As pointed out in a recent Financial Times opinion piece by Nassim Nicholas Taleb and Mark Spitznagel (see FT article, concepts also expressed in a recent CNBC interview), the core economic problem that we are facing "is that our economic system is laden with debt." In fact, as pointed out by the authors, the debt load is about triple the amount relative to the GDP levels of the 1980s. Given that Tabel and his colleague have been betting on debt-induced hyperflation becoming the next black swan event (see previous post), and making them even more coin in the process, it might be easy to dismiss this as someone simply talking their book - which is probably somewhat the case. Yet the levels of the deficit spending and debt are unprecedented, and scary. Of course, what is possibly even more shocking is how making these levels know and pointing out their consequences is still looked at as a revelation, or at least finally drawing serious concern. It is simply no longer enough to point out the irony of using debt to solve a problem caused by too much debt. That train has already left the station. The focus is finally shifting to those trying to slow down the train before we all get run over.

As pointed out in the FT article, Taleb and Spitznagel believe the only solution to the debt problem is to immediately convert debt to equity. After all, companies in bankruptcy do this all the time - then again, I am not sure what that says about a country and its credit rating [Note: As a follow-up, see the recent Felix Salmon Reuters blog post about the unsustainability of debt-to-equity conversion]. To bolster their case, the authors given three reasons for their concern and reasoning. First, debt and leverage cause the system to become fragile - i.e., there is less room for error. Second, globalization has caused the system to be more complex, which in turn has caused business parameters to be more volatile. Third, and somewhat novel in perspective, is that debt is "highly treacherous." Loans hide volatility since they do not really vary outside of default. Such risk is hidden even more in highly complex derivative products, such as swaps and CDOs.

So what additional steps can governments do to reverse the trends? Tabel and Spitznagel list two options: deflate debt or inflate assets (once again, the authors are betting on the later). What have governments done? Deficit-based stimulus spending. And they are considering more (see previous post). Besides adding more debt, stimulus spending is likely to over- or undershoot since it is difficult to get just right in size and timing. This of course leaves economies vulnerable to inflation, and in some cases creates hyperinflation. Therefore, unless the levels of consumer and government debt are dealt with, and we consider other approaches for dealing with current problems, we are likely to experience another black swan - even one that is large and can be seen flying right towards us.

Nassim Nicholas Taleb, author of the Black Swan, is using the impact of extreme events mentioned in the Black Swan to help the hedge fund he advises, Universa Investments L.P., benefit in October (see WSJ article). Separate funds in the Universa's Black Swan Protection Protocol were up between 65 percent to 115 percent in October alone. The fund has a strategy of buying far-out-of-the-money put options on stocks and stock indexes. Most of the time the fund will take small loses when nothing unusual happens, but occasionally a black swan even occurs (such as the recent 20% market decline in one month), causing the gains to be extraordinary. In addition to using deep out-of-the-money puts, the fund has a strategy of keeping more than 90% of its assets in cash or cash equivalents, and is believed to break even or only incur small losses while waiting for the next black swan event. The fund recently made huge profits buying cheap puts on the S&P 500 and AIG, with the S&P puts increasing in value over 50-fold. While profitable during times of extreme volatility changes, one has to wonder how often such changes and moves will occur. Even a previous fund that Taleb was involved with had to shut down in 2004 after lower volatility caused returns to suffer and investors to flee. But then again, a 50-fold increase in a few trades gives you time to wait for the next event. You just need to be patient, and of course, know where to look as you wait for the worst to happen. Easier said than done.

There is a nice article at the Financial Times giving an introduction to Value-at-Risk (VaR), along with a discussion of the good and bad aspects of using VaR for risk management. A few illustrative examples are also given. It is worth a read for those without much math background, or those who would like a quick and simple introduction and explanation of VaR.

In general, VaR calculations find the maximum loss that is not exceeded for a defined probability over a given period of time. Sound confusing already? Let us put it another way. As an example, one VaR calculation might find that we are "95% confident that we will not lose more than $1 million over the next month." In other words, the most we expect to lose this month is $1 million, and we are 95% confident that losses will not exceed this figure. Past normally distributed return and volatility values of our asset or portfolio allow us to make such a calculation.

While the calculation is useful and intuitive, it is not without its problems. Worth noting from the article is how VaR is backward looking, such that if the distribution of volatility and stock returns change, the values given by the VaR calculation will over- or under-estimate the risk. To have more confidence in your calculations, it is important to make sure you are using past data that is similar to the data in the current time frame you are concerned with.

VaR is also not designed in its basic form to deal with what are increasingly being called “black swans,” made popular by Nassim Nicholas Taleb in his book of the same name - The Black Swan: The Impact of the Highly Improbable. In essence, a black swan is a hard to predict event that is rare and beyond the current level of expectations, but when it occurs, it has the ability to not only be relatively unique, but also carry a large impact. The events of 9-11, or the recent subprime credit events leading to the problems at Bear Stearns and elsewhere are such events. These events are difficult to predict, and not often seen (like a black swan), but nonetheless have lasting effects. [For what it is worth, both of Taleb's books related to the subject - The Black Swan and Fooled by Randomness - are worth your time].

Another problem with VaR that is often discussed concerns herd mentality. During an event, like a market meltdown, if everyone moves in the same direction and performs the same task (such as panic selling), they are essentially moving to the same location on the normal distribution. This in and of itself will change the curve. What before looked like an extreme event is now much more probable. As such, your level of risk and exposure will also increase.

Personally, while teaching VaR and performing my own calculations for finance organizations, I find that helping them understand the math and basic concepts is relatively easy. This is especially true for graduate students in finance, or those with a basic background in statistics, such as engineers and computer scientists, among others. What becomes difficult to teach and put into practice is understanding the proper distribution of a complex portfolio, especially one that includes non-linear derivative securities. Furthermore, getting a proper historical data set to model the distribution and calculate VaR can be difficult. Linear approximation and non-linear quadratic models have been developed and are often used, but they are also difficult to formulate, or at times require unrealistic generalization and/or assumptions.

Of course, sometimes the math itself gets misused, or is misunderstood. The Financial Times article gives an example offered by David Einhorn, a New York hedge fund manager. For instance, assume that "... you are offered odds of 127 to one on $100 that when you toss a coin, heads will not come up seven times in a row. The chance that you will win is 99.2 per cent. So you can say with 99 per cent confidence that you have no value at risk. Using a VaR model, a bank could hold no capital to guard against a loss on this bet. But in fact there is a 0.8 per cent chance (not an unimaginable black swan) that they will lose $12,700."

In other words, it is unlikely that you will incur a loss, therefore regulators will allow you to hold less capital. Of course, if you do happen to fall into the tail of the distribution, your loss would be significant, so significant that it could take down your company. When derivatives are used, and the non-linearity of options, complex swaps, CDOs, etc., are considered, the tail can become not only long, but also have a bump in it. This has the effect of further magnifying the potential loss and value that is at risk. In fact, the tail can be designed to be even less likely to occur (narrow and farther out, but having a taller bump), making the company look even less risky, but if the event does occur ........ , well, you get the picture. Unfortunately, this type of risk is hard to model, and even harder to understand. It also allows for abuse since it creates incentives to take excessive risk, albeit more remote. No doubt we are seeing the effect of this in the credit markets and elsewhere as we speak.