臺灣博碩士論文加值系統

本文探討下出界障礙選擇權模型(down and out call option) 應用於預測台灣企業之財務危機上是否較KMV模型更為有效。實務上KMV模型廣為信用風險控管者所使用，其以流動負債加二分之ㄧ長期負債為違約點，本文採用障礙選擇權模型解決KMV模型只考慮到期日違約的缺點並推估最適違約點；此外，我們採用段錦泉教授在1994年提出的最大概似估計法(MLE)來估計資產價值、資產波動度和隱含界限值等未知的參數，進而推估出公司之違約機率。在研究資料的部分，排除金融機構後，從台灣經濟新報中蒐集2002年到2004年共865家資料完整的健全公司和倒閉公司，並將之分成電子業及其他行業，同時挑出上市公司作更進一步的研究。結果發現，由模型推估出上市公司樣本群的違約機率，效果比全部樣本群推估的結果來的好。另一方面，障礙選擇權模型配合MLE推估出的隱含界限值僅約為估計資產價值的30%，比KMV模型設的違約點更適切。從檢定力曲線發現應用障礙選擇權預測公司違約機率比KMV模型準確。

This paper compares the KMV model with the down and out call (DOC) barrier model in terms of their ability to predict corporate financial distress. The KMV model is popular in predicting default probabilities, but it assumes that a firm only defaults at maturity and its default point is current debt plus half long-term debt. We would like to test whether the prediction is improved after considering the barrier level. Based on the KMV model, and the DOC barrier framework, this paper adopts the transformed-data maximum likelihood estimation (MLE) method, which is developed by Duan (1994), to estimate the unobserved asset value, asset value volatility and the barrier level. Our data are obtained from the Taiwan Economic Journal databank (TEJ), and we classify our sample into the electronics industry and other industries. Meanwhile, we select the companies listed on the Taiwan Stock Exchange (TSE) as our sample to test whether the prediction is better than the overall sample for each of the two industry groups, respectively. Our data consist of 865 companies excluding the financial industry over the period from 2002 to 2004. The empirical results indicate that the optimal barrier level is about 30% of the estimated asset market value. Default probabilities that are calculated using the DOC barrier model outperform those inferred from a KMV model in terms of discriminatory power.

Contents
Abstract (Chinese) i
Abstract ii
Contents iii
List of Tables iv
List of Figures v
1. Introduction 1
2. Literature Review 3
3. Theoretical Model 6
3.1 Merton Model 6
3.2 KMV-Merton Model 7
3.3 Down and Out Barrier Option Framework 10
4. Data and Methodology 12
4.1 Data 12
4.1.1 Variable Definition 12
4.1.2 The Definition of Default 12
4.1.3 Data Sources and Sample Selection 13
4.2 Methodology 14
4.2.1 The Transformed-Data Maximum Likelihood Estimation 14
4.2.2 Test Methodology 18
5. Empirical Results and Analysis 20
6. Summary and Conclusion 24
References 28

References
Ammann, M., 2001. Credit Risk Valuation: Methods, Models, and Applications (Springer).
Bharath, S., and T. Shumway, 2004, Forecasting Default with the KMV-Merton Model, Working paper, University of Michigan.
Black, F., and J.C. Cox, 1976, Valuing Corporate Securities: Some Effects of Bond Indenture Provisions, Journal of Finance 31, 351-367.
Bohn, J., N. Arora, and I. Korablev, 2005, Power and Level Validation of the EDF™ Credit Measure in North America, Moody’s KMV.
Brockman, P., and H. J. Turtle, 2003, A Barrier Option Framework for Corporate Security Valuation, Journal of Financial Economics 67, 511-529.
Cox, D.R., and H.D. Miller, 1965. The Theory of Stochastic Processes (Chapman & Hall).
Crouhy, M., D. Galai, and R. Mark, 2000, A Comparative Analysis of Current Credit Risk Models, Journal of Banking & Finance 24, 59-117.
Crouhy, M., D. Galai, and R. Mark, 2003, Risk Management (McGraw-Hill).
Davydenko, S.A., 2005, When Do Firms Default? A Study of the Default Boundary, Working paper, The University of Toronto.
Duan, J. C., 1994, Maximum Likelihood Estimation Using the Price Data of the Derivative Contract, Mathematical Finance 4, 155-167.
Duan, J. C., G. Gauthier, and J. G. Simonato, 2004, On the Equivalence of the KMV and Maximum Likelihood Methods for Structural Credit Risk Models, Working paper, The University of Toronto.
Duffie, D., and D. Lando, 2001, Term Structures of Credit Spreads with Incomplete Accounting Information, Econometrica 69, 633-664.
Duffie, D., and K. J. Singleton, 1999, Modeling Term Structures of Defaultable Bonds, Review of Financial Studies 12, 687-720.
Jarrow, R. A., D. Lando, and S. M. Turnbull, 1997, A Markov Model for the Term Structure of Credit Risk Spreads, Review of Financial Studies 10, 481-523.
Jarrow, R., and S. Turnbull, 1995, Pricing Derivatives on Financial Securities Subject to Credit Risk, Journal of Finance 50,53-85.
Kealhofer, S., 2003, Quantifying Credit Risk I: Default Prediction, Financial Analysts Journal 59, 30-44.
Lando, D., 2004. Credit Risk Modeling: Theory and Applications (Princeton University Press).
Leland, H., and K. B. Toft, 1996, Optimal Capital Structure, Endogenous Bankruptcy, and the Term Structure of Credit Spreads, Journal of Finance 51, 1057-1057.
Longstaff, F. A., and E. S. Schwartz, 1995, A Simple Approach to Valuing Risky Fixed and Floating Rate Debt, Journal of Finance 50, 789-819.
Merton, R. C., 1974, The Pricing of Corporate Debt: The Risk Structure of Interest Rates, Journal of Finance, 29, 449-470.
Patel, K., and P. Vlamis, 2006, An Empirical Estimation of Default Risk of the UK Real Estate Companies, Journal of Real Estate Finance and Economics 32, 21-40.
Reisz, A. S., and .C Perlich, 2004, A Market-Based Framework for Bankruptcy Prediction, Working paper, Baruch College (CUNY).
Ronn, E. I., and A. K. Verma, 1986, Pricing Risk-Adjusted Deposit Insurance: An Option-Based Model, Journal of Finance 41, 871-895.
Saunders, A., and L Allen, 2002. Credit Risk Measurement (John Wiley).
Shih, Kuan-Yu, 2005, Alternative Methods for Estimating KMV Model, Master’s thesis (National Chiao Tung University).
Shumway, T., 2001, Forecasting Bankruptcy More Accurately: A Simple Hazard Model, Journal of Business 74, 101-124.
Tudela, M., and G. Young, 2003, Predicting Default Among UK Companies: A Merton Approach, (Domestic Finance Division, Bank of England).
Wang, H.Y., and T. W. Chio, 2004, On Bias of Testing Merton’s Model, Working paper, The Chinese University of Hong Kong.