Derivatives VIII --- CDS and CDO

Before getting into these two products, we need to understand what credit does. Credit is like glue that holds banks together. If you take credit risk, i.e., you lend money to others but they don’t pay you back, you are in trouble. The risk is removed until they pay you back. This can be a very long period of time. Credit agencies play the words of “A” (the company will pay you back) and “B” (they may pay you back).

Because of the credit risk, there must be a way to diversify the risk. Here comes the first credit derivatives: credit default swap (CDS). The idea of CDS is like this: assume that a bank has made a loan to a client. The banks want to sell the risk on the loan because it has too much exposure to the loan. So the bank finds someone who wants to take the risk. Someone likes the client company and doesn’t think the client would default, or he simply is unaware of the risk. So the bank and the investor enter into a CDS. The bank pays the investor a fee. In return, the investor agrees to indemnify the bank against losses if the company defaults. CDS ensembles a guarantee against bankruptcy.

Derivatives VII --- Structured Products

Structured financial products are repacked products so that something are wrapped under a coat. What are the “something”? Gold and Junk, depending on what you’re looking at. If you can’t see through the coat, which one of the goals of setting up structured products, then everyone is happy. The bank is happy because they have successfully wrap up dead asset and sell. The customer is also happy because he thinks he gets a good deal.

Inverse floaters, arrears resets, currency linked bonds and others are the underlying of many structured products. Again, the aim is to find investors who want to take risk to get higher return. They become even more complicated because every dealer has their own names on the essentially same product. Every dealer can issue their own products, which are quite similar to others. So the key of the game is to stay ahead of the competition. You can’t protect your IP.

Because there are many structured products are packed together, one problem is to separate different pools of assets and derivatives that underlay each individual transaction within the issuer. Lawyers device a system of mortgages, charges and non-recourse agreement to create separate pools of assets for each product. The non-recourse agreement says that the investor agrees to limit any claim against the issuer to the identified assets and derivatives. In other words, the issuer is immune to lawsuits. An implication of CDS is that after the bank gets rid of the risk, the bank can benefit greatly in the company’s failure. But more than this simple idea, CDS has complicated regulations by ISDA (international swap and derivatives association)

After CDS took off, CBO, collateralized bond obligation --- the predecessor of CDO, collateralized debt obligation was invented by Michael Milliken. CBOs were used to repackage junk bonds. Regulations required insurance companies holding junk bonds to provide lots of reserves against the investment. To get around the rule, insurance companies repackaged high yield assets into CBOs and transferred the riskier parts to their holding companies (which didn’t have to hold reserves). Now the companies are attached to high risk bonds in other forms (may not be recognized by the companies).

Banks also sell accumulated loans in SPV (special purpose vehicle) to distribute mortgage loan risk. SPV pays the bank in the form of loan they purchased. This is called MBS (mortgage backed securities). The bank continues to collect payment and the payments are passed to SPV to pay interest and principle on the MBS. Mortgage owners don’t realize these structures at all. Other variants to this format exist.

Derivatives VI --- Models

Finance’s become quantitative. So models are needed. That is also what Quant is called for financial engineers. Quants analyze and develop new products and trading ideas. Quants drifted to trading rooms to model financial instruments.

Other than their work nature, i.e., research and analytical skills, quants are quite different to traders. Quants are useful particularly in derivatives markets. But they are still serving traders and rely on traders’ recognition. Emmanuel Derman’s book “My Life as A Quant” described what a quant’s life is. Quants would love to become traders.

Financial models are not as secretive as it sounds. Many universities provide financial engineering courses. For example, buying forwards is the same as buying now and holding the asset until the maturity date. Adjustment has to make to reflect what the borrowing cost is. This is the carry cost model. Another frequently used case is the yield curve. Interpolation and extrapolation have to be used to know where the curve is likely heading. One case almost everyone has to face is options pricing model.

Derivatives V --- Risk Management

Traders and banks take risks to make money and risk management is about measuring and controlling risks.

Risk is managed by numbers. But how to pick relevant and convincing numbers are more of art. For example, how a bank’s earning is affected by interest rate. To select an interest rate gives others convincing signal that the rate change would occur but also you don’t want to pick a rate very unlikely occur so that it would scare others not to take risk.

Banks’ management should understand derivatives and risks thoroughly. If not, at least can spell these two words (plural in derivatives). Risks are 4 groups: market risk, credit risk, liquidity risk, and operation risk. They are quite self-explained. For banks, credit risk, liquidity risk and operation risk are the first three ranking risks. Market risk is less serve. On the other hand, non-financial entities are exposed to much higher market risk.

Risk is measured in terms of VAR --- value at risk. VAR calculates the distribution of price changes in the past. It signifies the maximum amount that you could lose as a result of market moves for a given probability over a fixed time. For example, a VAR of $50M at a 99% 10 day holding period means that the bank has 99% probability that it will not suffer a loss of more than $50M over a 10-day period. The probability is calculated from the famous Gaussian distribution to price movement.

The assumption of Gaussian distribution on price movement brings in doubters on VAR. Price movements are not Gaussian distributed. VAR did have times of flaw such as in 1998 Crisis where the historical data was too mild to predict the scale of the storm. Another classic example of VAR’s failure is LTCM. So another theory, called EVT, stands for extreme value theory, was born. It is similar to the Stress Tests the government just did on the 19 largest banks. The goal is to see how the banks can survive if the worst conditions occur. It came from physical sciences.

Besides financial risks, others are harder to measure. For example, ERM (enterprise risk management) encompasses personnel risk, operation risk, system risk, and legal risk. These are hard to quantified. The hardship doesn’t mean they can’t be measured but just mean the outcome is difficult to validate.