Credit Metrics - The Foolproof Key To Handle All Credit Transactions
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Sam Miller
May 4, 2013, 9:10:02 AM5/4/13
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Article Title: Credit Metrics - The Foolproof Key To Handle All Credit Transactions
Author: Sam Miller
Word Count: 521
Article URL: http://www.isnare.com/?aid=202714&ca=Business+Management
Format: 64cpl
Contact The Author: http://www.isnare.com/eta.php?aid=202714
Easy Publish Tool: http://www.isnare.com/html.php?aid=202714
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Credit Metrics is a method of reigning in credit risk by modeling changes in credit ratings portfolio. This implies a propositional change in value of the holdings. Credit metrics tries to construct that is not readily observable, which is the volatility of value due to changing credit quality. This approach renders credit metrics more of an exercise in proposing models and which explain the changes in credit related instruments. More than often the models that best describe credit risk don't rely on the assumption that returns distribution is imperative.
Credit metrics is basically a framework that helps to quantify credit risk on portfolio of everyday credit products. This includes loans, commitments to lend, and market -driven instruments which are vulnerable to counterparty defaults. The sound of knowledge of Credit metrics enables you get a transparent depiction of credit risk. Transparency and effective management share a direct proposition and usually goes hand in glove. The common crisis that has been plaguing the credit risk measurement is the absence of a common point reference. The multiple approaches to measure of credit risk render them practically incomparable.
Credit measure and Credit metrics are often misinterpreted to be the same. When we refer to a measure we are actually assigning a number to something. A metric on the other hand is how interpret that assigned number. A simple example would be that of calculating a person's height. Let's ay it measures to 5.1 inches, the inches is the measure of the person's height and the, "height" is the metric.
Although credit metrics and risk metrics are similar in many ways they are not the same. The primary difference between the two is that risk metrics presents an loads of daily liquid pricing data which can be easily used to construct a model of conditional volatility. On the other hand credit metrics offers relatively less and sporadically priced data for constructing a model of unconditional volatility
The recovery of a claim remains unknown until an obligor defaults. Credit metrics on the other hand models recovery by using a beta distribution. A beta distribution is characterized by a mean and standard deviation. The recovery of the distribution is affected by changes in parameters as demonstrated by the beta distribution spreadsheet.
In credit metrics the changes in value is not only influenced by chancy default events but also by the upswings and downswings in credit quality. Credit risk also addresses the value-at-risk (VaR) which is basically the volatility of value and not just the expected losses. It makes sense to address the co-relation of credit quality fluctuation across obligors as it allows you directly calculate the potential over -concentration across the portfolio.
Modeling transitions for a single name is pretty simple. If one has an idea of the probability to each state, then he/she can approximately simulate a transition corresponding to each state by observing a random uniform variable. The transition can be made by basing on the outcome of the random uniform variable. The glitch is when there are multiple correlated names in the portfolio.
About The Author: If you are interested in credit metrics, check this web-site to learn more about loan metrics. http://www.strategy2act.com/solutions/credit_risk_excel.htm
Please use the HTML version of this article at:
http://www.isnare.com/html.php?aid=202714
*********************** ARTICLE END ***********************
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- For more free-reprint articles go to http://www.isnare.com
Message delivered directly to members of the group:
publish-the...@googlegroups.com
*****************************************************************
Please consider this free-reprint article written by:
Sam Miller
*****************************
IMPORTANT - Publication/Reprint Terms
- You have permission to publish this article electronically in free-only publications such as a website or an ezine as long as the bylines are included.
- You are not allowed to use this article for commercial purposes. The article should only be reprinted in a publicly accessible website and not in a members-only commercial site.
- You are not allowed to post/reprint this article in any sites/publications that contains or supports hate, violence, porn and warez or any indecent and illegal sites/publications.
- You are not allowed to use this article in UCE (Unsolicited Commercial Email) or SPAM. This article MUST be distributed in an opt-in email list only.
- If you distribute this article in an ezine or newsletter, we ask that you send a copy of the newsletter or ezine that contains the article to http://www.isnare.com/eta.php?aid=202714
- If you post this article in a website/forum/blog, ALL links MUST be set to hyperlinks and we ask that you send a copy of the URL where the article is posted to http://www.isnare.com/eta.php?aid=202714
- We request that you ask permission from the author if you want to publish this article in print.
The role of iSnare.com is only to distribute this article as part of its Article Distribution feature ( http://www.isnare.com/distribution.php ). iSnare.com does NOT own this article, please respect the author's copyright and this publication/reprint terms. If you do not agree to any of these terms, please do not reprint or publish this article.
*****************************
Article Title: Credit Metrics - The Foolproof Key To Handle All Credit Transactions
Author: Sam Miller
Word Count: 521
Article URL: http://www.isnare.com/?aid=202714&ca=Business+Management
Format: 64cpl
Contact The Author: http://www.isnare.com/eta.php?aid=202714
Easy Publish Tool: http://www.isnare.com/html.php?aid=202714
*********************** ARTICLE START ***********************
Credit Metrics is a method of reigning in credit risk by modeling changes in credit ratings portfolio. This implies a propositional change in value of the holdings. Credit metrics tries to construct that is not readily observable, which is the volatility of value due to changing credit quality. This approach renders credit metrics more of an exercise in proposing models and which explain the changes in credit related instruments. More than often the models that best describe credit risk don't rely on the assumption that returns distribution is imperative.
Credit metrics is basically a framework that helps to quantify credit risk on portfolio of everyday credit products. This includes loans, commitments to lend, and market -driven instruments which are vulnerable to counterparty defaults. The sound of knowledge of Credit metrics enables you get a transparent depiction of credit risk. Transparency and effective management share a direct proposition and usually goes hand in glove. The common crisis that has been plaguing the credit risk measurement is the absence of a common point reference. The multiple approaches to measure of credit risk render them practically incomparable.
Credit measure and Credit metrics are often misinterpreted to be the same. When we refer to a measure we are actually assigning a number to something. A metric on the other hand is how interpret that assigned number. A simple example would be that of calculating a person's height. Let's ay it measures to 5.1 inches, the inches is the measure of the person's height and the, "height" is the metric.
Although credit metrics and risk metrics are similar in many ways they are not the same. The primary difference between the two is that risk metrics presents an loads of daily liquid pricing data which can be easily used to construct a model of conditional volatility. On the other hand credit metrics offers relatively less and sporadically priced data for constructing a model of unconditional volatility
The recovery of a claim remains unknown until an obligor defaults. Credit metrics on the other hand models recovery by using a beta distribution. A beta distribution is characterized by a mean and standard deviation. The recovery of the distribution is affected by changes in parameters as demonstrated by the beta distribution spreadsheet.
In credit metrics the changes in value is not only influenced by chancy default events but also by the upswings and downswings in credit quality. Credit risk also addresses the value-at-risk (VaR) which is basically the volatility of value and not just the expected losses. It makes sense to address the co-relation of credit quality fluctuation across obligors as it allows you directly calculate the potential over -concentration across the portfolio.
Modeling transitions for a single name is pretty simple. If one has an idea of the probability to each state, then he/she can approximately simulate a transition corresponding to each state by observing a random uniform variable. The transition can be made by basing on the outcome of the random uniform variable. The glitch is when there are multiple correlated names in the portfolio.
About The Author: If you are interested in credit metrics, check this web-site to learn more about loan metrics. http://www.strategy2act.com/solutions/credit_risk_excel.htm
Please use the HTML version of this article at:
http://www.isnare.com/html.php?aid=202714
*********************** ARTICLE END ***********************
- To distribute your articles go to http://www.isnare.com/distribution.php
- For more free-reprint articles go to http://www.isnare.com
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