 Wishart‐gamma random effects models with applications to nonlife insuranceMichel Denuit and Yang Lu Journal of Risk & Insurance, 2021, vol. 88, issue 2, 443-481 Abstract: Random effects are particularly useful in insurance studies, to capture residual heterogeneity or to induce cross‐sectional and/or serial dependence, opening hence the door to many applications including experience rating and microreserving. However, their nonobservability often makes existing models computationally cumbersome in a multivariate context. In this paper, it is shown that the multivariate extension to the Gamma distribution based on Wishart distributions for random symmetric positive‐definite matrices (considering diagonal terms) is particularly tractable and convenient to model correlated random effects in multivariate frequency, severity and duration models. Three applications are discussed to demonstrate the versatility of the approach: (a) frequency‐based experience rating with several policies or guarantees per policyholder, (b) experience rating accounting for the correlation between claim frequency and severity components, and (c) joint modeling and forecasting of the time‐to‐payment and amount of payment in microlevel reserving, when both are subject to censoring. Date: 2021 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jrinsu:v:88:y:2021:i:2:p:443-481 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Risk & Insurance is currently edited by Joan T. Schmit More articles in Journal of Risk & Insurance from The American Risk and Insurance Association Contact information at EDIRC. |
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