> From what I can tell (I obviously haven't read the whole document), the SEC is proposing to require ABS issuers to specify these rules in Python, and to specify the underlying asset pool in an XML format. This will make it easier for independent investors to analyze the securities on their own, without having to let the now-discredited rating agencies do their work for them.
Except that this just makes it easier to figure out who gets paid given a set of events, which isn't the hard part. (Yes, there can be lots of rules, but ....) It's also not where the ratings agencies screwed up.
The hard part is predicting the likelyhood of various events. This proposal does nothing to address that problem.
That is true. But it makes it much easier for potential investors to load up a bond in their own modeling tools, and run their own scenarios. They don't have to reverse engineer the cashflow structure based on the legal wording in the prospectus.
Part of the problem with the mortgage bubble though was that no one (other than a very select few, that were shorting them by buying CDSes) really knew what was in all the mortgage bond derivatives, especially once they got put into CDSes and then re-packaged into CDOs. By the end of it even the people that knew what was going on had to spend tremendous amounts of time figuring out what specific mortgages were in what packages, whereas under the new proposed rules at least all that information will be available up front (in theory).
And as sibling comments say- once you know what's actually in them, you can use your own risk models. If you don't know what's in them, tough to do that.
> By the end of it even the people that knew what was going on had to spend tremendous amounts of time figuring out what specific mortgages were in what packages, whereas under the new proposed rules at least all that information will be available up front (in theory).
The "in python" rule doesn't provide any information about what's in the package.
The hard part is predicting the likelyhood of various events. This proposal does nothing to address that problem.
Actually, it does. It lets you, as a potential investor, plug in your own values to the model. So if you think the issuer is undervaluing the default risk, you can use your own default risk numbers and remodel just by changing some values in a XML file or Python script and then running the interpreter again.
Of course, you need your own predictions for the likelihood of various events, but you could even just toss in a "default_risk = default_risk*1.5" line if you wanted to.
> > The hard part is predicting the likelyhood of various events. This proposal does nothing to address that problem.
> Actually, it does.
Actually it doesn't. As you wrote later
> Of course, you need your own predictions for the likelihood of various events,
That's the hard problem.
> It lets you, as a potential investor, plug in your own values to the model.
Right. It does nothing to help you figure out what those values should be. It merely tells you the consequences of whatever values you use.
Expressing how payout works as a python program does nothing to help you figure out whether 10% of the mortgages in a given portfolio will go into default. It only lets you compute the consequences of that event.
Comments
> From what I can tell (I obviously haven't read the whole document), the SEC is proposing to require ABS issuers to specify these rules in Python, and to specify the underlying asset pool in an XML format. This will make it easier for independent investors to analyze the securities on their own, without having to let the now-discredited rating agencies do their work for them.
Except that this just makes it easier to figure out who gets paid given a set of events, which isn't the hard part. (Yes, there can be lots of rules, but ....) It's also not where the ratings agencies screwed up.
The hard part is predicting the likelyhood of various events. This proposal does nothing to address that problem.
That is true. But it makes it much easier for potential investors to load up a bond in their own modeling tools, and run their own scenarios. They don't have to reverse engineer the cashflow structure based on the legal wording in the prospectus.
Part of the problem with the mortgage bubble though was that no one (other than a very select few, that were shorting them by buying CDSes) really knew what was in all the mortgage bond derivatives, especially once they got put into CDSes and then re-packaged into CDOs. By the end of it even the people that knew what was going on had to spend tremendous amounts of time figuring out what specific mortgages were in what packages, whereas under the new proposed rules at least all that information will be available up front (in theory).
And as sibling comments say- once you know what's actually in them, you can use your own risk models. If you don't know what's in them, tough to do that.
> By the end of it even the people that knew what was going on had to spend tremendous amounts of time figuring out what specific mortgages were in what packages, whereas under the new proposed rules at least all that information will be available up front (in theory).
The "in python" rule doesn't provide any information about what's in the package.
The hard part is predicting the likelyhood of various events. This proposal does nothing to address that problem.
Actually, it does. It lets you, as a potential investor, plug in your own values to the model. So if you think the issuer is undervaluing the default risk, you can use your own default risk numbers and remodel just by changing some values in a XML file or Python script and then running the interpreter again.
Of course, you need your own predictions for the likelihood of various events, but you could even just toss in a "default_risk = default_risk*1.5" line if you wanted to.
> > The hard part is predicting the likelyhood of various events. This proposal does nothing to address that problem.
> Actually, it does.
Actually it doesn't. As you wrote later
> Of course, you need your own predictions for the likelihood of various events,
That's the hard problem.
> It lets you, as a potential investor, plug in your own values to the model.
Right. It does nothing to help you figure out what those values should be. It merely tells you the consequences of whatever values you use.
Expressing how payout works as a python program does nothing to help you figure out whether 10% of the mortgages in a given portfolio will go into default. It only lets you compute the consequences of that event.