Yield curve extrapolation with machine learning

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<rdf:Description>
<dc:creator>Akiyama, Shinobu</dc:creator>
<dc:creator>Matsuyama, Naoki</dc:creator>
<dc:creator>International Actuarial Association</dc:creator>
<dc:date>2025-01-29</dc:date>
<dc:description xml:lang="es">Sumario: Yield curve extrapolation to unobservable tenors is a key technique for the market-consistent valuation of actuarial liabilities required by Solvency II and forthcoming similar regulations. Since the regulatory method, the SmithWilson method, is inconsistent with observable yield curve dynamics, parsimonious parametric models, the NelsonSiegel model and its extensions, are often used for yield curve extrapolation in risk management</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/189392.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Matemática del seguro</dc:subject>
<dc:subject xml:lang="es">Empresas de seguros</dc:subject>
<dc:subject xml:lang="es">Requerimientos financieros</dc:subject>
<dc:subject xml:lang="es">Solvencia II</dc:subject>
<dc:subject xml:lang="es">Tasa interna de rendimiento</dc:subject>
<dc:subject xml:lang="es">Cálculo actuarial</dc:subject>
<dc:subject xml:lang="es">Machine learning</dc:subject>
<dc:subject xml:lang="es">Gestión de riesgos</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Yield curve extrapolation with machine learning</dc:title>
<dc:relation xml:lang="es">En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 29/01/2025 Volume 55 Issue 1 - January 2025 , p. 76 - 96</dc:relation>
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