This paper introduces a method for pricing insurance policies using market data. The approach is designed for scenarios in which the insurance company seeks to enter a new market, in our case: pet insurance, lacking historical data. The methodology involves an iterative two-step process. First, a suitable parameter is proposed to characterize the underlying risk. Second, the resulting pure premium is linked to the observed commercial premium using an isotonic regression model. To validate the method, comprehensive testing is conducted on synthetic data, followed by its application to a dataset of actual pet insurance rates. To facilitate practical implementation, we have developed an R package called IsoPriceR. By addressing the challenge of pricing insurance policies in the absence of historical data, this method helps enhance pricing strategies in emerging markets.

翻译：本文提出了一种利用市场数据为保险产品定价的方法。该方法适用于保险公司在缺乏历史数据的情况下进入新市场的场景，本文以宠物保险为例。该方法采用迭代式的两步流程：首先，提出一个合适的参数来表征潜在风险；其次，通过保序回归模型将计算得到的纯保费与观察到的商业保费进行关联。为验证该方法，我们在合成数据上进行了全面测试，随后将其应用于实际宠物保险费率数据集。为便于实际应用，我们开发了名为IsoPriceR的R软件包。该方法通过解决无历史数据情况下的保险定价难题，有助于提升新兴市场的定价策略。