Description: "Living to 100 in the time of COVID-19: A Study of Late –Life Mortality Trajectories, Leonid A. Gavrilov, Ph.D., Natalia S. Gavrilova, Ph.D., Academic Research Centers, NORC at the University of Chicago Swedish Mortality Does Not Decelerate in Recent Cohorts, or Does it? – Roland Rau, Ph. D., Professor, University of Rostock & Max Planck Institute for Demographic Research, Rostock, Germany; Marcus Ebeling, Ph.D., Karin Modig, Ph.D., Associate Professor, Anders Ahlbom, Ph.D., Professor Emeritus, Karolinska Institute, Stockholm Sweden; Kai Kaufhold, Aktuar DAV, Ad Res Advanced Reinsurance Services GmbH, Cologne, Germany; Annika Ziegler, Lukas Stake, Hochschule Koblenz & Ad Res, Germany"

Description: Retirement market study of Mexico in English

Description: Retirement market study of Chile in English.

Description: This paper is a literature review of available papers, publications, articles, and presentations on the topic of longevity risk.

Description: From a session at a meeting of the Society of Actuaries held in Las Vegas, NV May 22-24, 2000 This session covers innovations in pension plan designs and participant options that are happening in a variety of settings.

Description: Full retirement market study of Latin America in English.

Description: Mortality Model Selection and Combination in RAlternative approaches for selecting mortality models and forecasting mortality have been proposed. The usual practice is to base mortality forecasts on a single mortality model selected using in-sample goodness-of-fit measures. However, cross-validation and other alternative out-of-sample measures are increasingly being used in model selection, and model combination methods are becoming a common alternative to using a single mortality model. Such methods combine predictions from multiple mortality models to reduce model choice uncertainty and improve forecast accuracy. Explore techniques to select and combine mortality models using the freely available open source R package CoMoMo (https://github.com/kessysalvatory/CoMoMo), which extends the widely used R package StMoMo (https://CRAN.R-project.org/package=StMoMo). We will use data for the USA and other countries to discuss and demonstrate four different model combination approaches including simple model averaging, Bayesian model averaging, model confidence set, and stacked regression ensembles.

Description: Navigate a perspective on the process of developing estimates and projections of mortality rates, based on a Living to 100 paper by the teacher. Discover the approach and considerations an actuary (or others) can take to develop a mortality rate assumption for five time periods. It will describe techniques to estimate historical mortality rates from which to project future mortality rates.

Description: This article from The Actuary, May 1972 – Volume 6, provides references to three notes: 1. Benefit Loads in Relation to Coverage and Reserves for Railroad Retirement and Other Major Retirement and Social Insurance Plans. 2. Two Decades oj Employee Benefit Plans, 1950- 70. 3. Social Security Programs throughout the World.

Description: "On the decomposition of mortality models into causes of death – Andrés M Villegas Ph.D – UNSW Australia, Madhavi Bajekal, Ph.D.,HONFIA, University of London, UK, Steven Haberman, Ph.D. – Bayes Business School, University of London, UK Forecasting cause-specific mortality rates using the insights from the cointegration analysis, Séverine Arnold, PhD, Viktoriya Glushko, Department of Actuarial Science, Faculty of Business and Economics (HEC Lausanne), University of Lausanne, Switzerland"