Cash flow risk management in the property/-liability insurance industry : a dynamic factor modeling 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">MAP20180025778</controlfield>
<controlfield tag="003">MAP</controlfield>
<controlfield tag="005">20180830121744.0</controlfield>
<controlfield tag="008">180808e20180601usa|||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="245" ind1="1" ind2="0">
<subfield code="a">Cash flow risk management in the property/-liability insurance industry</subfield>
<subfield code="b"> : a dynamic factor modeling approach</subfield>
<subfield code="c">Min-Ming Wen...[et al.]</subfield>
</datafield>
<datafield tag="520" ind1=" " ind2=" ">
<subfield code="a">This study proposes and demonstrates a dynamic factor model that can be empirically carried out by the utilization of a factoraugmented autoregressive technique to explain and forecast the time-varying patterns of cash flows of insurance companies in the United States. Aprincipal component approach is employed in the Factor-Augmented Autoregressive Model (FAARM) to capture the augmented factors that are to be utilized for forecasting. We describe the cash flow statistical model by a dimension-reduction technique that can depict the dynamic patterns of the cash flows of insurance firms and then measure the FAARM model. Results from the first step (principal component analysis) help capture the macroeconomic variables and the variables pertaining to insurance companies' cash flows, namely, cash flows from investment, underwriting, and risk management activities. Results from the second step offer evidence supporting that the FAARM improves the out-of-sample forecasting accuracy assessed by a forecasted root-mean-squared error (FRMSE). This article presents a set of feasible FAAR models from which an insurance firm can choose one that can be a better fit to the firm corresponding to its specific firm characteristics, such as firm size. Consequently, the chosen FAARM(s) can improve the accuracy of cash flow forecasting and thus can help insurers to manage risk via cash-flow-matching techniques.</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20080549985</subfield>
<subfield code="a">Cash-flow</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20080586294</subfield>
<subfield code="a">Mercado de seguros</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20080627638</subfield>
<subfield code="a">Seguro de responsabilidad civil</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20080590567</subfield>
<subfield code="a">Empresas de seguros</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20080597733</subfield>
<subfield code="a">Modelos estadísticos</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20080591182</subfield>
<subfield code="a">Gerencia de riesgos</subfield>
</datafield>
<datafield tag="700" ind1="1" ind2=" ">
<subfield code="0">MAPA20100034507</subfield>
<subfield code="a">Wen, Min-Ming</subfield>
</datafield>
<datafield tag="773" ind1="0" ind2=" ">
<subfield code="w">MAP20077000239</subfield>
<subfield code="t">North American actuarial journal</subfield>
<subfield code="d">Schaumburg : Society of Actuaries, 1997-</subfield>
<subfield code="x">1092-0277</subfield>
<subfield code="g">04/06/2018 Tomo 22 Número 2 - 2018 , p. 309-322</subfield>
</datafield>
</record>
</collection>