Stationary probabilities and the monotone likelihood ratio in bonus-malus systems

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<dc:creator>Csaba Ágoston, Kolos </dc:creator>
<dc:creator>Papp, Dávid</dc:creator>
<dc:creator>International Actuarial Association</dc:creator>
<dc:date>2026-01-15</dc:date>
<dc:description xml:lang="es">Sumario: This paper examines bonusmalus systems (BMS) and demonstrates that their stationary probabilities satisfy the monotone likelihood ratio property (MLRP), a key feature that explains why higher-risk policyholders tend to remain in higher premium classes, while lower-risk individuals migrate toward lower premiums. Focusing on BMS models represented by an ergodic Markov chain with a +1/d transition rule and at most one claim per period, the authors establish the MLRP using linear recurrences derived from the stationary distributionan analytical approach not previously applied in this context. The study also highlights an important practical implication: in bonusmalus design problems, the premium scale becomes automatically monotonic when this property holds</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/189433.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">Seguro de automóviles</dc:subject>
<dc:subject xml:lang="es">Tarificación</dc:subject>
<dc:subject xml:lang="es">Bonus-malus</dc:subject>
<dc:subject xml:lang="es">Matemática del seguro</dc:subject>
<dc:subject xml:lang="es">Modelos matemáticos</dc:subject>
<dc:subject xml:lang="es">Estadística matemática</dc:subject>
<dc:subject xml:lang="es">Cálculo actuarial</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Stationary probabilities and the monotone likelihood ratio in bonus-malus systems</dc:title>
<dc:relation xml:lang="es">En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 19/01/2026 Volume 56 Issue 1 - January 2026 , p. 89 - 100</dc:relation>
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