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: "COVID-19 and excess mortality: an actuarial study – Camille Delbrouck, Aon Benfield, Belgium, Jennifer Alonso-García, Department of Mathematics, Université Libre de Bruxelles, Belgium, ARC Centre of Excellence in Population Ageing Research (CEPAR), UNSW Sydney, Australia Netspar, The Netherlands How did COVID-19 change the dependence structure across major causes of death – Han Li, Ph.D., Associate Professor, Centre for Actuarial Studies | Faculty of Business and Economics, The University of Melbourne, Victoria, Australia"

Description: "Weighted compositional data analysis for modeling and forecasting life-table death counts – Han Lin Shang, Ph.D., Professor of Business Analytics and Director of Research at the Department of Actuarial Studies and Business Analytics at Macquarie Business School, Macquarie University, Sydney, Australia, Steven Haberman, PhD, Bayes Business School, City University of London, UK Compensation Effect of Mortality as a Challenge to Life Extension – Natalia S. Gavrilova, Ph.D., Leonid A. Gavrilov, Ph.D. Academic Research Centers, NORC at the University of Chicago"

Description: Discover an introduction to decomposing differences in life expectancy by age and cause of death. Take a look into demographic techniques that decompose life expectancies at birth and older ages into the portions contributed by later age groups and causes of death-. These have been in existence for some time and provide a convenient device for analyzing mortality change over time as well as differences between sexes, countries, and ethnic groups. The methods will be described and illustrated by spreadsheet examples.

Description: "Demographic components of Future Potential Old-Age Support Ratios – Kirill Andreev, Ph.D., United Nations, Population Division, New York, Mila Andreeva, KAMA Consulting Effect of internal Migration on the Health of Taiwan’s Elderly People – Jack C. Yue, Ph.D., – National Chengchi University, Department of Statistics, Taipei, TaiwanPopulation Aging and Canada’s Social Contract: Covid-19 Catalyst for Change – Douglas Andrews, FSA, FCIA, Ph.D., Lori J. Curtis, Ph.D., – University of Waterloo"

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: "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"

Description: Mortality is a fundamental element in the financial projections of the finances of social security programs around the world. Prominent actuaries from the U.S. Social Security Administration, Canada's Office of Superintendent of Financial Institutions and the U.K. Government Actuary's Department will discuss some of the key factors and current issues associated with these projections. Improving mortality has and will have a significant effect on the affordability and importance of income through retirement to many population segments in these countries.

Description: Overview/Foreword by the 2017 Symposium Chair and Foreword by the 2020 Symposium Chair.