Bad Bank
We are all in agreement that we prefer a government that acts and tries to find solutions than one that sits back and does nothing while the economy collapses.
However, we should discuss and analyse the plan of action before jumping to conclusions.
We are all aware of the gangrene that banks have in their balance-sheet, toxic-loans that are still being hidden in “for sale books”, amongst other accounting tricks and manoeuvres. And just like gangrene, banks should cut their losses before it spreads. Some will be left standing and alive, while others may lose a leg, an arm, or even their lives in the process.
That is what gangrene does when you leave it rotting inside you.
Like a fatherly figure the government, although with best intentions, is offering to hold the gangrene on behalf of the banks. Like a transplant, to cut the gangrene out from banks and insert it in the state.
No matter who holds it, it is still gangrene!
It is naïve to think that healthy tissue won’t get contaminated by it, or that healthy debt won’t get contaminated by the toxic debt.
Default probabilities are normally computed by correlation matrix, meaning that when assets belong to corporations from the same sector, geographical region, and credit rating they are likely to suffer the same macro-economic influences and therefore likely to hold some type of correlation between them. This methodology is used to estimate probabilities of default, and ultimately how we calculate credit spreads.
Credit Ratings are not instantaneous, nor efficient, they may take months to get updated by credit agencies, and sometime may not reflect the real financial situation of the company (i.e. Lehman Brothers that only in September 2008 had its credit rate cut by Moody’s from A2 to a B3, which is not that bad for a bank that is about to open bankruptcy).
Besides these inefficiencies and time-lags, recovery rates also are not calculated correctly. Most models assume that recovery rates are dependent on the seniority of debt or collateral and do not respond to systematic factors, therefore they do not attract risk-premia and are considered independent of probability of default.
There are three main variables when estimating credit risk:
Expected default frequency (or probability of default), exposure (or risk) and loss given default (which is one minus recovery rate).
We denote,
EL – Expected Loss
EDF – Expected Default Frequency or PD (Probability or Default)
LGD – Loss Given Default
RR – Recovery Rate
R100 – Exposure (i.e. DV100
where,
LGD = 1-RR
EL = R100 * EDF * LGD = R100 * PD * (1-RR)
The recovery rate is a key variable to price and estimate credit risk. One would assume that accurate models are in place to estimate and forecast recovery rates.
However, most literature and academic work has been focused on developing models to estimate accurately the probabilities of default: reduced form (exogenous) and structural (endogenous), i.e. Merton Model, Moody’s KMV.
Over the last eight years more studies have appeared focused on RR estimation and relationship between RR and PD.
Frye (2000a and 2000b), Jarrow (2001), Hu and Perraudin (2002), Jokivuolle and Peura (2003), Carey and Gordy (2003), Bakshi et al. (2001), Altman, Brady, Resti and Sironi (2001 and 2004), and Acharya, Bharath and Srinivasan (2003).
Moreover, evidence from many countries, in recent years suggests that collateral values and recovery rates can be volatile and tend to go down just when the number of defaults goes up in economic downturns
Schleifer and Vishny (1992), Altman (2001), Hamilton, Gupton and Berthault (2001).
These new and more realistic models which estimate RR, are not being used by banks to price their assets yet. Therefore, in reality the toxic-debt hiding in their books is even worse than they actually have assumed or estimated it to be. Banks of course are aware of this and do not intend or wish to implement the changes necessary to price correctly their assets, as this would cause them to take bigger reserves for the positions they hold. Given that they are short of cash, reporting such a huge loss (based on methodology choice) wouldn’t be in their best interest. Instead it is easier to justify to regulators that the methods utilised are benchmark methods, and therefore they must be accurate.
It has been since the very start pure misconduct; lending even when the borrower could not possibly pay the loan back (mortgages that at times were over 10 times people’s income), and now turning a blind eye to the mispricing of their toxic-assets.
Taking the toxic-assets out of banks and moving it to a state owned bank, won’t free up any significant amount of cash (or reserves) to be lent back into the economy. It is somewhat optimistic to think that would be the case. Banks have already chosen carefully the most appropriate ways to report all toxic-assets priced at par (or close to it) by either using accountancy, regulatory or legal manoeuvres that enable them to lawfully declare these debt at much higher prices than they are actually worth, without breaching any compliance or regulatory guidelines.
Hence, Lehman, Goldman Sachs, Barclays and others were considered to have a much higher credit rating than they actually deserved. Most banks real finances are disguised under so many layers that revealing the truth is almost mission impossible.
Instead the government might like to consider simplifying and tightening the financial sector’s regulations, increase legal penalties for negligence or misconduct, and propose a “qualified certification” scheme to control and monitor professionals within the banking system.
by Andrea Graves
16.Jan.2008
