In this module, we're going to discuss the pricing and risk management of CDO portfolios. We will focus mainly on the risk management of CDO portfolios. Unfortunately, the risk management of CDO portfolios is an enormous topic in and of itself, and we won't have time to do justice in this module. However, we will discuss some of the issues that arise. We will discuss some of the weaknesses with the Gaussian copula model. We will also mention as an aside that linear correlation, the correlation coefficient in and of itself, is not enough to describe the dependent structure of a multivariate distribution. So this fact is lost on many people and we will emphasize it at the end of this module. Here's an example of a sample synthetic CDO portfolio. Across the top, we've got various columns. So the first column index, this contains a name. So CDX IG A, this actually refers to a reference portfolio. So this is an index with a certain number of names in the index, and these names are known. Typically, there will be a 100 names or a 125 names in the index. So CDX IG A. IG stands for investment grade. A is actually a number. It could be 8, 9, 10, 11, and so on. Different numbers contain different reference portfolios. The second column is just a tranche description. It could be an equity tranche, mezzanine tranche, senior tranche, or it could be an index. This can be viewed as a tranche with a lower attachment point of zero and an upper attachment point of 100 as we have here. The next two columns indeed contains the attachment points. L for lower, U for upper attachment point. We have the maturity. These are 10 years, seven years, five years, three years, four years, and so on. So the maturities can vary and we have the notional amount. So this is the notional amount of protection that we're buying or selling. These are the current prices and basis points. Now, these are the spreads, the spreads, the current market spreads of these tranches. The equity tranche prices were often quoted in a different format to reflect upfront payments and so on, but we're not going to concern ourselves with that. IG as I said will refer to investment grade. HY, for example, refers to a high yield. So these would be riskier credits or riskier bonds that are more likely to default. Very often, there is substantial overlap in these portfolios. So for example, IG A and IG B, each portfolio or reference portfolio will contain a 125 names or a 120 names and so on. In the case of A and B here, typically, there'll be a very large overlap between the two portfolios. So most of the names in A will also be in portfolio B, and so on. In practice, structured credit portfolios could contain many, many positions with different reference portfolios, different maturities, and counter-parties. They also can have different trading formats. So as I said earlier, sometimes these tranches trade in the form of a spread, a spread that is paid quarterly or premium, that is paid quarterly. This is the insurance rate if you like for buying protection or buying insurance on the tranche. But sometimes they can also trade in an upfront format and/or a running spread format. The ultimate payoff of such a portfolio is very path dependent with substantial idiosyncratic risk. They are very difficult to risk-manage. They cannot be very expensive to unwind and that is due to why bid-offer spreads. Over here, what I'm giving here is a current price, but this should really be interpreted as the midpoint of the bid-offer spread. So the bid and the offer will be on either side of say, 223, maybe we'll have 210 and 240. So if you want to sell protection you will hit one side of the bid-offer spread, if you want to buy protection, you will hit the other side. So if I'm buying protection, I'm going to have to pay 240 basis points for that protection. If I'm selling protection, I'm going to receive 210. So that's the bid-offer. Sometimes these bid-offer spreads can be very wide. So actually, unwinding such a portfolio can be very expensive, especially in terms of market stress or when these portfolios, these synthetic CDO tranches aren't trading very often as would be the case today. Computing the mark-to-market value of these portfolios can also be very difficult because market prices may be non-transparent. I don't think I've used the phrase mark-to-market yet in this course, but just to be clear, mark-to-market is referring to the current value of a portfolio using current prices in the marketplace. So you're not using the historical price at which you purchased a portfolio of securities, instead you're using the current market price for this security. So that refers to mark-to-market. On this note, you might be interested in the "Belly of the Whale Series" on the Alphaville blog of the Financial Times. You can actually get to that blog via this link here. While the Financial Times does have a paywall, so most of the articles aren't available for viewing freely. Their blogs are. So the articles in this series can be found here. This series refers to the so-called London Whale, and the fact that London Whale first came to attention because price levels in the CDX IG9 index. So this is an example where we're using a number. So the IG9 index diverged too much from other related price levels, in particular diverge too much, given the CDS prices of the credits in the IG9 portfolio. So you'll see a lot of interesting material in the articles that have been published on this series on this Alphaville blog. You won't be able to understand everything in this series, and that's in part because there's a lot of jargon and there's a lot of references to positions that we can't see. Indeed, there is references to communications that we can't see. Maybe they weren't email communications but verbal communications between some of the players. So you won't always understand what's going on, but you will see a lot of discussion of value, risk, and Gaussian copula, and synthetic CDOs and risk management. By the way, I should have said this at the beginning, that this London Whale came to attention because of ultimately massive losses that occurred in the synthetic credit portfolio of the Chief Investment Office of JP Morgan. So this is a very recent situation where they lost seven billion dollars out of the Chief Investment Office on synthetic credit portfolios. Reading this series is certainly of interest and certainly relevant to what we've been discussing in these modules.
