-world problem in a business setting.

Description: This is an example of forecasting causes of death-Neoplasms mortality for the project Modeling and Forecasting Cause of Death Mortality by Socioeconomic Factors.

Description: This paper investigates the implications of using the Basel II motivated Advanced Measurement Approaches as a method to assess operational risk capital charges for banks and insurance companies with regard to U.S. regulations. The AMA developed in the paper uses actuarial loss models complemented by the extreme value theory to determine the empirical probability distribution function of the aggregated capital charges in the context of various classes of copulas. From Actuarial Research Clearing House, 2006.

Description: This paper develops a formal framework to model the behavior of individual and small group health insurance policies in a competitive market. In this framework, anti-selective lapse is not assumed, but arises as a result of rational consumers seeking the best value in a competitive market where the premium rates are restricted by law. The authors obtain a model that addresses a number of fundamental questions which have not been fully answered previously, including the effect of price-induced anti-selective lapse on the mix of insureds in a block and the optimal renewal price to maximize the aggregate profit of a block.

Description: A statistical estimate is obtained for the total number of nodes in a three level tree. The number of nodes at each level is randomly distributed with certain pattern of distribution. Then the result is applied to the problem of estimating the LRA [logical record access] in data base design in a more refined way. It is more refined in the sense of using more information contained in the empirical distributions and being associated with a probability statement.

Description: This paper evaluates the current practice of operational risk management in the insurance sector. Operational risk is nothing new in insurance, but, because of regulatory requirements, companies have initiated computation of risk capital for their operational losses. The current effort to manage operational risk is not a naturally evolving phenomenon, and operational risk, in the Basel Committee on Banking Supervision’s Basel II definition, is unlikely to be a significant cause of insurers’ failure. In addition, the current Basel II definition of operational risk is not suitable for the insurance sector. Consequently, the invention of models and tools based on the definition is incomplete and illusionary. My findings are based on the analysis of dozens of interviews with insurance industry professionals. I demonstrate the way operational risk is quantified in practice, show that the result obtained from this computation is of littl e use in managerial decision making and propose a set of policy recommendations illustrating the characteristics of operational risk in insurance. This study can be used as a platform for launching dialogues to initiate fresh thinking about operational risk in insurance beyond the current artificial and narrow boundaries.

Description: This article lays conceptual foundations associated with projecting investment-related discretionary elements of various life insurance and annuity products. It outlines potential implementation approaches that may be appropriate when calculations are performed in a risk-neutral setting. The article concludes that crediting rates in risk-neutral models can be projected as in real-world models, except that the modeled provision for expected credit losses should be modified to equal real-world expected losses initially, but over the projection reflect management’s evolving expectation, moving ultimately to risk neutral expected losses.