دانلود مقاله ISI انگلیسی شماره 41640
عنوان فارسی مقاله

رویکرد بیزی مبتنی بر رابط برای الگوهای متحمل پرداخت ادعایی برای رزرو بیمه غیرعمر

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
41640 2014 21 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A copula based Bayesian approach for paid–incurred claims models for non-life insurance reserving
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Insurance: Mathematics and Economics, Volume 59, November 2014, Pages 258–278

کلمات کلیدی
نردبان زنجیره ای - ادعاهای رزرو - زنجیره مارکوف مونت کارلو تطبیقی
پیش نمایش مقاله
پیش نمایش مقاله رویکرد بیزی  مبتنی بر رابط برای الگوهای متحمل پرداخت ادعایی برای رزرو بیمه غیرعمر

چکیده انگلیسی

In this way the paper makes two main contributions: firstly we develop an extended class of model structures for the paid–incurred chain ladder models where we develop precisely the Bayesian formulation of such models; secondly we explain how to develop advanced Markov chain Monte Carlo sampling algorithms to make inference under these copula dependence PIC models accurately and efficiently, making such models accessible to practitioners to explore their suitability in practice. In this regard the focus of the paper should be considered in two parts, firstly development of Bayesian PIC models for general dependence structures with specialised properties relating to conjugacy and consistency of tail dependence across the development years and accident years and between Payment and incurred loss data are developed. The second main contribution is the development of techniques that allow general audiences to efficiently work with such Bayesian models to make inference. The focus of the paper is not so much to illustrate that the PIC paper is a good class of models for a particular data set, the suitability of such PIC type models is discussed in Merz and Wüthrich (2010) and Happ and Wüthrich (2013). Instead we develop generalised model classes for the PIC family of Bayesian models and in addition provide advanced Monte Carlo methods for inference that practitioners may utilise with confidence in their efficiency and validity.

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