International conferences Topic: Credibility of Credit Rating Agencies and Efficiency of Credit Risk Metrics

Transcription

1 International conferences Topic: Credibility of Credit Rating Agencies and Efficiency of Credit Risk Metrics Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal* =========================================================================================== Ongoing Financial crisis have been raised a number of issues of effectiveness as well efficiency of Risk Matrices to identify the Risk associated with financial engineering and advance derivative instrument product. However, it is said that these product are essential as they reduce transaction cost and speedy settlement to develop standardized product as well to help to develop in mature Markets. Whereas role of credit rating agencies become more critical and recently have been criticized mostly due to their methodology and parameters they have taken to provide rating and various model to use to elucidate risk associated with the financial instrument product. So, Paper is going to explore the various pro and cons of credit risk matrices Keywords: Risk Matrices, Financial Instrument, Credit Risk. ===================================================================== Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 11

2 In the developed and underdeveloped financial market significant changes have been done in the last few decades including development of highly sophisticated financial product, setting up huge number of Institution, continuous innovation in complex derivative instrument, and advance financial engineering product which reduce risk as well as transaction cost in the Market. However to maintain the credibility of those advance derivative instrument is become really challengeable due to lack of transparency and disclosure while there is advantage from these product for transferring credit risk to the third Party. However, the product depicted inherent risk with that instrument and the role of Credit Rating Agency CRA s become essential as they are solely and Independent to elucidate the risk lies within the instrument so, this Institution becomes helpful to provide transparency for the Investment decision. It is said that CRA s use unique methodology to assess the inherent risk most importantly Market and Credit Risk associated with these instrument and due to lack of identified risk which rationalized the creditworthiness of the instruments as well party who are actively involve to transferring risk with the help of those financial derivative instruments. So, precisely role of CRA s remain vital to assess underlying risk (i.e. market risk, interest rate risk, operational risk, liquidity risk, credit risk etc) of which liquidity risk and interest risk can be manage by assigning portfolio through so called securitization as what financial institution did in the past. Role of CRA s in the Banking The role of CRA is to provide parallel information to the financial institutions, banking, Corporate and sovereign government to access this information and help to reduce the cost of capital and to advise suitability of investment. Thus the role of credit rating agencies become more vital first, to assist in the development of financial market in the way of sharing information and strength of the institutions. Second, to assist regulator to indentify risk by assigning ratings are one of the important job. A new shape was given to the rating agencies under the Basel-II as CRA s has been assigned huge responsibility to determining the Risk weight for capital charges form the borrowers in the three formats (i) the foundation approach; (ii) the standardized approach; (iii) the Internal ratings-based approach; Generally first two done by external credit rating agencies while under third one the risk is assessed by banks itself as per supervisory approval by calculating PD (probability of default) over the time horizon and other measured like loss given default (LGD) and exposure at default (EAD). These quantities tools also used by CRA s to estimate the default or occurrence of default over a expected time, PD widely used by S&P while Moody s focus on the expected loss (EL) and expected recovery rate (RE), while Fitch focus on both PD and RE to estimated of the default occurrence on the basis of past with comparison to the actual and use sophisticated model to calculate such LGD and EAD like simulation and credit risk metric or credit risk+ of Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 12

3 Kamakura methodology are standard approach. However, this methodology has number of advantage that it focused on volatility rather than only relying on market value of equity, and used information to predict default whereas the implicit limitation is to return on firm s assets cannot be directly observed; while credit metric use the publically available information for predicting and modeling for portfolio risk management and have less consideration on individual bonds and returns on the securities and suffer from its own limitation. Banks usually involve in huge off balance sheet items which inherently risky nature as individual transaction are subject to risk associated with them due to probability of default and to mitigate risk they transferred those collateral to SPV (special purpose vichel) and then put in the pool to risky collateral and issued a securities with assigning different rating with the help of rating agencies and sold to mitigate risk and to generate liquidity, these all process known as securitization. Here role of CRA s are not transparent and risk lies inherent with the securitization process and spread over the financial institution those who invested in the securities. Further, top notch credit rating agencies also provides rating not only of governments bonds, but also corporate instruments, treasury bills, highly sophisticated financial products, and rating of independent countries, their currency and for other individual financial institutions on the basis of their performance and financial highlights and fiscal indicators. To provide rating to the any instrument are complex process and to watch continuously inherited risk is again continuous process and to provide rating different countries which are geographically scattered are one of the difficult task, apart from that there is ample scope that they have been criticized because of the selection of parameters they have taken and some time miss-interpretation is the basic cause of disarray of variable they have taken and pari-passu sometimes those variable has been appreciated by the authorities as the risk underlies unlimited downside and limited upside so the predominating role of credit rating agencies are to give precaution that some necessary steps has not been taken as they are essential to maintain creditworthiness and thrust as happened recently Doing their jobs CRA s assess all type of risk underling in the economy, as a simple definition risk can arise by simply doing a single transaction where two party involve and doing a meaningful transaction the possibilities that one of them may become default is the underlying risk. With a single transaction more than one of the risks arises including Liquidity risk, market risk and interest rate risk and mostly credit risk or default risk. Further, a risk may be so Sevier that may have greater possibility of counterparty risk or reputation risk To pricing the credit risk the key rating agencies are Fitch, Moody and S&P are among the top who continuously guide for the investment strategy, to estimate credit risk usually those rating agency assess risk by using quantitative model which have their own drawbacks and unable to depict a qualitative changes as not capture by quantities models, as for the banking risk assessment credit rating agencies have to rely on 5C s which are to assess credit qualities are: i. character (reputation), ii. Capital (leverage), iii. Capacity (earning volatility), Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 13

4 iv. Collateral and; v. cycle (macroeconomic) condition. Usually risk assessment are done by human due to which some inconsistent and biasness lies in the model for the assessment. However, artificial neural networks 1 have been introduced for evaluation to expert system and consistency and biasness which is nothing but a historical repayment experience and default data. Role of CRA s in current crisis In the era of advance financial system role of credit rating agency are essential and become Table- 1: Rating Scale S&P Moody Fitch Investment Grade Speculation Grade AAA Aaa AAA AA+, AA- Aa1, Aa3 AA+,AA- A+, A- A1, A3 A+, A- BBB+, BBB- Baa1, Baa3 BBB+, BBB- BB+, BB- Ba1, Ba3 BB+, BB- CCC+, CCC- Caa1, Caa3 CCC, C Default/Distressed SD & D Ca, C DDD, D dominating as they are responsible to check the performance of individual, corporate or country on the basis of their worthiness and past historical data, but they also provide an opinion of credit quality of borrower on the basis of past performance using some quantitative parameters, including rations and other economic parameters as well qualitative parameters which are vital to create liquidity and raise capital through capital market or information for the purpose of investment in the economy After the global meltdown in September, 2007 most banks in the matured economies have been failed and not able to survive in the crisis in U.S and Europe. However, it seems that those banking having huge exposure in off balance sheet items and their derivative product are nontransparent due to which these instrument and products was Questionable, including role of credit rating agencies, which was criticize that they have done their job improperly or they do just provide rating and earn handsome money why spreading risk across the sector, most of the big banks collapse having huge exposure in sophisticated financial derivative product (e.g. credit default swaps, collateral, risk bonds, mortgage backed securities etc.) which are risky product. 1 Kim and Scott (1991) use a supervised artificial neural network to predict bankruptcy in a sample of 190 Consultant firms. Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 14

5 Rating agencies are considered to be an essential element of development and functioning of capital market, the data shows that top there rating agencies of the US account almost 90 percent market share of which S&P and Moody s accounts almost 80% of U.S market shares while Fitch the largest third one have largest share in the European market. So, role of CRA s are become critical and to criteria to assigned the rating is also questionable for the validation of the parameter (financial and management parameter, ratio analysis) According to Ministry of Finance in India the Credit rating has been define as an opinion on the creditworthiness or the relative degree of risk of timely payment of interest and principal on a debt instrument. The ratings are a comment on the relative likelihood of default in comparison to other rated instruments. In other words, a rating indicates the probability of default of the rated instrument and therefore provides a benchmark for measuring and pricing credit risk The Rating nomenclature The credit rating agencies used various nomenclature and definition for the purpose to assigned rating to bring the transparency or credit worthiness of the corporate and sovereign government to identify the underlying risk in the generally CRA s used following nomenclature for the study Z-Score (Altman, 2000) which used 5-ration to predict the bankruptcy and different weighted are assigned on these ration as to predict a probability of default these ration are Working Capital/Total Assets, Retain Earning/Total Assets, Market Value of Equity/Book Value of total liabilities, Sales/Total Assets Value, Earning before Interest and taxes/total Assets and other more variable are used by credit rating agencies. Structure Model used to define the default based on distant to default and include combination of assets value, debt and deviation of the fluctuation of the assets value and point of default calculated on the basis of where debt exceed the value of assets, the standard equitation is given below Where in the above equation E stands market value of the firm s equity, F is face value of debt, and r is risk free rate of interest, N stand cumulative standard normal distribution function on the basis of daily volatility return and correlation are calculated Credit Metrics (Finger &Bhatia,1997) based on the transitional matrix and which deal with historical data on the basis the value are obtain and some probability were assigned on the basis weighting of probability are provided on the basis of default value and historical data of which measured by using VaR (value at risk ) method on the basis of variance and standard deviation of portfolio Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 15

6 How security migrate from one rating to another is defined in the below chart and the probability of migration were assigned on the basis of their probability of default; can be best understood suppose current rating of government securities are rated as a BBB and after one year what would be its rating or it will be rated as BBB?. Could be understood from migration chart (below), where its probability to remain BBB is 86.93% and to move AAA is 0.02% or to rated D is 0.18%; shows the different type of migration of individual security. If we drawn the probability of the different type of migration of securities in the Normal curve we would conclude the below graph of normal curve which shows the underlying of different type of rating shown in the normal curve. As right side show the higher return and improvement in quality of security while Left tail shows worsening of credit quality of which underlying risk rises and threat occur for further worsening of credit quality of securities or portfolio of the Asset and different possibilities of migration of portfolios which are further helpful to manage for deterioration of credit ratings. * The above two figure are taken from JP Morgan credit metric, Technical Document due to lack exact figures, and described that how rating migration occurred with the time period under migration analysis, it s a simple model. However, In the case of more than two securities are available than migration analysis become complex process and required joint probability distribution Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 16

7 Type of risk modeled Market risk are separately defined from credit risk however both risk are often looks same and approach to differentiate both risk are need more works as market risk are clearly adverse movement of price, and measure by using volatility or sensitivity known as VaR (value at risk) for which was computated on the basis of historical data of past event and compared with the actual date which further help to update model as new variable were identified and these model become more effective to capture the underlying risk which are used by CRA s, Banks particularly Risk Metric deal with the those models known as a Risk metric or Credit risk metric which used to estimate portfolio risk due to any default occurred or any event occurred due to uncertainty in the portfolio in the risk horizon cause by possibility of obligor credit quality changes- both up (down) grades. It is very dramatic to change credit quality of customers as they transfer from one rating to another (both side up (down)) and risk increases as time horizon also increases which shows underlying inherent risk associated with the securities, bonds, etc. Risk metric/credit risk metrics is a model which use to assess the portfolio risk due to change of the debt value due to any default or deterioration of credit quality. Here we are addressing only downside risk which we need to address because if create the sever problem as one become default the risk spread and pose threat to the other similar and non-similar securities, and lies more far from normal curve (see, figure-2), as left tail shows that typical losses and mid of the normal curve called z-score shows typical markets return which have fundamental difference when modeling, that is Equity prices risk are normal distributed and relatively symmetric with the value of N~ 0,1, as known mean and sd. (standard deviation ), and credit risk are highly skewed and fat-tailed so we need to study mean and sd. to understand a credit portfolio distribution (Normal curve, see figure below) a long downside tail of the distribution of credit returns is caused by defaults, the another difference is to lack of data makes it difficult to estimate any type of credit correlation directly from history. To study the historical default one we need to study unexpected losses which is due to volatility of loss and usually become difficult to estimate more than expected losses, also which is shown how to estimates volatility there are two approaches one to study the historical default volatility Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 17

8 and, secondly volatility of holding period return. Further, this default have been assigned a number or label as did for the purpose of rating categories for the independent quantitative method assigned by credit rating and on the basis of likelihood changes of the quality of the obligor may change risk horizon and assigned a different value either side of upgrading or downgrading known as a credit distress (i.e. default rate which calculated on the taking weighted by obligor rather than weighted by number of issues) which gives possible effect to the credit rating migration analysis as one securities face change in their value due to change in default or credit quality and migrated either side due to underlying risk associated with them Pricing of credit risky Instrument For the credit risk modeling there are two approaches to capture risk these are (i) the structural (Merton, 1974) and (ii) reduced form (Jarrow and Turnbull, 1995); the first approach address the default to assets of the firms as the firms value of asset is less the its debt in a simple capital structure and second approach address to the pricing the credit derivative off different term structure of interest rate and credit class on the basis of conditional probabilities and no default occurs prior to time t-approximately Exposures Value at Risk due to Credit Correlations User Portfolios Credit Rating Seniority Credit Rating Series, Equities Spreads series Market Volatilities Rating migration likelihoods Recovery rate Present value Models (e.g., In default bond revaluation correlations) Exposure distributions Standard Deviation of value due to credit quality changes for a Joint credit rating single exposures changes Portfolio Value at Risk due to Credit Source: JP Morgan credit metric Technical Document Even it seems that all CRA s adopt similar methodology and use almost common parameters including top three rating agencies i.e. Fitch, S&P, and Moody, what is common with three that they use common parameter as mention in the chart-3, however it was observed that Fitch and S&P emphasis more on debt Burden and Liquidity Management (i.e. more parameter are taken of debt and liquidity ratio as compared to moody s debt profile), whereas moody s has assigned equal weight for the debt profile equivalent to Governance and Management policies which seem if the government policies are not very effective and efficient in the case of Reforms and transparent index 2 if will marks as a questionable to sovereign rating and downgrading become more possible as happen recently for India. Moody s corporation gives equal weight to Debt profile of the country and Governance and Management policy taking as a two different variable and third one which have given more Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 18

9 value is Institutional Framework which include further Predictability, stability and responsiveness including fiscal adequacy which seems difficulty for Indian government recently and moody has downgraded Indian sovereign rating and it was hugely criticized and compared with the other European Countries commonly known as a PIIGS Economy as it was argued that CRA s neglect growth part of the Indian economy and it s one of the fastest growing economy among the world due to which rating of Indian economy should be downgraded and form stable to unstable while debt suffering countries in the Europe was assigned stable rating. this paper suggest that stable rating was assigned to debt suffering countries in the advance or mature economy due to their operating environment and economic fundamentals what studies suggest it seems that apart from those variables moody s assigned highest weighing to these parameters which almost account 70% or weighting for the index for the developing countries including GDP per capita, GDP volatility, Government Effectiveness Index. However, S&P another top notch rating agency include another important parameter off balance sheet liabilities as a separate variable and predictability beside those variable what studies found that S&P also use a political risk factor as a separate variable to predict the rating. The studies observed that the variable predictability are associated to reforms and futuristic action and become the crucial variable in the meantime of downward of business cycle or economic activity due to lacuna in policies, government failure to manage downgraded, institutional supports, and non-transparency in decision process which affect the creditworthiness and performance of the sovereign government and as Indian is also get affected as recently India s central bank governor that Indian should prepare for the further even its ability to cope with any adverse economic downturn and crisis which shown due to high volatility in some of the vulnerability index of which they are external and internal variable. The methodology used by rating agencies are not static but more dynamic variable such a offbudget liabilities of special purpose vehicles, contingent exposure are given more weight as compared to traditional rating, recalibration of debt has been given more weight by Fitch whereas S&P has introduced the valuation of derivative instrument to address sovereign risk; which shifted more towards balance-sheet approach from macro management approach They are the prime reasons and due to assigned adverse rating for Indian sovereign as compared to other matured economies even despite that India is one of the fastest growing economies and while S&P argued that why India s rating is low even then compared to debt facing countries in the Europe the argument was given by S&P that their income and economic structure are better than compare to emerging economies and even they have larger financial market including debt market which emerging economies still do not have. As paper mention above that economy and system support predictability has highest weighting in the Index of CRA Financial Express on dated Aug, Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 19

10 CRA s assessing to sovereign risk they not only consider the quantitative data and tools but mostly they also account qualitative tools and it looks for finalizing rating by rating committee. Conclusion The studies has conclude that role of rating agencies are essential for the financial market and provide low cost of capital and easily access of funds by passing on information of asymmetry and public data to on the basis of which they assigned rating for the risky instruments or credibility of sovereign economy. The rating are assigned after typical studies rather than individual and lead analyst were assigned who cover all aspect and studies regarding underlying risk which are capture both i.e. quantitative and model based approach beside this the other important aspect to provide rating qualitative factor and institutional strength such as political risk, government stability, and economic environment. Qualitative data are vital for the sovereign rating and data used for the qualitative date is usually not possible due to lack of transparency and lack of accuracy of and qualities are questionable for the developing countries Further, to raise capital or debt form the domestic capital market in the developing countries at beginning of the development path become more complex in the developing nations due to lack of infrastructure and market as compared to developed countries due to which they usually assigned low rating grade as compared to mature or developed economics. The credibility also affected due to political factor; domestic miss-management; lack of fiscal and financial strength of institution which are qualitative factors and address to the uncertainties and risk underlying in the future Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 20

11 Annexure-I, Defining Rating Variable S.No Moody s Fitch Standard & Poor s 1 Operating Profit Institutional and Administrative Economy 2 Institutional framework Economic and Social Profile System support & & responsibility Predictability 3 Financial Position Fiscal & Budgetary Performance Management and and Performance Institutional Legitimacy 4 Debt Profile Debt, Liquidity and Indirect Risk Financial Flexibility 5 Governance Management Budgetary Performance & Management Practices 6 Economic fundamentals --- Liquidity and Debt Management 7 Debt Burden 8 Off Balance sheet Liabilities 9 Political Risk Source: S&P (2009), Moody (2008a), and Fitch (2008a) Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 21

12 Bibliography 2000, Ammer J& Packer. How Consistent are Credit Ratings? A Geographic and Sectoral Analysis of Default risk, Board of governor of the Federal Reserve System August, Lili Liu, Kim Song Tan. Sub national Credit Ratings, policy research Working paper-5013, The World Bank Group Feb, Mai Hassan& Christian, Regulation of Credit Rating Agencies, Working Paper No-26, The German University in Cairo 2011, Gunther Tichy, Intereconomics, Credit Rating Agencies: Part of the Solution or Part of the Problem? A Forum Jan, 2001, Rangrajan K. Sundaram,The Merton/KMV Approach to pricing Credit Risk 30 Aug, 2012, New Delhi, India need to prepare for credit downgrade, The Financial Express 2008a. International Rating Methodology for Regional and Local governments, Criteria Report, July 8. Jan, Credit Rating Agencies and Their Potential Impact on Developing Countries Discussion paper no-186, UNCTD April 2, The Benchmark for understanding Credit Risk, Technical Documents J.P.Morgan Chase & Co. 5 May, 2012, New Delhi, S&P ratings of Spain, Italy 2 notches higher than India, The Financial Express 31 May, 2012, New Delhi, Don t get moody about S&P, The Financial Express December, Report of the committee on Comprehensive Regulation for Credit Rating Agencies, Ministry of Finance, Capital Market Division, India December 2011, Allen &Powell, credit risk rating methodology, School of Accounting, Finance and Economics, Edith Cowan University September 2010, Bo Becker and Todd Milbourn, How did increased competition affect Credit Rating?, working paper , Harvard Business School Feb 2006, Special Policy: Credit Policy, Basel-II: Refinements to the Framework, Fitch Rating October 2002, Bhatia & Lin. Sovereign credit rating methodology: An Evaluation Treasury Department, IMF Working paper series Aug 2000, Credit rating and complementary source of credit quality of information, Working paper, Basel committee of banking supervision March 2011, Deb. Mark &Toth, whether credits rating industries? Financial Stability Paper, Bank of England June 2011, Hilscher & Wilson. Credit rating and credit risk, International Business School, Brandies University, USA June 2009, Efraim & Jennifer. The Credit Rating Crisis, National Bureau of Economic Research, NBER Paper Series Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 22

13 Jan 2001, Consultive Document, The Internal Rating Based Approach, Basel Committee of Banking Supervision Web Link Resources Rajeev Rana, Prof. V.A. Bourai, Prof. R.R. Nautiyal volume 4 issue 9 SEP 2018 Page 23