Scholarlink

<p>The risk transfer of insurers to reinsurers is one of the most vital operations in insurance markets. Stop-loss contracts are the most widely adopted contract type for such operations. Specifying the main factors, such as retention and maximum (cap) levels in relation to on random loss, is essential to profit from stop-loss contracts. Thus, this study revisits the stop-loss contract modeling explored by [<CitationRef CitationID="CR1">1</CitationRef>], where the geometric Brownian motion (GBM) is provided and extends it by using the time-changed Brownian motion, namely, the variance gamma (VG) and normal inverse Gaussian (NIG) processes. The study aims to utilize these processes to model losses and use their advantages of skewness and kurtosis control and calibration. Further, the study illustrates simulations and compares the simulations with the GBM. The numerical experiments show that the VG and NIG processes are promising alternatives to the GBM in modeling the losses.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Analytical pricing of time dependent stop-loss reinsurance and exposure curves under time-changed Brownian motion

  • 期刊论文

  • 发表日期：

    2026年7月20日

查看全文
  • Bilgi Yilmaz,
  • Ali Alper Hekimoglu,
  • Omur Ugur

摘要

The risk transfer of insurers to reinsurers is one of the most vital operations in insurance markets. Stop-loss contracts are the most widely adopted contract type for such operations. Specifying the main factors, such as retention and maximum (cap) levels in relation to on random loss, is essential to profit from stop-loss contracts. Thus, this study revisits the stop-loss contract modeling explored by [1], where the geometric Brownian motion (GBM) is provided and extends it by using the time-changed Brownian motion, namely, the variance gamma (VG) and normal inverse Gaussian (NIG) processes. The study aims to utilize these processes to model losses and use their advantages of skewness and kurtosis control and calibration. Further, the study illustrates simulations and compares the simulations with the GBM. The numerical experiments show that the VG and NIG processes are promising alternatives to the GBM in modeling the losses.