Tag Archives: synthetic CDOs

Why Liquidity Rules

Businesses with strong cash-flow are rightfully held in high esteem as investments. Google and Apple are good examples. Betting/gambling firms and insurers (in non-stressed loss periods) are other examples of businesses, if properly run, that can operate with high positive cash-flow.

The banking sector is at a completely different end of the spectrum as liquidity transformation is essentially the business. Everybody knows of Lehman Brothers bankruptcy, which was instigated in late 2008 by an immediate need to find $3 billion of cash to meet its obligations. The winding-up of the Lehman Brothers holding company in the US is estimated to return approximately 26 cents on the dollar according to this FT article.  It was therefore a surprise to read in the FT article and in another recent article on the expected surplus of £6 to £7 billion from the winding up of Lehman Brothers operation in London after all of the ordinary creditors have been repaid in full. This outcome is particularly surprising as I understood that the US operation of Lehman did a cash sweep across the group, including London, just prior to entering bankruptcy.

In his book (as referenced in this post), Martin Wolf highlights the changing perceptions of value since the crisis by using ABX indices from Markit which represent a standardized basket of home equity asset backed securities. The graph below shows the value for one such index, the ABX.HE.1, to the end of 2011. These indices are infamous as they were commonly used to value securities since the crisis when confidence collapsed and can be used to demonstrate the perils of mark to market/model accounting (or more accurately referred to as mark to myth values!).

click to enlargeMarket Value Asset Backed Subprime Index

I have included the more recent values of similar ABX indices in the bubbles as at last year from Wolf’s book. This graph accentuates the oft used quote from Keynes that “the market can remain irrational longer than you can remain solvent”.

Wolf argues that the 3% liquidity ratio proposed under Basel III or indeed the 5% proposed in the UK are totally inadequate and he suggests a liquidity ratio closer to 10%. On capital ratios, Wolf argues for capital ratios of 20% and above with a strong emphasis on tier 1 type equity or bail-inable debt that automatically converts. This contrasts against the 6% and 2.5% of tier 1 and 2 capital proposed respectively under Basel III (plus a countercyclical and G-SIFI buffer of up to 5%). Wolf also highlights the bankers ability to game the risk weighted asset rules and suggests that simple capital ratios based upon all assets are simpler and cleaner.

Wolf supports his arguments with research by Bank of England staffers like David Miles1 and Andrew Haldane2 and references a 2013 book3 from Admati and Hellwing on the banking sector. Critics of higher liquidity and capital ratios point to the damage that high ratios could do to business lending, despite the relatively low level of business lending that made up the inflated financing sector prior to the crisis. It also ignores, well, the enormous cost of the bailing out failed banks for many tax payers!

For me, it strengthens the important of liquidity profiles in investing. It also reinforces a growing suspicion that the response to the crisis is trying to fix a financial system that is fundamentally broken.

 

 

  1. Optimal Bank Capital by David Miles, Jing Yang and Gilberto Marcheggiano
  2. The Dog and the Frisbee by Andrew Haldane
  3. The Bankers New Cloths by Anat Admati and Martin Hellwing

 

Confounding correlation

Nassim Nicholas Taleb, the dark knight or rather the black swan himself, said that “anything that relies on correlation is charlatanism”.  I am currently reading the excellent “The signal and the noise” by Nate Silver. In Chapter 1 of the book he has a nice piece on CDOs as an example of a “catastrophic failure of prediction” where he points to certain CDO AAA tranches which were rated on an assumption of a 0.12% default rate and which eventually resulted in an actual rate of 28%, an error factor of over 200 times!.

Silver cites a simplified CDO example of 5 risks used by his friend Anil Kashyap in the University of Chicago to demonstrate the difference in default rate if the 5 risks are assumed to be totally independent and dependent.  It got me thinking as to how such a simplified example could illustrate the impact of applied correlation assumptions. Correlation between core variables are critical to many financial models and are commonly used in most credit models and will be a core feature in insurance internal models (which under Solvency II will be used to calculate a firms own regulatory solvency requirements).

So I set up a simple model (all of my models are generally so) of 5 risks and looked at the impact of varying correlation from 100% to 0% (i.e. totally dependent to independent) between each risk. The model assumes a 20% probability of default for each risk and the results, based upon 250,000 simulations, are presented in the graph below. What it does show is that even at a high level of correlation (e.g. 90%) the impact is considerable.

click to enlarge5 risk pool with correlations from 100% to 0%

The graph below shows the default probabilities as a percentage of the totally dependent levels (i.e 20% for each of the 5 risks). In effect it shows the level of diversification that will result from varying correlation from 0% to 100%. It underlines how misestimating correlation can confound model results.

click to enlargeDefault probabilities & correlations