Numerical Modeling of Dependent Credit Rating Transitions with Asynchronously Moving Industries

详细信息    查看全文

• 作者：D. V. Boreiko ; Y. M. Kaniovski ; G. Ch. Pflug
• 关键词：Macroeconomic factor ; Markov process ; Loss distribution ; Maximum likelihood ; Credit rating ; Monte ; Carlo simulations ; Correlation
• 刊名：Computational Economics
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：49
• 期：3
• 页码：499-516
• 全文大小：451KB
• 刊物类别：Business and Economics
• 刊物主题：Economic Theory/Quantitative Economics/Mathematical Methods; Computer Appl. in Social and Behavioral Sciences; Operation Research/Decision Theory; Behavioral/Experimental Economics; Math Applications
• 出版者：Springer US
• ISSN：1572-9974
• 卷排序：49

文摘

Two models of dependent credit rating migrations governed by industry-specific Markovian matrices, are considered. Caused by macroeconomic factors, positive and negative unobserved tendencies, encoded as values “1” or “0” of the corresponding variables, modify the transition probabilities and render the evolutions dependent. They are neither synchronized across industry sectors, nor over credit classes: an upswing in some of them can coexist with a decline of the rest. The models are tested on Standard and Poor’s data. MATLAB optimization software and maximum likelihood estimators are used. Obtained distributions of the hidden variables demonstrate that the considered industries migrate asynchronously trough credit classes. Since downgrading probabilities are less affected by the unobserved tendencies, estimated by Monte-Carlo simulations distributions of defaults, exhibit lighter, than for the known coupling models, tails for schemes with asynchronously moving industries. Moreover, the lightest tails were obtained in the case of industry-specific transition matrices.