There is currently a belief that financial risk is easily measured. That we can stick some sort of risk-meter into the financial system and get an precise measurement of the risk of complex financial instruments. The poorly misguided belief that this risk-meter exists plays a key role in getting the financial system into the mess it is in. Of course, nothing has been learned. In a sense, we are trying to put wings on pigs and throwing them up in the air. (Don’t try this at home, I am a professional trained pig tosser) Risk sensitivity is expected to play a key role both in the future regulatory system and new areas such as executive compensation.
So where does this belief come from? Perhaps the risk-meter is incredibly clever – after all, it is designed by some of the smartest people around, using incredibly sophisticated mathematics. Perhaps this belief also comes from what we know about physics. After all, aerodynamics proves pigs aren’t the best animal to be airborne. By understanding the laws of nature and pig bellies, engineers are able to create the most amazing things. If we can leverage the laws of nature into an Airbus 380, we surely must be able to leverage the laws of finance into a CDO.
This is false. The laws of finance are not the same as the laws of nature. The engineer, by understanding physics, can create structures that are safe regardless of what natures throws at them because the engineer reacts to nature but nature does not generally react to the engineer. Simply said, pigs don’t have wings.
In physics, complexity is a virtue. It enables us to create supercomputers and iPods. In finance, complexity used to be a virtue. The more complex the instruments are, the more opaque they are, and the more money you make. So long as the underlying risk assumptions are correct, the complex product is sound. In finance, complexity has become a vice.
We can create the most sophisticated financial models, but immediately when they are put to use, the financial system changes. Outcomes in the financial system aggregate intelligent human behavior. Therefore attempting to forecast prices or risk using past observations is generally impossible. This is what Hyun Song Shin and I called endogenous risk.
Because of endogenous risk, financial risk forecasting is one of the hardest things we do. In my recent paper, I tried what is perhaps the easiest risk modeling exercise there is – forecasting value-at-risk for IBM stock. The resulting number was about plus or minus 30 per cent accurate, depending on the model and assumptions. And this is the best case scenario. Trying to model the risk in more complicated assets is much more inaccurate. Plus or minus 30 per cent accuracy is the best we can do.
The inaccuracy of risk modeling does not prevent us from trying to measure risk, and when we have such a measurement, we can create the most amazing structures Unfortunately, if the underlying foundation is based on sand, the whole structure becomes unstable.
** No pigs were harmed in the writing of this post **