預測台灣上市上櫃公司財務危機─信用評分法與選擇權評價法之比較

Prediction of financial crisis, taking companies listed in Taiwan Security Exchange and OTC for instance－Comparison of credit scoring model and option pricing model

黃亮維(Liang-Wei Huang)

指導教授 ： 林允永

淡江大學/商學院/財務金融學系碩士班/碩士(2005年)

https://doi.org/10.6846/TKU.2005.00487

摘要

隨著全球財金環境的變遷，信用風險（Credit risk）的問題日漸受重視，衡量信用風險的模型亦不斷推陳出新，而在眾多衡量信用風險的模型中，信用評分法以及選擇權評價法被廣泛使用，本研究旨在比較此兩大信用風險衡量模式在預測台灣企業之財務危機上何者較為準確，其中以Altman（1968）所提出的Z-Score作為信用評分法的代表；而選擇權評價模式則以路徑相依模式取代傳統路徑獨立模式。實證的對象為2000年至2004年台灣上市上櫃公司。在模型比較上採用三大比較法：群內分析法（intra-cohort analysis）、Logit迴歸分析，以及檢定力曲線比較法。實證結果發現，三種比較方法均顯示信用評分法在預測公司財務危機上優於選擇權評價法。

關鍵字

信用風險 ； 信用評分法 ； 選擇權評價法 ； 財務危機

並列摘要

With the flourishing development of the financial market and institution, the measurement and management of the credit risk becomes more and more important. There are many methods to measure the credit risk, such as the credit scoring model and the option pricing model are used extensively. This paper compares these two major credit risk model: “Altman’s Z-Score (credit scoring) and barrier option pricing model (option pricing).” Using the data of Taiwan’s listed companies from 2000 to 2004, and we adopt three major comparative laws: intra-cohort analysis, logit regression, and power curve. The empirical results of three kinds of comparative methods all get the same conclusion that Z-Score is better than barrier option pricing model.

並列關鍵字

Credit risk ； Credit Scoring ； Option Pricing ； Financial Crisis

參考文獻

◎陳業寧、王衍智、許鴻英（2004），「台灣企業財務危機之預測：信用評分法與選擇權評價法孰優？」，風險管理學報，第6卷第2期，頁155~179。

◎胡志宏（2003），新版巴塞爾資本協定對我國金融業信用風險管理之衝擊，元智大學管理研究所碩士論文。

◎Altman, E. I. and Anthony Saunders,（1998）, “Credit Risk Measurement: Developments over the Last 20 Years,” Journal of Banking and Finance, Vol. 21, pp. 1721-1742.

◎Altman, E. I., Haldeman R. and Narayanan P.,（1977）, “ZETA Analysis, A New Model to Identify Bankruptcy Risk of Corporations,” Journal of Finance, Vol. 1, pp. 29-54.

◎Altman, E. I., Marco G. and Varetto F.,（1994）, “Corporate distress diagnosis: Comparisons using linear discriminate analysis and Neural networks(The Italian Experience),” Journal of Banking and Finance, Vol. 18, pp. 505-529.