Modeling multicountry longevity risk with mortality dependence : a Lévy subordinated hierarchical archimedean copulas approach

<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
  <record>
    <leader>00000cab a2200000   4500</leader>
    <controlfield tag="001">MAP20170015130</controlfield>
    <controlfield tag="003">MAP</controlfield>
    <controlfield tag="005">20170602100512.0</controlfield>
    <controlfield tag="008">170518e20170403esp|||p      |0|||b|spa d</controlfield>
    <datafield tag="040" ind1=" " ind2=" ">
      <subfield code="a">MAP</subfield>
      <subfield code="b">spa</subfield>
      <subfield code="d">MAP</subfield>
    </datafield>
    <datafield tag="084" ind1=" " ind2=" ">
      <subfield code="a">7</subfield>
    </datafield>
    <datafield tag="100" ind1=" " ind2=" ">
      <subfield code="0">MAPA20170005773</subfield>
      <subfield code="a">Zhu, Wenjun</subfield>
    </datafield>
    <datafield tag="245" ind1="1" ind2="0">
      <subfield code="a">Modeling multicountry longevity risk with mortality dependence</subfield>
      <subfield code="b">: a Lévy subordinated hierarchical archimedean copulas approach</subfield>
      <subfield code="c">Wenjun Zhu, Ken Seng Tan, Chou-Wen Wang</subfield>
    </datafield>
    <datafield tag="520" ind1=" " ind2=" ">
      <subfield code="a">This article proposes a new copula model known as the L¿evy subordinated hierarchical Archimedean copulas (LSHAC) for multicountry mortality dependence modeling. To the best of our knowledge, this is the first article to apply the LSHAC model to mortality studies. Through an extensive empirical analysis on modeling mortality experiences of 13 countries, we demonstrate that the LSHAC model, which has the advantage of capturing the geographical structure of mortality data, yields better fit, compared to the elliptical copulas. In addition, the proposed LSHAC model generates out-of-sample forecasts with smaller standard deviations, when compared to other benchmark copula models. The LSHAC model also confirms that there is an association between geographical locations and dependence of the overall mortality improvement. These results yield new insights into future longevity risk management. Finally, the model is used to price a hypothetical survival index swap written on a weighted mortality index. The results highlight the importance of dependence modeling in managing longevity risk and reducing population basis risk. </subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080591182</subfield>
      <subfield code="a">Gerencia de riesgos</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080555016</subfield>
      <subfield code="a">Longevidad</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20090035034</subfield>
      <subfield code="a">Modelización mediante cópulas</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080579258</subfield>
      <subfield code="a">Cálculo actuarial</subfield>
    </datafield>
    <datafield tag="773" ind1="0" ind2=" ">
      <subfield code="w">MAP20077000727</subfield>
      <subfield code="t">The Journal of risk and insurance</subfield>
      <subfield code="d">Nueva York : The American Risk and Insurance Association, 1964-</subfield>
      <subfield code="x">0022-4367</subfield>
      <subfield code="g">03/04/2017 Volumen 84 Número S1 - abril 2017 , p. 477-493</subfield>
    </datafield>
  </record>
</collection>