(Final) Counterparty Credit Risk Model- White Paper 081016

Counterparty Credit Risk ModelBrandon Bilton, Operations Intern, Derivatives/Risk Team August 2, 2016

Counterparty Credit Risk Model

Project Scope

This project was given to me by Kelly Newhall and Anthony Paolini of the Derivatives team on

my first day of the internship with IMD. They informed me that the Counterparty Credit Risk

Model had originally been created by an intern in 2010, but needed to be reviewed and updated

so that the Risk Team would be able to implement it within their current framework. The goal of

the Counterparty Credit Risk Model is to be an effective real-time scorecard for measuring bank

risk. This spreadsheet has multiple tabs that hold significant information combining the main

factors and sub factors which are related to the risk and creditworthiness of the 13 ISDA

counterparties with whom TRS does business. At the end of the spreadsheet, a scorecard will

be found that accumulates all the essential data of the 13 financial institutions that is taken from

Bloomberg and summarized to give you an overall score of the banks ranking them from best to

worst. The overall questions that will be answered from this project will be: Who is the end user?

What are the most effective data points for monitoring counterparty risk? What are the weights

that should be applied to each data point?

Background

First, I would like to say how thankful I am to have had this opportunity to work with a great

company like the Teacher Retirement System of Texas (TRS). A company which I have found

to be comprised of a talented group of corporate professionals who are willing to lend a helping

hand and give advice when I was in doubt. This was my first internship, and it has offered me a

real-world corporate experience that I have benefitted tremendously. During my stint with TRS, I

have furthered my skills in Microsoft Excel; gained a more concise view of the way financial

investment institutions operate; and expanded my communication skills. Through this internship,

my skill set as a student, and business professional, have been enhanced and it will ultimately

give me a better foundation to stand upon.

When I was assigned the Counterparty Credit Risk Model project at the beginning of my

internship, I knew without a doubt that it would be a daunting task. This is due to the fact that I

had never worked with an Excel spreadsheet of this magnitude; nor the level of difficulty.

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Counterparty Credit Risk Model August 2, 2016

Knowing those facts, I should also go on to say that I would not let this deter me in any way

from getting this project accomplished.

The initial idea of this project was for me to find the most effective data points for monitoring

counterparty risk; the weights I felt should be applied; and also to determine if this counterparty

scorecard could be useful in real time to the end user. In this research paper, I will explain to

you the steps I took, and the progress I made until the completion of this project.

The Investment Management Division (IMD) of TRS has been entrusted with managing

investments and mitigating risk of the millions of dollars that come out of the pockets of

thousands of diligent educators across the great state of Texas. These role models who have

educated Texas students through the years, expect TRS to invest their funds intelligently so that

they can live comfortably after retirement.

All over-the-counter (“OTC”) derivatives must be subject to an established ISDA Master

Agreement. One of the risks that IMD monitors is the 13 ISDA Counterparties with whom TRS

does business. I believe the Counterparty Credit Risk Model spreadsheet is unique and will be

an asset to the Risk and Derivatives team because it allows the user to monitor and weight the

basic economic data that are necessary when assessing the financial strength of any banking

institution.

The spreadsheet utilizes Bloomberg API functionality to populate the spreadsheet with the most

up to date data points for the factors selected. The first change I made was updating the ISDA

counterparty Bloomberg tickers in which I added three new counterparties to the list. Then I

began to familiarize myself with the different tabs, data points, and the actual scorecard.

Research & Analysis

TRS Internal Feedback

Within a few weeks, I understood the spreadsheet slightly better than I did when I first received

it and began to clean up minor things such as removing unnecessary country tickers, changing

Bloomberg mnemonics that no longer existed, and repairing broken excel formulas. From there I

met with IPM’s Financial Sectors Analyst, KJ Van Ackeren, in regards to getting a better

understanding of the data points and also checking to see if the data points within the

spreadsheet were still suitable in the current 2016 marketplace. Once KJ confirmed that

everything was still relevant he instructed me to take a look at the CCAR Report instead of the

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Stress Test because it would contain the tier capital 1 capital ratio, common equity tier 1 ratio,

tier 1 leverage ratio, and the total capital ratio along with their minimums.

After I had incorporated the information into the spreadsheet that KJ thought would be beneficial

a meeting was setup with James Neild and Steven Lambert of the Risk team. It was now time to

get their insight on what they felt would be the next step to further advance this counterparty risk

model. From there, I was enlightened on several different ideas that I could include into the

spreadsheet that would prove beneficial for the end users. They felt it would be best for me to

include the probability of default chart which would significantly drop the ratings as the letter

grade began to fall, create a visual representation of the minimum requirements compared to

their actual score, and incorporate a way to should the bank regulatory and credit rating

rankings.

Regulatory/ Credit Ratings Review

To begin, I started by researching Basel, Dodd-Frank, and three credit rating agencies to find

out what was common between them and what they were monitoring in terms of bank economic

health. I began reading on Basel III which is a nationwide committee with 10 different countries

that serve as a leader in regulatory reform. This committee has come up with several different

measurements of bank health which will come into full effect during the year 2019. Banks that

are known as “too big to fail” must be in compliance with these minimums in order to continue

their day to day operations. After the major financial crisis in 2008, the Federal Reserve began

to brainstorm ways they could ensure that another market collapse doesn’t happen again in the

United States. As a result, the Obama administration implemented the Dodd-Frank Act

regulatory reform passed in 2010.

Finally, I began to research the three credit rating agencies: Moody’s, Standards & Poor, and

Fitch. However, due to lack of transparent information, I chose to only focus on Moody’s

because they gave me a wealth of good information on their methodologies, whereas, S&P and

Fitch didn’t.

I also determined that Basel, Dodd-Frank, and Moody’s had the following data points:

Common equity tier 1, which is the core equity capital divided by its total risk-

weighted assets.

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Tier 1 capital ratio, which is core measurement of financial strength of a bank.

Total capital ratio, which is the total capital that a bank holds to protect itself

from losses due to risk from underperforming loans.

Tier 1 leverage ratio, which is calculated by dividing Tier 1 capital by a bank's

average total consolidated assets and certain off-balance sheet exposures.

Based on the importance of these four capital measurements, I weighted the Capital, Liquidity,

and Asset Quality tab 50% of the total scorecard. I felt the capital was the most important

because when the bank generates an adequate amount of capital, it is then able to fund itself.

Once the foundation of research had been laid after several additional follow up meetings with

IPM, Risk and Derivatives, I was then able to implement this information within the spreadsheet

by completing the following procedure:

1. I entered the correct data points.

2. Added equity performance BBG mnemonics.

3. Revised the rating scale using Moody’s probability of default scale.

4. In addition to the interal scorecard, a regulatory scorecard, and credit agency

scorecard have been added which all rate and rank the 13 ISDA counterparties

from 1 being the best to 13 being the worst.

5. Finally, with all this information I was then able to create five visual graphs; four

of which indicated how low the counterparties may or may not be to the

minimums and the fifth was an equity confidence graph that showed the one year

and 30 day changes in equity price for each bank.

Conclusion

In conclusion, this revised Counterparty Credit Risk Model scorecard will be a very useful tool to

the Risk and Derivatives team whom are considered to be the end users. These improvements

now permit both teams mentioned above the ablility to quickly reference and determine the

financial condition of the 13 ISDA Counterparties. In addition to, it has also been concluded that

between Basel, Dodd-Frank, and Moody’s there was a sense of cohesion in regards to the data

points they used to monitor the banks; those were tier capital 1 capital ratio, common equity tier

1 ratio, tier 1 leverage ratio, and the total capital ratio. These four data points are the most

frequently used ratios when the regulators and credit rating agency want to gauge the status of

a banks health during any giving point in time. Finally, the best part about this Counterparty

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Credit Risk Model scorecard is that you are also able to input your own weights according to the

market and what you feel is best. With that being said, I weighted the Capital, Liquidity, and

Asset Quality tab 50% of the total scorecard because as stated before I felt that these are the

core basis of bank stress testing and monitoring. I weighted the Company Credit Risk tab a total

of 40% of the overall scorecard because it contained equity performance and leverage which I

felt gave investors a sense of confidence about the market. I also wanted to show that the

capital ratios required a more significant impact on the overall outcome of the scores. The last

10% was given to the two Country Domicile tabs due to the fact that during my research there

was very little stated about the Country Domicile so that is my reasonsing for giving it such a low

percentage. But if anything is to happen within a country such as a major crisis or something of

that nature you are able to make the proper adjustments. Now that my work is complete I am

able to give a finanlized project to the Risk and Derivatives team so that they may now put it to

use and continue the great work that is ahead of them.

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Appendix

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(5 Year CDS Spread/30 Day Change in CDS Spreads needs to be historically tracked and updated during the next phase of improvemnts)

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Documents

(Final) Counterparty Credit Risk Model- White Paper 081016