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標題: 產業脆弱相關之違約模型驗證─以台灣上市櫃公司為例
Default Model with Industrial Frailty:An Empirical Study for Taiwan Public Listed Firms
作者: Ken-Yu Lin
關鍵字: 脆弱因子;違約強度;違約群聚;馬可夫鏈蒙地卡羅;期望最大演算法;frailty;default intensity;default clustering;MCMC;EM-algorithm
引用: 黃瑞卿、諶自強 (2011),'脆弱相關違約模型─台灣上市櫃公司實證研究',《中國統計學報》,49:3,98-122。 黃瑞卿、吳中書、林金龍、蕭兆祥 (2012),'臺灣企業財務危機因子的實證研究',《臺灣金融財務季刊》,13:4,55-77。 Altman, Edward I. (1968), 'Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,' The Journal of Finance, 23:4, 589-609. Altman, Edward I., Haldeman, Robert G., and Narayanan P. (1977), 'Zeta analysis: A new model to identify bankruptcy risk of corporations,' Journal of Banking and Finance, 1, 29-54. Basel Committee on Banking Supervision (2005), 'Studies on the Validation of Internal Rating Systems,' Working Paper No. 14, Bank for International Settlements. Bharath, S. T. and Shumway, T. (2004), 'Forecasting Default with the KMV-Merton Model,' AFA 2006, Boston, US. Black, F. and Scholes, M. (1973), 'The Pricing of Options and Corporate Liabilities,' The Journal of Political Economy, 81:3, 637-654. Beaver, William H. (1966), 'Financial Ratios as Predictors of Failure,' Journal of Accounting Research, 4, 71-111. Chava, S., Stefanescu, C., and Turnbull, S. (2011), 'Modeling the Loss Distribution,' Management Science, 57:7, 1267-1287. Clayton, D. G. (1978), 'A Model for Association in Bivariate Life-Tables and its Application in Epidemiological Studies of Chronic Disease Incidence,' Biometrika, 65:1, 141-151. Cox, David R. (1955), 'Some Statistical Methods Connected with Series of Events,' Journal of the Royal Statistical Society, 17:2, 129-164. Cox, David R. (1972), 'Regression Models and Life-Tables,' Journal of the Royal Statistical Society, 34:2, 187-220. Christian P. Robert and George Casella (2009), Introducing Monte Carlo Methods with R, USA, Spinger. Das, S., Duffie, D., Kapadia, N., and Saita, L. (2007), 'Common Failings: How Corporate Defaults Are Correlated,' The Journal of Finance, 62:1, 93-117. Duan, J.C. (2010), 'Clustered Defaults,' National University of Singapore – Business School and Risk Management Institute. Duffie, D., Saita, L., and Wang, K. (2007), 'Multi-Period Corporate Default Prediction with Stochastic Covariates,' Journal of Financial Economics, 83: 3, 635-665. Duffie, D., Eckner, A., Horel, G., and Saita, L. (2009), 'Frailty Correlated Default,' The Journal of Finance, 64:5, 2089-2123. Gelfand, A.E. and Smith, A.F.M. (1990), 'Sampling-Based Approaches to Calculating Marginal Densities,' Journal of the American Statistical Association, 85, 398-409. Hastings, W.K. (1970), 'Monte Carlo Sampling Methods Using Markov Chains and Their Applications,' Biometrika, 57, 97-109. John C. Hull (2010), Options, Futures,and Other Derivatives, USA, Prentice Hall. Lane, W.R., Looney, S.W., and Wansley, J.W. (1986), 'An Application of the Cox Propotional Hazards Model to Bank Failure,' Journal of Banking and Finance, 10:4, 511-531. Lefebvre, Mario (2005), Applied Stochastic Processes, Springer. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., and Teller, E. (1953), 'Equations of State Calculations by Fast Computing Machines,' Journal of Chemical Physics, 21:6, 1087-1092. Merton, Robert C. (1974), 'On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,' Journal of Finance, 29:2, 449-470. Ohlson, J. (1980), 'Financial Ratios and the Probabilistic Prediction of Default,' Journal of Accounting Research, 18: 1, 109-131. Ronn, E.I. and Verma, A.K. (1986), 'Pricing Risk-Adjusted Deposit Insurance: An Option-Based Model,' The Journal of Finance, 41:4, 871-896. Vassalou, M. and Xing, Y. (2004), 'Default Risk in Equity Returns,' The Journal of Finance, 59, 831-868.
本論文首先說明違約預測模型的重要性,並介紹三種違約預測模型,包括歷史分析模型、公司價值模型以及違約強度模型。之後探討違約群聚現象對違約模型造成的問題,其中違約強度模型的條件獨立性質亦受違約相關性影響。因此本論文依據Duffie et al. (2009)及Chava et al. (2011)的方法中,以脆弱因子改善違約強度模型的假設缺陷,兩個模型皆以EM演算法求解參數,且前者利用馬可夫鏈蒙地卡羅法產生脆弱因子。並且以費雪離散檢定及卡方適合度檢定檢測模型適合度,以測試脆弱因子的效果。本文以台灣上市櫃公司為例,分別估計傳統違約強度模型與產業脆弱因子違約強度模型,最後實證結果顯示,產業脆弱因子雖無法處理違約事件在時間維度的相關性,但可充分捕捉在產業維度上的相關性。

This paper first shows how important default models are and introduces the main types of default models, including credit scoring models, structural models and intensity models. However, most default models have problems when they deal with default clustering because of the assumption of independence. In the case of intensity model, it has an assumption of conditional independence of default events which is used to construct the likelihood function and still has biases when default clustering happens. To improve the model assumption, we follow Duffie et al. (2009) and Chava et al. (2011) which add time-variant and industrial frailty into intensity function and expect the frailty could capture the correlation between defaults. Both of these two models use EM algorithm to solve the parameters, and the former also use MCMC algorithm to generate the value of frailty. In the empirical study, we use Taiwan public listed firms as data samples and implement an intensity model without frailty and the other with industrial frailty. Moreover, we conduct several goodness-of-fit tests including Fisher dispersion test and chi-square test. As the result shows, though the industry correlated intensity model failed to capture the correlation in time dimension, it did have a great improvement in industrial dimension.
其他識別: U0005-2406201413153100
Rights: 同意授權瀏覽/列印電子全文服務,2016-07-02起公開。
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