Finite time ruin probabilities for tempered stable insurance risk processes

<?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">MAP20130033006</controlfield>
    <controlfield tag="003">MAP</controlfield>
    <controlfield tag="005">20131112164945.0</controlfield>
    <controlfield tag="008">131008e20130902esp|||p      |0|||b|eng 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="1" ind2=" ">
      <subfield code="0">MAPA20130014142</subfield>
      <subfield code="a">Griffin, Philip S.</subfield>
    </datafield>
    <datafield tag="245" ind1="1" ind2="0">
      <subfield code="a">Finite time ruin probabilities for tempered stable insurance risk processes</subfield>
      <subfield code="c">Philip S. Griffin,  Ross A. Maller, Dale Roberts</subfield>
    </datafield>
    <datafield tag="520" ind1=" " ind2=" ">
      <subfield code="a">We study the probability of ruin before time t for the family of tempered stable Lévy insurance risk processes, which includes the spectrally positive inverse Gaussian processes. Numerical approximations of the ruin time distribution are derived via the Laplace transform of the asymptotic ruin time distribution, for which we have an explicit expression. These are benchmarked against simulations based on importance sampling using stable processes. Theoretical consequences of the asymptotic formulae indicate that some care is needed in the choice of parameters to avoid exponential growth (in time) of the ruin probabilities in these models. This, in particular, applies to the inverse Gaussian process when the safety loading is less than one.</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080611613</subfield>
      <subfield code="a">Modelos probabílisticos</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080576790</subfield>
      <subfield code="a">Modelo Gaussiano</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080602437</subfield>
      <subfield code="a">Matemática del seguro</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080579258</subfield>
      <subfield code="a">Cálculo actuarial</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20130016245</subfield>
      <subfield code="a">Maller,  Ross A.</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20130016252</subfield>
      <subfield code="a">Roberts, Dale</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">02/09/2013 Volumen 53 Número 2 - septiembre 2013 , p. 478-489</subfield>
    </datafield>
  </record>
</collection>