An Effective bias-corrected bagging method for the valuation of large variable annuity portfolios

Recurso electrónico / Electronic resource
Registro MARC
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001  MAP20200029762
003  MAP
005  20200924174412.0
008  200924e20200901bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
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100  ‎$0‎MAPA20200019138‎$a‎Gweon, Hyukjun
24513‎$a‎An Effective bias-corrected bagging method for the valuation of large variable annuity portfolios‎$c‎Hyukjun Gweon ,Shu Li, Rogemar Mamon
520  ‎$a‎To evaluate a large portfolio of variable annuity (VA) contracts, many insurance companies rely on Monte Carlo simulation, which is computationally intensive. To address this computational challenge, machine learning techniques have been adopted in recent years to estimate the fair market values (FMVs) of a large number of contracts. It is shown that bootstrapped aggregation (bagging), one of the most popular machine learning algorithms, performs well in valuing VAcontracts using related attributes. In this article, we highlight the presence of prediction bias of bagging and use the bias-corrected (BC) bagging approach to reduce the bias and thus improve the predictive performance. Experimental results demonstrate the effectiveness of BC bagging as compared with bagging, boosting, and model points in terms of prediction accuracy.
650 4‎$0‎MAPA20080573614‎$a‎Renta vitalicia
650 4‎$0‎MAPA20200019183‎$a‎Anualidad variable
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
650 4‎$0‎MAPA20080602437‎$a‎Matemática del seguro
7730 ‎$w‎MAP20077000420‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association‎$x‎0515-0361‎$g‎01/09/2020 Volumen 50 Número 3 - septiembre 2020 , p. 853-871