Value at Risk: Just Valid Enough to Be Dangerous
With an economy hooked on steroids, it is crucial that thinking not be replaced by statistics in controlling risk...
Value at Risk is a methodology adopted by most of Wall Street (this includes just about every entity such as hedge funds, dealers, pensions, insurance companies, etc. that invest in markets) to evaluate the "riskiness" of their portfolios.
Essentially, if the portfolio is found to have too little risk more risk is taken, especially in this income starved world, in order to generate higher returns. Another way to put that in real world terms is that as long as the risk taken can be documented and rationalized, all is fine.
VAR is inherently flawed as a panacea of risk control. It is a "chicken-before-the-egg problem" first and foremost; secondly, it is linear based which in no way describes market risk or movement (the market did this in the past, so it is likely to do this in the future). VAR of course is "other people's money" syndrome and therefore eventually their problem.
VAR is on one hand simple (the first indication of the problem), which is one reason it is so popular. It can easily be described to board members and plan sponsors, the people ultimately responsible for risk (responsible, but not liable), so they can sleep at night. On the other hand, it is derived out of option math, so it has enough fuzziness to earn a strange respect and assurance from those that really don't understand its limitations.
VAR is just valid enough to be dangerous.
I have explained in previous discussions how implied volatility is calculated; VAR is an extended version of that. Basically take any group of assets such as a group of stocks, or a group of asset classes, or a group of hedge funds, and begin to collect data over time. Price (or performance) of those assets is calculated interdependent of each other to determine a "volatility" of that group. The less correlated those assets are to each other the less the resulting volatility will tend to be.
Once volatility is established, an estimate is made as to how much that group (portfolio) of assets can lose given the statistics. If a 5% volatility number is established, a potential loss is associated with that number (the portfolio can be up or down 5% in a year). Most entities use one-standard deviation (66% of observations) to calculate their "risk". If they used two-standard deviations, they would look at 98% of all observations and that 5% would basically double.
Here is where the chicken-or-egg thinking comes in. If a risk manager decides that 5% is too low (which many do as they rationalize the fact that they need to make more money), they transact so as to increase risk. But the process itself of taking more risk occurring over many managers time and time again in and of itself drives volatility down. Come next quarter the manager finds himself back at the 5% number so it is decided to take even more risk. In other words, this over-simple measure of risk causes common sense to go out the window, replaced by a linear mathematical model that re-enforces itself.
But the only thing happening is people taking more and more risk while they slowly box themselves into a corner for an eventual mean-reverting move in volatility (I call this compression). Normally this move occurs only from an increase in the correlation of assets, something most ignore (or don't understand) until it is too late. When volatility increases, very quickly this model tells them that they are taking too much risk and will have to reduce it. The rush to reduce it creates more volatility. Where is the common sense in that?
I have mentioned that the Fed has actually targeted volatility, desiring to reduce it so that investors take more risk. Remember, taking risk is inflationary while avoiding it is deflationary. A prime example of the Fed doing this is ARMs. Remember when Greenspan pointed out to home-owners how much money they were giving away in interest by holding fixed mortgages instead of flipping into adjustable rate mortgages? Why was this done?
When a home-owner holds a fixed mortgage, they hold an option, a very valuable one. At any time they can refinance when rates go lower (for a fee). This option makes them long volatility, a position home-owners have held for decades. Lenders are short this option. We have explained this in detail when talking about Fannie Mae (FNM): when prepayments occur because of refinancing, FNM must rebalance their duration and buy bonds as they are rising in price (yield falling). The more rates move around the more FNM (or any lender holding a mortgage as an asset), the more FNM must adjust; each time they do it is a cost.
When home-owners en mass flipped into ARMs, they became sellers of volatility (they gave up this option) and lenders became long volatility (exchanged convexity risk for default risk). The absolutely massive amounts of ARMs significantly reduced volatility in the bond market, which transferred into all markets.
The Fed used the home-owner to reduce volatility. VAR went down and investors took more risk.
The second problem is linearity. As all mutual funds warn (disclaimer) that past results are not indicative of future ones, they choose to ignore the fact when applying to risk. All these correlations and volatility statistics are in the past, but crucially, the statistics weight the times when correlations break down and volatility increases the same as all the other data points. This is the fallacy for when they break down they are normally such significant events that they force rapid change of risk.
With an economy hooked on steroids (our best guess is that M3 is growing at an astounding 10%), it is crucial that thinking not be replaced by statistics in controlling risk; no matter how much Wall Street tries.
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