Theory and practice of credit risk modelling pdf

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theory and practice of credit risk modelling pdf

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This study is designed to shed light on the current practices of these firms. A short questionnaire, containing seven questions, was mailed to each of the top banking firms headquartered in the USA. Close to half of the responding institutions utilize models that are also capable of dealing with counterparty migration risk. The results help one to understand the current practices of these firms.

Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory

To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy. Log In Sign Up. Download Free PDF. Download PDF. A short summary of this paper. IntroductionBanking institutions encounter two broad types of risks in their everyday businesscredit risk and market risk.

Credit risk may be defined as the risk that borrowers might default on their obligations, whereas market risk reflects the variability in the value of their financial position due to changes in interest rates, exchange rates, etc. Over the last decade, rapid strides have been made in developing Value at Risk VaR models for managing market risks in a portfolio context.

Such models have also been recognized for regulatory capital setting for market risks. However, a similar approach to measure credit risk in a portfolio context was found difficult on account of certain crucial differences between credit risk and market risk. While market rates change from one second to the next, credit events are rare, and hence, the amount of credit data available is much smaller.

Also, whereas the historic data necessary to calculate market rate correlations are readily available, correlations in credit quality cannot be readily observed and may have to be inferred from other sources like equity prices. In the last few years, credit risk models, which attempt to measure risk in a "Portfolio" context, and compute VaR due to credit, have emerged in the market.

While significant hurdles, especially relating to data limitation and model validation, still need to be addressed before a VaR type model for credit risk can be accepted as an alternative to the standardized approach to the measurement of capital, such modeling techniques have caught considerable attention amongst the community of bankers and banking supervisors.

The primary objective of credit risk models is to treat credit risk on a "Portfolio" a basis to address issues, such as, qualifying aggregate credit risk, identifying concentration risk, quantifying marginal risk, i. The traditional techniques for managing credit risk, the use of limits. While the limit system takes care of the various factors, which contribute to the magnitude of credit risk, viz.

Concentration risk refers to additional portfolio risk resulting from increased exposure to one borrower or groups of correlated borrowers. However, such limits tend to be arbitrary in nature. Credit risk models have the potential to address concentration risk in a more systematic manner as they provide risk estimates, which give an idea of the 'relative riskiness' of the various exposures in a portfolio.

Further, a portfolio view of credit risk facilitates a rational assessment of portfolio diversification. For example, the decision to take an ever higher exposure to a borrower will result in higher marginal risk, which will increase exponentially with increasing exposure to the borrower.

On the other hand, a similar additional exposure to another borrower, although having a higher absolute risk, offers a relatively small marginal contribution to the overall portfolio risk due to diversification benefits. Perhaps, the most significant objective, which the output of a credit risk model can address is in the estimation of the amount of capital needed to support a bank's credit risk, termed as 'economic capital' it is now a well-recognized fact, that the current riskbased capital standards for banks established by the Basle Accord have significant shortcomings inasmuch as the quantum of capital arrived at under the standard may not be a true measure of the riskiness of a bank's business.

A notable weakness under the current regime is that the risk-weighted assets in the denominator of the capital ratio may not represent the true risk, as all commercial credits are assigned percent risk weight, and therefore, the methodology ignores the crucial difference in credit risk among different borrowers.

The methodology also ignores the effect of portfolio diversification on credit risk. In addition the current risk-based capital standards have provided incentives to banks to indulge in regulatory capital arbitragea prime example is the use of asset securitization by banks in the United States to achieve significant reduction in capital requirements without materially reducing the credit risk in their books although this is not relevant in the Indian context as the securitization market is yet to take off.

Credit risk models facilitate computing a measure of economic capital reflecting more closely the perceived riskiness of the underlying assets of an institution. Types of Credit Risk ModelsEssentially, credit risk models can be classified into two types based on the definition of credit loss. First, Default Mode DM models, also called as "two-state" models, recognize credit loss only if a borrower defaults within the planning horizon, i.

Such models are useful in situations, where secondary loan markets are not sufficiently developed to support a full mark-to-market approach.

Second, Mark-tomarket MTM models, also called "multistate" models, recognize that 'default' is the only one of the several possible credit rating grades to which the instrument could migrate over the planning horizon.

Therefore, a credit loss under the MTM paradigm is defined as an unexpected reduction in portfolio value over the planning horizon due to either deterioration in credit ratings on the underlying loans or a widening of credit risk spreads in financial markets.

Choice of Planning HorizonAlong with deciding on the conceptual definition of credit loss, a bank has to choose a time horizon over which to measure this loss. Generally, a constant time horizon, such as, one-year or a hold-tomaturity time horizon under which each facility is assessed according to its maturity is chosen.

Internal Credit Rating and Transition MatricesA reliable internal credit rating system is a key component needed to implement a credit risk model, as the probability of a credit facility defaulting within the planning horizon is determined solely on the basis of its internal rating. Another component required is a 'rating transition matrix', which indicates the probability of a customer migrating from the current rating category to any other category within the time horizon.

A sample one-year transition matrix showing the credit rating one year in the future is shown in the Loan ValuationThe current and future values of each credit instrument at the beginning and end of the planning horizon have to be computed under both DM and MTM models.

In the DM model, the current value of a loan is its book value and the future value depends on whether or not the borrower defaults during the planning horizon. If the borrower does not default, the future value would be the book value at the end of the planning horizon, after adding back the interest and principal payments received during the planning horizon.

The future value of a defaulting loan would be the recovery rate measured as the loan's book value multiplied by 1-its loss rate given default. Computation of loss rate give defaults LGDs is a difficult task. Banks computeLGDs from a variety of sources which include: a internal data on the bank's own LGD, wherever available, b loss rate from external reports like publicly available regulatory reports, c intuitive judgments of experienced lending officers, etc.

In respect of many types of credit instruments, a bank's exposure is not known with certainty, but will depend on the occurrence of future random events. In respect of a committed line of credit, for example, the customer's drawdown rate would tend to increase as his credit quality deteriorates, reflecting the higher costs of alternative sources of funds. Credit risk models treat such 'credit related optionality' associated with a line of credit by treating the draw-dawn rate as a known function of the customer's end-of-period credit rating.

For example, consider a oneyear line of credit that is initially undrawn. Then, depending on the customer's credit grade at the end of the planning horizon, assumed end-of-period draw-down rate would be based on the average historical draw-down experience of customers having that future grade.

In the DM framework, the undrawn facility is converted into a loan equivalent exposure LEE to make it comparable to a term loan. The LEE is calculated as the expected draw-down under the line in the event the customer becomes insolvent by the end of the period if the customer remains solvent, the size of the draw-down is irrelevant in a DM model, as credit losses would be Zero.

Credit Events CorrelationAfter determining the current and future values of each credit instrument, the next step is to consider the correlation among the factors determining credit-related losses.

According to modern portfolio theory, portfolio credit risk is not just the summation of the credit risk of the individual credit instruments comprising the portfolio, there is also an element of system risk on account of joint movements in loan values arising from their dependence on common influences.

Under DM models, of course, credit spreads are irrelevant. For example, it is well known that the fortunes of the tyre industry are linked to that of the automobile industry. Therefore, a rating downgrade of an exposure in the automobile sector is very likely to trigger off a similar downgrade amongst borrowers in the tyre industry.

However, while bankers are well aware of such correlation, its quantification is difficult in practice. Quantification of such correlations is the most challenging and the least evolved area in credit risk modeling.

Thus, a customer might be assumed to default if the underlying value of its assets falls below some threshold, such as, the level of his liabilities. For MTM models, the change in the value of a customer's assets in relation to the various thresholds is often assumed to determine the change in its risk rating over the planning horizon. Probability Density FunctionOnce all the parameters are specified as described in the above paragraphs, the credit risk model can be used to quantify credit risk through a concept called 'probability density function PDF ' of credit losses over the chosen time horizon.

The concept of PDF and the process of setting economic capital using the same is explained with the help of the graph below: Graph 1: PDF and Economic CapitalWhile a standard shape of PDF is yet to emerge unlike in the case of market risk models where the normal distribution has evolved as the standard , observed portfolio credit loss distributions are typically skewed towards large losses as shown in the graphthe PDF of a risky portfolio has a relatively long and fat tail.

An important property of the PDF is that the probability of credit losses exceeding a given amount X as shown in the graph is equal to the shared area under the PDF to the right of X. The amount of economic capital depends on the target credit loss quintile chosen. Due to the long-tailed nature of distribution of credit losses, a target quintile in the range of Thus, if the confidence interval is set at The overall portfolio risk under the method is summarized as follows:The portfolio expected credit loss u over the chosen time horizon equals the sum of the expected losses for the individual credit facilities:Where, for the i facility, X LossesLGD i is the expected loss rate given default.

Further, the stand-alone standard deviation of credit losses for the i facility can be expressed as:Where VOL is the standard deviation of the facility's LGD.

Monte Carlo simulation technique, on the other hand, characterizes the full distribution of portfolio losses. However, it is computationally burdensome and can take several days for execution. The technique, which is used in MTM models, involves generating scenarios, with each scenario corresponding to a possible credit rating of each of the obligors in the portfolio. For each scenario, the portfolio is revaluated to reflect the new credit ratings. Thus, a large number of possible future portfolio values are generated, with which the distribution of portfolio values is estimated.

Thereafter, a target insolvency rate is chosen and the corresponding quantum of economic capital is computed. ConclusionWhile credit risk models are not a substitute for sound credit appraisal systems, it is now generally accepted that such models have the potential to contribute significantly to enhancing the internal risk management systems in banks. The future scenario in this regard is articulated in the consultative paper issued by the Basel Committee on Banking Supervision on the new capital adequacy framework.

The Committee intends to explore ways in which such models could play an explicit part in the regulatory capital setting process. The Indian banks, especially those which are internationally active, would do well to critically study the credit risk models available in the market the two prominent models for which extensive technical documentation is available are Credit Metrics by J.

Morgan and Credit Risk' by Credit Suisse Financial Products , and prepare themselves for a model-based approach to the measurement of risk capital in the future. While the MTM modeling Table below :belowSample credit rating transition matrix Probability of migrating to another rating within one year as a percentage Credit rating one-year in the future Note: The credit rating transition matrix is based on the historical migration frequencies of publicly rated corporate bonds.

Above the transition matrix indicates, for example, that the likelihood of an AA- rated loan migrating to single A within one year would be 7. For DM models, only the last column would be relevant, as such models recognize only two states, viz. Related Papers.

By chienvm SEx. Does adding up of economic capital for market- and credit risk amount to conservative risk assessment? By Klaus Rheinberger. By Kasper Roszbach. By Tor Jacobson. Internal ratings systems, implied credit risk and the consistency of banks' risk classification policies, Sveriges Riksbank Working Papers No.

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Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory

It's not restricted to retail customers but includes small, medium and big corporate houses. In news, you might have heard of Kingfisher Company became non-performing asset NPA which means the company had not been able to pay dues. High NPAs lead to huge financial losses to the bank which turns to reduction of interest rate on the deposit into banks. Serious honest borrowers with good credit history credit score would have to suffer. Hence it is essential that banks have sufficient capital to protect depositors from risks. As these home loan borrowers had high chance to default, many of the them started defaulting on their loans and banks started seizing foreclose their property.

Credit Risk Pricing Models

The GVAR model is combining by the satellite credit risk equation to find the non-performing loan under stress conditions. The advantage of using GVAR model is that on the one hand, it captures the transmission of global, external and domestic macroeconomic shocks on banks non-performing loans. On the other hand, this model considers the nonlinear pattern between business cycle and the bank credit risk indicator during the extreme events as highlighting by the macro stress test literature. The forecast of non-performing loan is then used to obtain stress projections for capital requirement for the banking system level.

Credit Risk Modelling: A wheel of Risk Management

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Theory and Practice

Imagine that you are a bank and a main part of your daily business is to lend money. If the borrower defaults, you will face losses in your portfolio. Or, in a bit less extreme scenario, if the credit quality of your counterparty deteriorates according to some rating system, the loan will become more risky. These are typical situations in which credit risk manifests itself. According to the Basel Accords, a global regulation framework for financial institutions, credit risk is one of the three fundamental risks a bank or any other regulated financial institution has to face when operating in the markets the two other risks being market risk and operational risk. This course offers you an introduction to credit risk modelling and hedging.

Scientific Research An Academic Publisher. In recent years, Internet finance has been growing rapidly, and electronic banking has taken a larger share of banking services in commercial banks. In , the users of electronic banking in China amount to million, out of million netizens. Compared with traditional banking, this new form of banking, which is free of the need for counters, not only increases market risks, but also has a great impact on the risk measurement of commercial banks. The measurement and management of credit risk is one of the most important research directions in the financial industry. Narrowly speaking, credit risk refers to the risk of debtors failing to make required payments. By the end of , the non-performing loans of commercial banks added up to

Portfolio credit risk models estimate the range of potential losses due to defaults or deteriorations in credit quality. Most of these models perceive default correlation as fully captured by the dependence on a set of common underlying risk factors. In light of empirical evidence, the ability of such a conditional independence framework to accommodate for the occasional default clustering has been questioned repeatedly. Thus, financial institutions have relied on stressed correlations or alternative copulas with more extreme tail dependence. In this paper, we propose a different remedy—augmenting systematic risk factors with a contagious default mechanism which affects the entire universe of credits.

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