Retirement Income Recipes in R [electronic resource] : From Ruin Probabilities to Intelligent Drawdowns / by Moshe Arye Milevsky
- Milevsky, Moshe Arye
- Cham : Springer International Publishing : Imprint: Springer, 2020.
- 1st ed. 2020.
- Physical Description:
- XXIX, 302 pages 16 illustrations, 5 illustrations in color : online resource
- Additional Creators:
- SpringerLink (Online service)
- 1 Setting Expectations and Deviations -- 2 Loading and Getting to Know R -- 3 Coding the (Simple) Financial Life-cycle Model -- 4 Data in R: The Family Balance Sheet -- 5 Portfolio Longevity: Deterministic & Stochastic -- 6 Modeling the Risk of Sequence-of-Returns -- 7 Modeling Human Longevity and Life Tables -- 8 Life & Death in Continuous Time: Gompertz 101 -- 9 The Lifetime Ruin Probability (LRP) -- 10 Life Annuities: From Immediate to Deferred -- 11 Intelligent Drawdown Rates -- 12 Pensionization: From Benefits to Utility -- 13 Biological (and other) Ages -- 14 Exotic Annuities for Longevity Risk -- 15 Very Last Thoughts -- Glossary of User Defined R-Functions. .
- This book provides computational tools that readers can use to flourish in the retirement income industry. Each chapter describes recipe-like algorithms and explains how to implement them via simple scripts in the freely available R coding language. Students can use those skills to generate quantitative answers to the most common questions in retirement income planning, as well as to develop a deeper understanding of the finance and economics underlying the field itself. The book will be an excellent asset for experienced students who are interested in advanced wealth management, and specifically within courses that focus on holistic modeling of the retirement income process. The material will also be useful to current and future wealth management professionals within the financial services industry. Readers should have a solid understanding of financial principles, as well as a rudimentary background in economics and accounting.
- Digital File Characteristics:
- Part Of:
- Springer Nature eBook
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