Enron Mail

From:vince.kaminski@enron.com
To:william.bradford@enron.com
Subject:Re: FW: Credit Risk Model Comments - at this point.
Cc:rick.buy@enron.com, mark.ruane@enron.com, vince.kaminski@enron.com
Bcc:rick.buy@enron.com, mark.ruane@enron.com, vince.kaminski@enron.com
Date:Thu, 12 Apr 2001 08:31:00 -0700 (PDT)

Bill,

We spent about an hour with Rick explaining the rationale behind different=
=20
solutions
and I think he has a better understanding of the models at this point.

I think he was asked by Jeremy to look into it.

Vince



From: William S Bradford/ENRON@enronXgate on 04/11/2001 09:05 PM
To: Rick Buy/ENRON@enronXgate, Vince J Kaminski/HOU/ECT@ECT
cc: Mark Ruane/ENRON@enronXgate=20
Subject: FW: Credit Risk Model Comments - at this point.

Rick/Vince,

Should this not be a Credit/Research initiative while the business unit=20
focuses on originating good economic transactions? Not to be complaining,=
=20
but shouldn't EES be focusing on infrastructure issues rather than waste=20
resources on a project we are already moving forward on? You can't run a=
=20
portfolio model, unless you have deals in a risk system! How complex do we=
=20
want these MODELS to be? Behavioral implications on credit default? They=
=20
still don't seem to understand.

Regards,
Bill


Mark - please attend. You may want to include Martin to help EES understan=
d=20
the complexity of their deals.

-----Original Message-----
From: Krishnarao, Pinnamaneni =20
Sent: Wednesday, April 11, 2001 9:14 AM
To: Kaminski, Vince; Dhar, Amitava; De, Rabi; William S=20
Bradford/HOU/ECT@ENRON; Tamarchenko, Tanya
Subject: Credit Risk Model Comments - at this point.

Comments from Rick Jones on the credit reserve model. Anita Dupont is setti=
ng=20
up a meet with Rick Jones to discuss these. Vince & Bill - if you want to=
=20
join the meeting, please let me or Anita know.

Regards,
Krishna.
---------------------- Forwarded by Pinnamaneni Krishnarao/HOU/ECT on=20
04/11/2001 09:04 AM ---------------------------


Richard B Jones@EES
04/10/2001 04:16 PM
To: Pinnamaneni Krishnarao/HOU/ECT@ECT
cc: =20
Subject: Credit Risk Model Comments - at this point.


---------------------- Forwarded by Richard B Jones/HOU/EES on 04/10/2001=
=20
04:16 PM ---------------------------


Richard B Jones
03/23/2001 05:53 PM
To: Cheryl Lipshutz/HOU/EES@EES, Trushar Patel/Corp/Enron@Enron,=20
michelle.wenz@enron.com, Gayle Muench/ENRON@enronXgate, Jeremy=20
Blachman/HOU/EES@EES
cc: =20
Subject: Credit Risk Model Comments - at this point.

Hi everyone,

I have run the model and, along with the contract briefs I have some=20
questions & ideas. I was hoping to talk to each of you so I could avoid=20
writing this detailed, one-sided e-mail, but with our schedules being so=20
exclusive, this will have to do for now.

Every deal has its own model because of the commodity deal structure=20
complexity. So no aggregate results can be obtained without having the mode=
ls=20
for each contract. However, the JC Penny=01,s version can serve as a testin=
g=20
platform for some of the items I am mentioning below. I have not talked to=
=20
the people in research who are the most knowledgeable about the model, so=
=20
some of these comments may be mute points. I plan to do that went I get bac=
k.=20

1) Since the credit risk is developed for a time period, it makes sense to=
=20
regularly update the commodity data (and credit rating if its chaged) and=
=20
re-run the model for the time remaining. I would expect this is done alrea=
dy.

2) The default probabilities seem not to change. That is, if the input cred=
it=20
rating is E1, then the E1 default probability curve is used for the contrac=
t=20
period. For annual accounting that seems OK, but in MTM, it seems to me tha=
t=20
the credit analysis needs to take into consideration the credit rating=20
transition probabilities. That is, the credit implications of companies=20
changing their credit rating during the contract period. with some=20
constraints imposed by actually slow credits appear to change would give a=
=20
more realistic view of our credit risk in the MTM world.

3) Are all "defaults" created equal to us? Look at OC. It seems to me that=
=20
the data used to develop the default probabilities are over different=20
business segments and are OK ----for that range of companies. However, we a=
re=20
dealing with specific types of firms where "default" may not mean we do not=
=20
get paid. Sure we still have some credit risk, but it=01,s not like Montgom=
ery=20
Ward=01,s where the lights are being turned off for good. Energy is so=20
fundamental for a company=01,s success and default actions can be used as a=
way=20
to save a company albeit in a different form. So financial default does no=
t=20
neccesarily mean default for EES commodity payments totally.

4) A while back someone said to me that may, maybe the people who reach for=
a=20
life preserver are more likely to live than those that don=01,t. By that I =
mean=20
that, perhaps our use of these default probabilities actually overstates th=
e=20
credit risk in that if a company has at least enough proactive vision to=20
contract EES, then they are more likely to improve that one that doesn=01,t=
.=20
This is a type of behavioral variable that the data doesn=01,t consider. Th=
is=20
would be a useful MBA project to examine these types of corporate variables=
=20
and compare it to their credit rating forward curve.

5) This leads me to something I hope we can acomplish in the special financ=
e=20
team. The contract briefs are, to me, the begimnning of this exercise. If =
we=20
can combine our customers into "exposure group portfolios" (for lack of a=
=20
better term), where a group has similar "risk characteristics" beyind the=
=20
current parameter set, that we define, then this offers a potential to shop=
=20
some of these exposure to specialized insurance markets.

6) A technical point. Monte Carlo simulations are numerical experiments.=20
Besides the model assumptions, numerical experiments have three inherent=20
error attributes; the number of trials, numerical roundoff, and random numb=
er=20
generator randomness statistical properties. The first two are not a proble=
m=20
in this application but the last one could be. Has anyone examined the effe=
ct=20
of using different random number generators on Enron=01,s aggregate credit =
risk?

7) There is one last point here. For most of the above points, the "improve=
d"=20
analysis could make the credit risk be higher.=20

Rick=20