The following column appears in the Fall 2009 issue of Financial Partner magazine:
The Financial Crisis of 2008: What went wrong?
What caused last fall’s financial crisis? What lessons can we learn?
In this column, I discuss one of many contributing factors: The mathematical models that financial institutions used to manage risk were not up to the task. Don’t worry, this column isn’t about mathematics. It’s about the risks of thinking we know more than we actually do.
Some things in our world can be calculated with precision and some cannot. Farmers know this intuitively. We can calculate exactly what time of day and where on the horizon the sun will rise three weeks from next Tuesday. (I use “we” loosely. I mean that someone in our society is capable of such a calculation, and the rest of us can look it up on the Internet.) But we cannot calculate with any certainty at all whether that sunrise will be visible or obscured by a cloud.
Can financial risk be precisely calculated like the motions of heavenly bodies? Or is it as unpredictable as the weather?
A typical way to measure financial risk is in terms of the volatility of the price of something. The “something” can be almost anything that can be owned, such as stocks, bonds, commodities or real estate. Or it can be a derivative financial instrument based on such things as stocks, bonds, commodities or real estate. Trillions of dollars of such assets are traded regularly. One can observe the prices at which they trade and measure changes (or volatility) in those prices.
The next step is to estimate the probabilities of future price changes. There is a high probability of small price changes which represent little risk. Conversely, large price changes represent significant risk but occur infrequently.
We can calculate these probabilities using modern financial theory. (Again, I use “we” loosely.) The mathematics involved is well beyond high school algebra, so Wall Street employed an army of mathematics PhDs.
Even those of us who don’t understand higher mathematics often make investments based on information that incorporates the mathematics of modern financial theory, such as public ratings on bonds and other debt instruments. The result is that investors made trillions of dollars of investment decisions thinking that they understood the risk in their portfolios.
Alas, events proved otherwise. Many investments were far riskier than the mathematical models predicted. Large price movements occurred more frequently than expected and the theory underpredicted the likelihood of extreme events. As a result, several large financial institutions suffered unexpected losses that they couldn’t absorb, resulting in failure. And this cascaded through the financial system, causing more failures.
What are the lessons?
Should we tar and feather the mathematicians? Well, no. Mathematics is useful and successfully explains significant portions of our world. But we got ahead of ourselves in thinking that we fully understood the mathematics of risk. One very costly lesson learned is that we should be more cautious when thinking that we understand our world. Another lesson is that many financial institutions should hold more capital. The financial institutions that failed thought they had sufficient capital based on their mathematical models. Unfortunately, they were wrong.
At June 30, 2009, Yankee’s permanent capital ratio was 18.2 percent. While no amount of capital completely guarantees against all events, you should take comfort that this level is considerably higher than most other financial institutions, both inside and outside the Farm Credit System.
More discussion
The following two posts on this blog are also on this topic:
Risk models - some math
Risk models - more information
