One year value at risk for longevity and mortality
<?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">MAP20110070601</controlfield>
<controlfield tag="003">MAP</controlfield>
<controlfield tag="005">20111214125903.0</controlfield>
<controlfield tag="008">111202e20111101esp|||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">6</subfield>
</datafield>
<datafield tag="100" ind1=" " ind2=" ">
<subfield code="0">MAPA20090033245</subfield>
<subfield code="a">Plat, R.</subfield>
</datafield>
<datafield tag="245" ind1="0" ind2="0">
<subfield code="a">One year value at risk for longevity and mortality</subfield>
<subfield code="c">R. Plat</subfield>
</datafield>
<datafield tag="520" ind1=" " ind2=" ">
<subfield code="a">Upcoming new regulation on regulatory required solvency capital for insurers will be predominantly based on a one-year Value-at-Risk measure. This measure aims at covering the risk of the variation in the projection year as well as the risk of changes in the best estimate projection for future years. This paper addresses the issue how to determine this Value-at-Risk for longevity and mortality risk. Naturally, this requires stochastic mortality rates. In the past decennium. a vast literature on stochastic mortality models has been developed. However, very few of them are suitable for determining the one year Value-at-Risk. This requires a model for mortality trends instead of mortality rates. Therefore. We will introduce a stochastic mortality trend model that fits this purpose. The model is transparent, easy to interpret and based on well known concepts in stochastic mortality modeling. Additionally, we introduce an approximation method based on duration and convexity concepts to apply the stochastic mortality rates to specific insurance portfolios</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="1">
<subfield code="0">MAPA20080564254</subfield>
<subfield code="a">Solvencia II</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="1">
<subfield code="0">MAPA20080602437</subfield>
<subfield code="a">Matemática del seguro</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="1">
<subfield code="0">MAPA20090039629</subfield>
<subfield code="a">Riesgo actuarial</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="1">
<subfield code="0">MAPA20080555306</subfield>
<subfield code="a">Mortalidad</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="1">
<subfield code="0">MAPA20080555016</subfield>
<subfield code="a">Longevidad</subfield>
</datafield>
<datafield tag="773" ind1="0" ind2=" ">
<subfield code="w">MAP20077100574</subfield>
<subfield code="t">Insurance : mathematics and economics</subfield>
<subfield code="d">Oxford : Elsevier, 1990-</subfield>
<subfield code="x">0167-6687</subfield>
<subfield code="g">01/11/2011 Tomo 49 Número 3 - 2011 , p. 462-470</subfield>
</datafield>
</record>
</collection>