CreditMetrics™. ✤ Introduced in by J.P. Morgan & Co. ✤ It is a structural model of default, which also takes into account the risk of credit deterioration. Value ($). AAA. AA. A. BBB. BB. B. . CCC. Default. Source: CreditMetrics, J. P. Morgan. rics published by J.P. Morgan. The complete document can be downloaded from Credit Ratings. An essential feature of the CreditMetrics.
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The RiskMetrics variance model also known as exponential smoother was first established inwhen Sir Dennis Weatherstonethe new chairman of J. Morganasked for a daily report measuring and explaining the risks of his firm. Nearly four years later inJ. Morgan launched the RiskMetrics methodology to the marketplacecerditmetrics the substantive research and analysis that satisfied Sir Dennis Weatherstone’s request freely available to all market participants.
RiskMetrics – Wikipedia
Inas client demand for the group’s risk management expertise exceeded the firm’s internal risk management resources, the Corporate Risk Management Department was spun off from J. Morgan as RiskMetrics Group with 23 founding employees. The RiskMetrics technical document was revised in Init was revised again in Return to RiskMetrics. Ina new method for modeling risk factor returns was introduced RM Portfolio risk measurement can be broken down into steps. The first is modeling the market that drives changes in the portfolio’s value.
The market model must be sufficiently specified so that the portfolio can be revalued using information from the rceditmetrics model. The risk measurements are then extracted from the probability distribution of the changes in portfolio value.
Risk management systems are based on models that describe potential changes in the factors affecting portfolio value. These risk factors are the building blocks for all pricing functions.
In general, the factors jpmrogan the prices of financial securities are equity pricesforeign exchange ratescommodity pricesinterest ratescorrelation and volatility. By generating future scenarios for each risk factor, we can infer changes in portfolio value and reprice the portfolio for different “states of the world”. The first widely used portfolio risk measure was the standard deviation of portfolio value, as described by Harry Markowitz.
While comparatively easy to calculate, standard deviation is not an ideal risk measure since it penalizes profits as well as losses. The tech doc crwditmetrics VaR as the risk measure of choice among investment banks looking to be able to measure their portfolio risk for the benefit of banking regulators.
VaR ccreditmetrics a downside risk measure, meaning that it typically focuses on losses. A third commonly used risk measure is expected shortfall crfditmetrics, also known variously as expected tail loss, XLoss, conditional VaR, or CVaR.
The Marginal VaR of a position with respect to a portfolio can be thought of as the amount of risk that the position is adding to the portfolio. It can be formally defined as the difference between the VaR of the total portfolio and the VaR of the portfolio without the position.
To measure jpmoorgan effect of changing positions on portfolio risk, individual VaRs are insufficient.
Volatility measures the uncertainty in the return of an asset, taken in isolation. When this asset belongs to a portfolio, however, what matters is the contribution to portfolio risk. Incremental risk statistics provide information crevitmetrics the sensitivity of portfolio risk to changes in the position holding sizes in the portfolio.
An important property of incremental risk is subadditivity. That is, the sum of the incremental risks of the positions in a portfolio equals the total risk of the portfolio. This property has important applications in the allocation of risk to different units, where the goal is to keep the sum of the risks equal to the total risk. Since there are three risk measures covered by RiskMetrics, there are three incremental risk measures: Incremental statistics also have applications to portfolio optimization.
A portfolio with minimum risk will have incremental risk equal to zero for all positions. Conversely, if the incremental risk is zero for all positions, the portfolio is guaranteed to have minimum risk only if the risk measure is jpmoragn. A coherent risk measure satisfies the following four properties:.
In other words, the risk of the sum of subportfolios is smaller than or equal to the sum of their individual risks.
Subadditivity is required in connection with aggregation of risks across desks, business units, accounts, or subsidiary companies. This property is important when different business units calculate their risks independently and we want to get an idea of the total risk involved. Subadditivity could also be a matter of concern for regulators, where firms might be motivated to break up into affiliates to satisfy capital requirements. If we double the size of every position in a portfolio, the risk of the portfolio will be twice as large.
If losses in portfolio A are larger than losses in portfolio B for all possible risk factor return scenarios, then the risk of portfolio A is higher than the risk of portfolio B. The estimation process of any risk measure can be wrong by a considerable margin.
If from the imprecise estimate we cannot get a good understanding what the true value could be, then the estimate is virtually worthless.
A good risk measurement is to supplement any estimated risk measure with some indicator of their precision, or, of the size of its error. There are various ways to quantify the error of some estimates. One approach is to estimate a confidence interval of the risk measurement. The first is very similar to the mean-covariance approach of Markowitz. The covariance matrix can be used to compute portfolio variance.
RiskMetrics assumes that the market is driven by risk factors with observable covariance. The risk factors are represented by time series of prices or levels of stocks, currencies, commodities, and interest rates. Instruments are evaluated from these risk factors via various pricing models. The portfolio itself is assumed to be some linear combination of these instruments.
The second market model assumes that the market only has finitely many possible changes, drawn from a risk factor return sample of a defined historical period. Typically one performs a historical simulation by sampling from past day-on-day risk factor changes, and applying them to the current level of the risk factors to obtain risk factor price scenarios.
These perturbed risk factor price scenarios are used to generate a profit loss distribution for the portfolio. This method has the advantage of simplicity, but as a model, it is slow to adapt to changing market conditions.
It also suffers from simulation error, as the number of simulations is limited by the historical period typically between and business days. The third market model assumes that the logarithm of the return, or, log-return, of any risk factor typically follows a normal distribution.
Collectively, the log-returns of the risk factors are multivariate jpmorgqn. Monte Carlo algorithm simulation generates random market scenarios drawn from that multivariate normal distribution. For each scenario, the profit loss of the portfolio is computed.
This collection of profit loss scenarios provides a sampling of the profit loss distribution from which one can compute the risk measures of choice. Nassim Taleb in his book The Black Swan wrote:. The giant firm J. From Wikipedia, the free encyclopedia. Not to be confused with risk metricthe abstract concept quantified by risk measures.
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