SIFI Modern Banking Credit Risk Manualconform to Basel Instructions
Author
Jamila Awad
Rights Reserved
JAW Group
Date
April 2013
Executive Summary
The omnipresent credit riskinstability in global financial markets poses a hazardous threat to the health of the economic system and deflates depositor confidence. The modern banking system modelsdefault exposuresin accordancewith two reference frameworks entitled CreditMetrics and CreditRisk+. The research paperstrives to engineer an enhancedmodern banking credit risk manual that migrates the above stated default peril protocols into theBasel management principles. The dissertation is partitioned in four sections. The first three sections cement the mathematical and qualitative arrangements to execute the pioneered debt uncertainty modeling manual. The closing component addresses the integration of the Basel principles of credit risk management into the designed code. In brief, inflamed effects from scarce debt plunge administration shall fade with the enforcement of a homogenous credit peril mapping.
Introduction
The spillover and contagion effects arise when global financial markets scarcely administer credit risk management. The modern banking industry designsdefault uncertainty foundations in compliance with strict regulations. The two widespread protocols utilized by financial institutions to schematizedebt defaults are the CreditRisk+ and the CreditMetrics. The frameworks differ in scope and in language however voice a common objective; estimate regulatory capital compelled to buffer exposure incurred through contractual obligations. In addition, the two frames of reference impose dissimilar restrictions and distributional assumptions. They also establish divergent calibration methods. Therefore, an identical portfolio of credit exposure can yield disparate credit risk evaluations. Finally, the determination of parameters at model inception enhances the comprehension of the sensitivity of the model to the specified employed assumptions.
The ever-present uncertainty in global financial markets necessitate a prototype that efficiently merges credit risk modus operandi to guard an international homogenous debt peril mapping that can curtain modeling bias and be implemented without frontier tailoring. Therefore, the rédigé aspires to pioneer aSIFI («Systematically Important Financial Institution») modern banking credit risk manual conform to Basel instructions. In addition, the proposed exemplary protocol intends to enhance financial stability during stress events, gold chamber investor confidence,smoothen macroeconomic variables effects injected during economic cycles in the credit risk modeling,embrace the globalization of financial markets,safeguard all market participants deposits, and finally, facilitate authority coercion.
The continuous adjustments to credit risk modeling and policies necessitate a convergent jurisprudence prescript that can be supervised by designated authorities. The modern banking industry conforms to the Basel committee directive lines in order to efficiently administer default exposure management. The Basel arrangement for credit risk is segmented in five modules addressing specific directive principles.
The discourse is partitioned in four sections. The inaugurating component presents the CreditRisk+ and the CreditMetrics general assemblage. The second section establishes the foundations, the mathematical theorems and the qualitative parameters of the pioneered SIFI modern banking credit risk manual. The tierce fragment maps the CreditMetrics model migration into the proposed debt uncertainty administration protocol. Finally, the closing component diffuses the integration of the Basel principles of credit risk management into the designed code.
In brief, SIFI modern banking entities shall continuously modernize default risk administration models to immune the economic system from hazardous threats.
1. Presentation of the Two Credit Risk Frameworks
The CreditMetrics system intends to measure credit portfolio peril from mathematical routes initiated with an ordered probit regime. The first step is initiated with the examination of portfolios weighted to their respective scenario probabilities to enable the charting of the loss distribution by assuming a linkage between changes in credit ratings to the movements in firm’s asset values. The second step assembles the correlation matrix that is derived from the firms’ asset prices across the industries of individual countries. The third step generates the standard deviation from the distribution of portfolio losses and selects the 1% quantile to diagnose the necessary economic capital. The final step launches analytical simulations via Cholesky factorization to address the multivariate normal distribution from the joint distribution of returns. Afterwards, the CreditMetrics approach schematizes changes in credit ratings, debtor correlations and recovery rates to finally simulate the distribution loss with Monte Carlo computation. The scenario simulation step slightly increases impreciseness in economic capital estimation due to the broad inputs to parameterize the model such as credit quality migration likelihoods, credit spreads, credit default probabilities and recovery rates. On the other hand, the CreditMetrics presents an attractive prescript when risk managers wish to consider credit rating changes as credit events. Furthermore, the frame also incorporates correlations from the multivariate underlying parameterization.
The CreditRisk+ actuarial minding also aims to calculate the economic capital required to buffer the entity’s credit risk compliance. It models default risk under the assumption of independency between the entity’s capital structure and the credit peril therefore deflating the risk of modeling error. In addition, the CreditRisk+ relies on minimal input initiation to parameterize the rational which in return enhances the flexibility of the model to analyze the marginal effects of incorporating additional instruments to massively sized fixed-income portfolios. It also diffuses scenario analysis into the loss distribution chart to consider major economic downturns of highly improbable events in financial markets. On the other hand, the methodology intentionally omits to examine the correlation between individual sectors therefore presents a model weakness. The following description summarizes the CreditRisk+ arrangement: The credit portfolio is divided into tranches of sectors whereas each sector comprises loans exposing similar risk. Afterwards, the portfolio loss distribution is charted by multiplying the individual sectors’ loss distributions under the supposition that they are mutually exclusive. Finally, various annual credit provisions are estimated from the distribution of losses at 99th percentile. The following presents strengths of the CreditRisk+ protocol: the light input data at modeling inception and the ability to schematize magnified volume portfolios with a distribution of losses. The downside of the CreditRisk+ code derives from disregarding the examination of correlations between listed debtors in the distribution of losses.
2. Foundations of the Pioneered SIFI Modern Banking Credit Risk Manual
The SIFI modern banking credit risk manual is engineered from theCreditRisk+ mathematical regimeand scenario analysis united to the CreditMetrics protocol. The approach underlying the risk of financial stress management aims to quantify provisions to be chambered for credit risk and to measure optimal diversification methodologies in investment portfolio administration.
The proposed model relies on a portfolio practice that incorporates data from various domains: the magnitude as well as the maturity of an exposure, the credit quality of an obligor and finally the inherent financial systematic threat. The statistical procedure is initiated on the basis of absence of assumption about the causes of default which enables to eliminate speculations about price movement drivers at inception. Default rates are presented as uninterrupted random variables following stochastic properties. The practice also injects the volatility of default rates into the model to capture their uncertainty. Finally, the forward-looking analysis intends to brainstorm highly unpredictable events that secret catastrophic consequences and that are oftenunraveled by mathematical theorems.In brief, the designed protocol strives to schematize a full loss distribution model to evaluate the credit risk of a portfolio.
The economic capital demanded by regulatory authorities is obtained from the likelihood of unexpected losses of debt plunge in a sample portfolio. The retained provisions aspire to ensure effective risk management and shield the entity from hazardous downturns following financial market collapses.
Model engineering hinders the following uncertainty parameters: model deficiencies, factors that cannot be determined and finally processesthat can plunge. The model deficiencies derive from models that fail to image the actual underlying procedure therefore generating inconclusive results. The unpredictable factors are noticed when the model loops inaccurate estimates about factors that are supposed to describe the model. The unpredictable factors are then tracked through sensitivity analysis such as stress tests to reduce their uncertainty. Lastly, the process plunge results from random fluctuations during the model factor estimates. Therefore, modeling credit risk with statistical high confidence significance levels vaporizes the process plunge uncertainty parameters to tolerated variances. Therefore, uncertainty parameters shall be monitored during the entire modeling operation to adjust the inputs, monitor downsides of erroneous model designing and finally secure accurate parameter estimates. On the other hand, the absence of assumptions at analytical initiation about the causes of defaultstrengthens the validation of the model in presence of uncertainty parameters. Furthermore, the minimal input requirement at model inception shrinks as well errors that occur from uncertainty parameters. The light data alsodelivers a snapshot of the credit environment where empirical history is scattered and continuously fluctuating.
2.1 The Types of Credit Risk
The pervasive credit default spread in financial markets emerges from credit disparities where portfolios are marked-to-market thus shifting value. Various accounting policies such as accrual basis shall therefore be translated into marked-to-market to adequately framework credit risk management for all financial instruments contained in the portfolio. In precise terms, financial instruments are treated equally regardless their position in the trading or banking book to enable the calculation of credit risk capital.
In other words, the credit risk spread represents the risk of financial drop in value following changes in daily credit gaps that affect the portfolio. Therefore, investors inquire compensation in form of excess return to bear credit risk spread. The entity’s value-at-risk (VaR) model merged to a historical simulation approach shall therefore adequately consider credit risk spread to estimate an accurate economic credit capital.
The credit default risk arises from portfolios exposing the probability of a debtor failing to meet contractual obligations. In consequence, the entity absorbs a loss that is equivalent to the sum indebted by the obligor less the recovery rate retained by the entity following a defaulted debtor. The main credit sectors such as fixed income and loan markets shall be adjusted with their positions in the portfolio as well as with mark-to-market changes to properly integrate liquidity differences. Hence, the credit default risk is also considered an inherent element in global markets.
2.2 The Default Rate Behavior
The credit risk model shall also incorporate default rate behavior such as credit quality changes to portray forward-looking projectionabout all market participants. Default rates obtained from market movements can therefore be sequenced as an uninterrupted random variable during credit modeling design to evaluate future outcomes in determined time horizons.
The strategy to diagnose default rates can be initiated from two distinct treatments: a continuous variable or a discrete variable. The procedure when the default rate is schematized as a continuous variable enables to trace a distribution of forward debt prices and their volatilities from future projections of obligation prices. The regime when the default rate is defined as a discrete variable intends to assign credit ratings to debts and then to sketch the default rates to credit ratings. The prospective outcomes of default rates derive from a rating transitional matrix that enumerates the shifting probabilities of default rates. The proposed procédéaims to default rates using the continuous variable route to migrate default rate probabilities and capture the unpredictability in default rate levels.
2.3 The Risk Measures
The risk measures utilized in credit risk management are drawn from the portfolio’s distribution of losses that is traced with a high confidence level and from the scenario analysis that incorporates the consequences of catastrophic events into the credit risk model.
The portfolio mode to managing credit risk is launched from the diversification theory that prescribes to retain various categories of financial instruments and thus reduce overall portfolio exposure. The main technique requires controlling credit risk with limit instructions for four parameters: debt sizing, maximum maturity projections, obligation rating exposures and lastlydebtors’ concentration risk boundaries. A well-diversified portfolio demands less economic capital thus emphasizes the importance of adequate risk management in long-term investment prudential minding. In precise terms, the technique focuses on the scale and the scope of obligations and debtors when mapping with limit instructions.
2.4 The Modeling Technique
The economic peril of a portfolio of credit venture can deteriorate when the sum of individual debt risk hindering low probability of occurrence are held in the portfolio. Therefore, sound credit risk management shall examine the severity of individual losses and the frequency of highly unpredictable events to plot the loss distribution curve. The modeling technique therefore relies on the absence of assumptions about the causes of default at the inception stage and then schematizes default losses to finally enable an analytical tractable approach.
2.5 The time horizon
Two possible time horizon instructions are recommended to design the credit risk model: a constant time horizon such as one year or a hold-to-maturity projection.
The time horizon alternative shall reflect the time frame necessary to mitigate risk arising from holding financial instruments in the portfolio. The constant time horizon aims to assemble all exposures in the portfolio to an identical future date and to match the normal accounting period stated as one year.Thehold-to-maturity projection recognizes the run-off time horizon of default rates. Therefore, this route facilitates comparison of maturity and credit quality ranges.
2.6 The Data Input
The suggested protocol shall incorporate the following inputs to model credit risk: debt exposure, debtor’s default rates, debtor’s default rate volatilities, and finally, the recovery rates. In addition, default data that can ameliorate the assessment of the portfolio credit risk management can also be retained as long as the parameters can be justified, monitored and tracked in a forward-looking basis.
The debt exposure resulting from segmented transactions with obligors shall be combined according to the legal corporate structure. However, all individual transaction possibilities relying on various types of instruments shall be individually evaluated before inception to adequately determine the customized probability of default. In the case of a multi-year time horizon, the changing debt peril over the passage of time shall also be considered in the conceptualization of credit exposure.
The debtor’s default rates shall be attributed to each debtor through the following strategies and shall consider changes deriving from economic cycles: credit spreads from trading instruments, the debtor’s credit ratings chart with the estimated default rates and with the default probabilities on a continuous measurementbasis.
The debtor’s default rate volatilities are defined as standard deviations arising from averages of default rates and shall encrypt fluctuations of credit ratings’ due to the economic cycles.
The recovery rates permit the entity to retain sums following an obligor’s default and they take into account the maturity stage of the debt as well as other collateral held by the entity. The recovery rates can demonstrate significant variation ranges derived from average standard deviations. Thus, the recovery rates shall therefore be examined with stress tests.
2.7 The Correlation of Background Factors
The pioneered model recommendsstudying the effects of default correlation arising from a portfolio of debt peril by uniting default rate probabilities and sector analysis.
Background factors occur when the random nature of defaults plays a contributing role in the incidence of correlated default rates whereas the default rates do not demonstrate casual interlinkages. Therefore, the prediction sequence of credit defaults becomes undetermined.
2.8Incorporating the Effects of Background Factors
The background factors are modeled by utilizing default rate volatilities that generate increased defaults from background factors as an input into a loss distribution chart. Scenario analysis is however necessary to ameliorate the analytical approach of modeling default rates. Furthermore, the absence of significant empirical data on default correlations calls for a proxy technique deriving from asset price correlations to plot default correlations. However, assumptions about the linkage between asset prices and probabilities of default shall be stated before the inception of the proxy technique to adequately assess the effects of background factors.
2.9The Impact of Macroeconomic Factors on Default Rates
Global financial markets are sound evidence that macroeconomic factors play a heavy role on credit default rates. Furthermore, industry sectors adapt to economic shifts depending on their sensitivity to macroeconomic stabilizers and their liaison with the debtor’s revenues. Banking regulations tolerate in-house models that adequately address the impact of macroeconomic movements and business cycles to chart default rate variability. However, the in-house model must be engineered with the following guidelines: stating the underlying assumptions, projecting a forward-looking process, establishing selected parameters to sketch the model, implementing a coercion agenda, and finally, elaborating the strengths as well as the weaknesses of the in-house model.