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

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      <subfield code="a">Gweon, Hyukjun </subfield>
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      <subfield code="a">An Effective bias-corrected bagging method for the valuation of large variable annuity portfolios</subfield>
      <subfield code="c">Hyukjun Gweon ,Shu Li, Rogemar  Mamon</subfield>
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      <subfield code="a">To 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.</subfield>
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      <subfield code="a">Renta vitalicia</subfield>
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      <subfield code="a">Anualidad variable</subfield>
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      <subfield code="a">Cálculo actuarial</subfield>
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      <subfield code="a">Matemática del seguro</subfield>
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      <subfield code="t">Astin bulletin</subfield>
      <subfield code="d">Belgium : ASTIN and AFIR Sections of the International Actuarial Association</subfield>
      <subfield code="x">0515-0361</subfield>
      <subfield code="g">01/09/2020 Volumen 50 Número 3 - septiembre 2020 , p. 853-871</subfield>