利用KMV的PFM模型來衡量美國壽險業的違約風險

Abstract

不論是對保險監理者或是保戶來說，保險公司是否具有清償能力一直都是大家關注的焦點。這方面的議題探討不勝枚舉。在過去的文獻裡，大家所採用的模型不竟相同，但相同的是，大家焦點都是放在保險公司破產機率這方面。\n 本文使用Moody研發的KMV模型下針對未上市公司有顯著解釋能力的PFM模型(Private Firm Model)。並利用PFM模型來預測北美壽險業的違約風險。一開始，我們先從上市的壽險業中取得足夠的資料，進而去估計未上市壽險業的資產市值及資產波動度，並利用這些資料算出違約距離(Distance-to-Default)。\n 本文的另ㄧ個重點，是將過去文獻中有顯著的比率與違約距離作比較，試圖提出一個能夠代表市場資訊的新比率。因此，我們利用羅吉斯迴歸來對照不同變數下的模型，並利用ROC(Receiver Operating Characteristic Curve)曲線下的範圍來衡量模型的適合度。\n 本文所採用的上市北美壽險業與未上市北美壽險業資料，取自CompuStat、DataStream及NAIC。

Insurer’s solvency has always been the primary concern of insurance regulators and policyholders. Researchers therefore have strived to develop various models to identify potentially troubled insurers. Our paper will contribute to the literature by applying a new method, the KMV’s private firm model (PFM), to predict the solvency/insolvency of life insurers.In this paper, we will apply the KMV’s PFM to estimate the default risk of life insurers. We will first apply the KMV’s public firm model to public life insurers and then use the two simple mapping methods to estimate the asset value and volatility of private life insurers. The estimated values and volatilities can then be used to calculate an insurer’s distance-to-default (DD) and default probability. The predictive power of PFM will be compared with the common ratio analysis using logistic regressions and Receiver Operating Characteristic (ROC) Curves. The data on public and private life insurers will come from CompuStat, DataStream and NAIC’s A-list data respectively. Both are readily available at our university.