Brajar When this asset belongs to a portfolio, however, what matters is the contribution to portfolio risk. The risk factors are represented by time series of prices or levels of stocks, currencies, commodities, and interest rates. Creitmetrics collection of profit loss scenarios provides a sampling of the profit loss distribution from which one can compute the risk measures of choice. Articles needing additional references from June All articles needing additional references Articles with topics creditmetrice unclear notability from April All articles with topics of unclear notability Articles with multiple maintenance issues Use dmy dates from November RiskMetrics assumes that the market is driven by risk factors with observable covariance. These perturbed risk factor price scenarios are used to generate a profit loss distribution for the portfolio.
|Published (Last):||23 March 2006|
|PDF File Size:||7.75 Mb|
|ePub File Size:||9.50 Mb|
|Price:||Free* [*Free Regsitration Required]|
The CreditMetrics data set provides volatility estimates for recovery rates based on historical data. This optionality derives from the fact that a credit loss can occur only if the position is in-the-money at the point at which the counter- party undergoes a change in credit quality. To evaluate this, CreditMetrics considers expected exposures at the risk horizon, which are, in turn, derived from market rates and volatilities.
The necessary expected exposure calculations for market-driven instruments are not performed by CreditManager, but can be performed in and imported from J. If credit risks were normally distributed, the mean and standard devi- ation would be sufficient to specify fully the distribution.
As shown in Chart 1, however, credit risk is clearly not symmetrical. Thus, the standard deviation measure cannot capture the fact that, for instance, the maximum upside might be only one standard deviation above the average, while meaningful occurrences of loss can be many standard deviations below the average.
Con- sequently, there is a need for more information about the distribution to understand risk in credit portfolios. To calcu- late a percentile level the full distribution of portfolio values must be specified. This, requires a potentially lengthy simulation, which is computationally complex. These reports can be expressed in the aggregate, or bro- ken down by country, industry, maturity, rating, product type, or any other category of credit exposure.
Managers are then able to identify different pockets, or concentrations of risk within a single portfolio, or across an entire firm. Moreover, reports can be generated using mean or standard deviation as a risk measure or any chosen percentile level, as well as valuing risk on a marginal or relative basis. Chart 3 is a value-at-risk report showing credit risk broken down by country and maturity using mean exposure. Chart 3 Value-at-risk due to credit To implement CreditMetrics methodology, a software package, CreditManager, has been developed to provide a flexible analysis and reporting tool in a Microsoft Windows NT or Windows 95 environment.
This desktop software provides both sophisticated graphics and statistics to represent the computations and analysis outlined in this and other CreditMetrics documents. Chart 3 is a direct output of Credit- Manager as are many of the charts and tables of this document. With CreditManager users can assess the different credit risks of their firm on an absolute, relative or marginal basis, across the various port- folios, categories, sectors, or product lines using the risk statistics that they feel most appropriate.
The case for a portfolio approach to credit risk 2. The credit market environment As discussed in section 1. Globally, buoyed by the momentum of a healthy business cycle and a relatively benign credit environment, more institutions are taking on an increasing amount of credit risk. Consequently, as credit exposures have multiplied, the need for more-so- phisticated risk management techniques for credit risk has also increased.
Common sense dictates that if businesses are demanding better performance in terms of return on economic capital, management must have a solid grasp of all forms of risks being taken to achieve this. Introduction to CreditMetrics page 10 Of course, more active credit risk management could be achieved by more rigorous enforcement of tra- ditional credit processes such as stringent underwriting standards, limit enforcement, and counterparty monitoring.
Increasingly, however, risk managers are also seeking to quantify and integrate overall credit risk within a benchmark value-at-risk statement capturing exposure to both market and credit risks. Concentration risk refers to additional portfolio risk resulting from increased exposure to one obligor or groups of correlated ob- ligors perhaps in a particular industry or location.
Concentration risk can be mitigated only by diver- sification or transactions that hedge the specific risk of the concentrated exposure. Such a model creates a framework to consider and stress-test concentrations along almost any dimension by industry sector, rating category, country or instrument type.
Intuitive — but arbitrary — exposure-based credit limits have been the primary defense against unacceptable concentrations of credit risk. Fixed exposure limits, however, do not recognize the relationship between risk and return.
A more quantitative approach, such as the one presented here, would make credit lines a function of marginal portfolio volatility that is, an output of the portfolio management model rather than an input to it.
For example, rightly or wrongly, financial markets are currently indicating a widespread perception of diminished risk due to credit, as illustrated by the historically tight level of credit spreads. In this envi- ronment, the bank lending marketplace has become increasingly competitive. As a result, good custom- er relationships have often become synonymous with heavily concentrated exposures as corporate borrowers command smaller bank groups and larger commitments from relationship banks.
Yet, banks are often caught in a paradoxical trap whereby those customers with whom they have developed the most valued relationships are precisely the customers to whom they have the least capacity to take in- cremental risk. Bank portfolio managers have begun to harbor suspicions that they may be vulnerable to a possible turn for the worse in global credit cycles, and that current levels of spread income may not justify the concentration of risks being accumulated.
Such concerns cannot easily be evaluated nor systematically reflected in pricing and credit extension decisions in the absence of a portfolio model.
In a portfolio context, the decision to take on ever higher exposure to an obligor will meet with ever higher risk — risk that grows geometrically with the concen- tration on that obligor. If relationship demands the extension of credit to a customer to whom the port- folio is overexposed, a portfolio model allows the portfolio manager to quantify in units of undercompensated risk exactly the extent of envisaged investment in relationship development.
Con- sequently the risk-return trade-off of concentrated lending activity can be better managed. Introduction to CreditMetrics page 11 Conversely, the portfolio manager can rationally take increased exposure to under-concentrated names.
Indeed, such names may be individually risky yet offer a relatively small marginal contribution to over- all portfolio risk due to diversification benefits. Such a model is equally appropriate for economic and regulatory capital purposes, but would differ fun- damentally from the capital measures currently mandated for bank regulation by the Bank for Interna- tional Settlements BIS. The weaknesses of this structure — such as its one-size-fits-all risk weighting for all corporate loans regardless of credit rating, and its inability to distinguish between diversified and undiversified portfolios — are increasingly apparent to regulators and market participants alike.
Particular concern has been paid to the uneconomic incentives created by the regulatory regime and the inability of regulatory capital adequacy ratios to accurately portray actual bank risk levels. In response to these concerns, bank regulators are increasingly looking for insights in internal credit risk models that generate expected losses and a probability distribution of unexpected losses.
The growth of derivatives activity has created uncertain and dynamic counterparty exposures that are significantly more challenging to man- age than the static exposures of traditional instruments such as bonds or loans.
End users and providers of these instruments need to understand such credit risk and its interaction with mar- ket risk. These include: third-party guarantees, credit derivatives, posted collateral, margin arrangements, and netting. Prudence requires that institutions thoroughly review existing risks before hedging or trading them.
Such risks are best understood in the context of a portfolio model that also explicitly accounts for credit quality migrations. Introduction to CreditMetrics page 12 This section addressed the factors making a portfolio approach to credit risk both necessary and timely.
The following section discusses why estimating portfolio credit risk is a much harder problem than es- timating portfolio market risk. The challenges of estimating portfolio credit risk 3.
If there were no further uncertainty relating to possible credit losses, that would be the extent of the risk management problem: Predictable credit losses year after year would be no more than a budgeted expense.
Risk, however, entails not just an estimated possibility of loss but also the uncertainty of loss. It turns out that if it is difficult enough to estimate even the expected portfolio values, it is harder still to predict uncertainties around these values.
Even a preliminary excursion into credit analysis reveals that not only are credit-related losses uncertain, but the distribution of outcomes is heavily skewed. It is not un- common for meaningful probabilities of loss in a credit portfolio to occur many standard deviations dis- tant from the mean. This reveals the inadequacy of an analysis that goes only so far as to characterize expected portfolio values without addressing the uncertainty of those values VaR.
The essence of prudent credit portfolio management is the establishment of a portfolio balance with adequate diversification.
Fundamental differences between credit risks and equity price risks, however, make equity port- folio theory problematic when applied to credit portfolios. The most immediate problem is that equity returns are relatively symmetrical and are well approximat- ed by normal distributions, while credit returns are highly skewed and fat-tailed as illustrated in Chart 1. Because of this asymmetry in credit returns, modeling the full distribution of portfolio values re- quires a great deal of information beyond simple summary statistics such as the mean and standard de- viation.
Without a full specification of the portfolio value distribution, it is not possible to compute the percentile levels necessary to describe risk in credit portfolios. By considering every possible combination of credit states across every obligor in the portfolio, the full distribution of a credit portfolio can be constructed mechanistically, but, this is computationally com- plex for portfolios of more than a few obligors. Consequently, the portfolio distribution can be estimat- ed only by a process of simulation.
Simulation reduces the computational burden by sampling outcomes randomly across all possibilities. Once the portfolio distribution has been approximated in this way, it is possible to compute percentile levels and summary statistics that describe the shape of the distribution. This is effectively a skewed bet in which the lender runs a small risk of incurring a large loss default , balanced by a much larger probability of earning a relatively small excess return net interest earnings , given no default.
Considering an entire portfolio rather than a single obligor has the effect of smoothing the distribution and capturing diversification effects. Nevertheless, the limitation of upside opportunity, combined with the remote possibility of severe losses, still causes the asymmetry and fat, long tails in typical credit portfolio distributions, where these risks are not easily diversified away.
For the layperson, a natural conclusion to draw might be that the benefits of diversification in a credit portfolio are not significant precisely because default correlations are so low. But this is not a correct conclusion. The implication of very low default correlations is that the system- atic risk in a credit portfolio is small relative to the nonsystematic or individual contribution to risk of each asset. Nonsystematic risk is hedgable or diversifiable risk. The greater the component of nonsys- tematic risk, the greater the benefits of diversification, and vice versa.
The problem can be viewed another way. Indices provide great hedges of risk in equity portfolios be- cause most equity portfolios are sufficiently diversified to resemble the market. However, because a portfolio of debt of those same names is unlikely to be sufficiently diversified to resemble the market, this same type of index hedge will not work in debt portfolios. The portfolio management consequences of a full characterization of credit risks are thus not insignificant: It takes many more names to fully diversify a credit portfolio than an equity portfolio,3 but when those diversification benefits are achieved, they are considerable.
An inadequately diversified portfolio, on the other hand, can result in significantly lower return on risk ratios than would seem intuitively obvious. In equity portfolios, it has been argued, indepen- dence of daily returns allows time to diversify risk.
Con- sequently, any persistent serial correlation in credit returns, as suggested by the historical tendency of one downgrade to be followed by another, can cause poor performance to increase volatility and create accelerating portfolio riskiness.
Morgan Securities Inc. It builds on Black-Scholes option pricing theory by stating that debt can be valued as if it were an option on the value of the underlying assets of the firm. Introduction to CreditMetrics page 14 is the only effective way to materially offset this effect.
An overview of CreditMetrics methodology 4. One popular approach considers only two states of the world: default and no default. Chart 4 A binomial model of credit risk No Default n.
CREDITMETRICS JPMORGAN PDF
Katilar Subadditivity is required in connection with aggregation of risks across desks, business units, accounts, or subsidiary companies. Conversely, if the incremental risk is zero for all positions, the portfolio is guaranteed to have minimum risk only if the risk measure is subadditive. The third market model assumes that the logarithm of the return, or, log-return, of any risk factor typically follows a normal distribution. The Marginal VaR of a position with respect to a portfolio can be thought of hpmorgan the amount of risk that the position jpmorgam adding to the portfolio. That is, the sum of the incremental risks of the positions in a portfolio equals the total risk of the portfolio.
Intro to Credit Metrics - JP Morgan