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Comparative Study of Modern Credit Risk Assessment Methods اﻟﺗﺣﻟﯾل

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Comparative Study of Modern Credit Risk Assessment Methods اﻟﺗﺣﻟﯾل Comparative study of modern credit risk assessment methods التحليل المقارن المعاصر ﻷساليب تقييم مخاطر اﻹفﻻس By Yerzhan Narzhanov Dissertation submitted in fullfillment of MSc in Finance and Banking Faculty of Finance and Banking Dissertation Supervisor Professor Pandey Dayanand January-2011 )وصف مختصر( Abstract القدرة على التنبؤ باﻻفﻻس هي ذات قيمة كبيرة للمستثمرين الدائنين وغيرهم من مستخدمي معلومات للشركة. وعﻻوة على ذلك كما اتضح من اﻷزمة اﻷخيرة - اﻻخنخاا المالي له أثر المضاعف على مستوى اﻻقتصاد الكلي وتكليف الحكومة والمجتمع غاليا. تنبئ خنماذج متعددة باﻹفﻻس و هي و ِضعت و ُع ِرضت من قبل العلم في السنوات اﻷخيرة. في هذه اﻷطروحة قد أستعملت أساليب التقييم التالية: أ( Merton’s model ب( KMV ت( Z-score ث( Binomial approach في هذا العمل أستخدمت بياخنات 41 شركة على مدى 3 سنوات )7002-7002( و هذه الشركات تمثل 1 مجاﻻت: أ( الصرافة ب( صناعة السيارات ت( صناعة اﻹلكتروخنيات ث( الناط و الغاز من بياخنات 41 شركة – 47 منها خناجحة, و 7 منها أفلست في سنة 7002. استنتاجات اﻷطروحة تتضمن مناقشة خنتائج اﻻختبارات التي أجريت من أجل تحديد إحتمال اﻹفﻻس. أيضا من هذه اﻷساليب المدروسة تم تحديد أفضل خنموذج ﻻختبار احتمال اﻹفﻻس. 2 Abstract The ability to predict bankruptcy is of great value for investors, lenders and other stakeholders of the companies. Moreover as it has been shown by the recent crisis originated in sub-prime market the financial distress can have multiplicative macroeconomic effect and bring high cost to economy of countries and the society. Thus there are various models to forecast failure of the firms which have been developed and proposed by academics in the recent years. This dissertation presents the basic framework and structure of four credit risk assessment models, namely (1) Merton's structural model, (2) KMV, (3) Z-score, and (4) Binominal approach. Then, work discusses limitations to practical usage of each model, and it also explains some necessary conditions before implementing these models. Real historical data was used to examine the effectiveness of each model in early bankruptcy forecasting. Financial variables of fourteen companies from four different industries were analyzed with two companies in the sample which eventually went bankrupt. The following industries are under consideration in this study − (1) banking, (2) automobile, (3) electronics, and (4) oil and gas sector. Back testing simulation was run on company's financial data collected for 3-5 years pre sub-prime crisis time horizon. On purpose, sample from each industry contains one company that has really defaulted in subsequent years. As a conclusion, work contains a discussion on results obtained to derive a conclusion on which risk assessment model(s) is (are) best in identifying pre-default companies. 3 Keywords: Merton, KMV, Z-score, Binominal, Distance-to-Default (DD), Expected Default Frequency (EDF), Implied Default Probability (IDP). Contents 1. Introduction ....................................................................................................................... 5 1.1. Academic background..................................................................................................... 8 1.1.1. Credit risk assessment model − Merton's contingent-claims approach ........... 8 1.1.2. Credit risk assessment model − KMV model ................................................ 15 1.1.3. Credit risk assessment model − Z-score ........................................................ 21 1.1.4. Credit risk assessment model − Binomial option pricing model ................... 29 1.2. Literature review ............................................................................................................ 33 1.2.1. Extensions of Merton’s model and First passage time models ..................... 35 1.2.2. KMV model ................................................................................................... 40 1.2.3. Z-score model ................................................................................................ 42 1.2.4. Binomial approach ......................................................................................... 45 2. Academic study ............................................................................................................ 47 2.1. Hypothesis ...................................................................................................................... 47 2.2. Definition of terms ........................................................................................................ 48 2.3. Methodology .................................................................................................................. 49 2.3.1. The methodology employed to measure Merton model estimates ................ 49 2.3.2. The methodology employed to measure KMV estimates ............................. 50 2.3.3. The methodology employed to measure Altman's Z-score model estimates 53 2.3.4. The methodology employed to measure Binomial approach estimates ........ 54 A. Build the binomial price tree ................................................................................ 54 B. Finding the price that option takes at each final node .......................................... 55 C. Find Option value at all previous ticks ................................................................. 56 2.4. Data sampling................................................................................................................. 57 2.4.1. Definition of defaulted company ................................................................... 57 2.4.2. Selection of financial variables and market data ........................................... 58 2.4.3. Selection of companies .................................................................................. 58 2.4.4. Banking industry ........................................................................................... 60 2.4.5. Oil and Gas industry ...................................................................................... 62 2.4.6. Automobile industry ...................................................................................... 63 2.4.7. Electronics industry ....................................................................................... 63 2.5. Data limitations .............................................................................................................. 65 3. Data Analysis ............................................................................................................... 68 4. Conclusions .................................................................................................................. 75 References ........................................................................................................................... 78 Appendix 1. ......................................................................................................................... 81 4 1. Introduction Credit assessment is an acknowledged worldwide issue – the direct and indirect costs associated with financial distress of the companies are enormous and affect both shareholders and stakeholders. Recent failure of such large companies as General Motors, Lehman Brothers, Chrysler and many more, has demonstrated that even giant corporations are not insured against failure and distress caused by their bankruptcy can lead to both economic and social problems and trigger financial crisis on a worldwide level. Thus both corporates and regulators pay so much attention to this issue and research on predictability of bankruptcy has become so important. A number of research papers have been devoted to the problem of credit assessment during last four decades. However two main methodologies and their extensions had gained the most popularity among both academics and practitioners. Specifically these are structural models, which are based on original approach proposed by Merton (1974) and reduced-form models which are based on model developed by Altman (1968). Data sample consist from financial variables of 14 companies for the time period of 2006-2009. Two companies in the selected pool have been classified as “defaulted” in this paper due to the fact that they have experienced to some extent or another troubles with meeting their financial obligations. The time horizon has been chosen deliberately so it would focus on recent turmoil in the World’s economy, thus each model could be tested on its effectiveness in predicting the possible failure. This paper compliments prior studies in several ways. First of all, the research objective of this study is to provide comparative analysis of the predictability powers of modern credit risk assessment methods: Altman’s Z-score and Merton’s structural models, 5 commercialized version of Merton’s approach called Moody’s KMV and binomial model. To the knowledge of the author this is the first attempt to compare these four methods in one research paper. Secondly, the study concentrates on time period of recent Global crisis, which followed the sub-prime lending crisis in USA, a time when it is especially crucial for institutions and supervisory bodies to be able to forecast financial distress. Thus this paper estimates the efficiency in forecasting powers of the considered models under distress environment. This part aims to provide background information on each of four selected modern credit risk assessment methods: (1) Merton's structural model, (2) KMV, (3) Z-score, and (4) Binominal. Academic background section describes underlying theoretical frameworks that explain cause and effect relationships
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