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The Flaws of Finance
GMO
By James Montier
May 14, 2012


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This paper is based on a speech delivered at the 65th Annual CFA Institute Conference in Chicago on May 6, 2012. As a child, watching my parents write postcards whilst we were all on holiday was an instructive experience. My mother would meticulously write out the card, scattering a few interesting holiday tidbits within the text. My father, whose sum total of postcards sent was invariably just one (to his office), opted for a considerably more efficient approach. His method is shown at the left in Exhibit 1.

I think we can construct a similar diagram to explain the Global Financial Crisis (GFC), represented at the right in Exhibit 1. In essence, the GFC seems to have sprung from the interaction of the following four “bads”: bad models, bad behaviour, bad policies (which is really just bad behaviour on the part of central banks and regulators), and bad incentives.

In an effort to rethink finance, I want to examine each of these factors in turn, beginning with bad models.

Bad Models, or, Why We Need a Hippocratic Oath in Finance

The National Rifle Association is well-known for its slogan “Guns don’t kill people; people kill people.” This sentiment has a long history and echoes the words of Seneca the Younger that “A sword never kills anybody; it is a tool in the killer’s hand.” I have often heard fans of financial modelling use a similar line of defence.

However, one of my favourite comedians, Eddie Izzard, has a rebuttal that I find most compelling. He points out that “Guns don’t kill people; people kill people, but so do monkeys if you give them guns.” This is akin to my view of financial models. Give a monkey a value at risk (VaR) model or the capital asset pricing model (CAPM) and you’ve got a potential financial disaster on your hands.

The intelligent supporters of models are always quick to point out that financial models are, of course, an abstraction from reality. Just as physicists can study worlds without frictions, financial modelers should not be attacked for trying to reduce the complexity of the “real world” into tractable forms.

Finance is often said to suffer from Physics Envy. This is generally held to mean that we in finance would love to write out complex equations and models as do those working in the field of Physics. There are certainly a large number of market participants who would love this outcome.

I believe, though, that there is much we could learn from Physics. For instance, you don’t find physicists betting that a feather and a brick will hit the ground at the same time in the real world. In other words, they are acutely aware of the limitations imposed by their assumptions. In contrast, all too often people seem ready to bet the ranch on the flimsiest of financial models.

Someone intelligent (if only I could remember who!) once opined that rather than breaking the sciences into the usual categories of “Hard” and “Soft,” they should be split into “Easy” and “Difficult.” The “Hard” sciences are generally “Easy” thanks to the ability to perform repeated controlled experiments. In contrast, the “Soft” sciences are “Difficult” because they involve trying to understand human behaviour.

Put another way, the atoms of the feather and brick don’t try to outsmart and exploit the laws of physics. Yet financial models often fail for exactly this reason. All financial model underpinnings and assumptions should be rigorously reviewed to find their weakest links or the elements they deliberately ignore, as these are the most likely source of a model’s failure.

Let’s take the CAPM as an example of the way in which behaviour is assumed to be exogenous in finance, and how this creates problems. The CAPM can be constructed from four assumptions:

1. Transaction costs and other illiquidities can be ignored.

2. All investors hold mean-variance-efficient portfolios.

3. All investors hold the same correct beliefs about mean variance and covariances of securities.

4. Every investor can lend all she or he has, or can borrow all she or he wants at the risk-free rate.

In case you object to the idea of a risk-free rate in assumption 4, this can be replaced by the following:

4a. It is possible to take a long or short position of any size in any risky asset.

Essentially this model says that the only “risk” is volatility (assumption 3), that illiquidity can be ignored (assumption 1), and that leverage is freely available and can be deployed without any consequences (assumption 4 or 4a). Those following this model will seek to leverage up illiquid assets. We have seen this movie before! Anyone remember the saga of Long-Term Capital Management?

Their business model (largely informed by more complex versions of the CAPM) involved trades such as buying off-the-run government bonds (e.g., 9½-year bond), and shorting the on-the-run equivalent bonds (e.g., 10-year benchmark bond), and then leveraging up. Basically, they were applying leverage to an illiquid asset. The dénouement for the LTCM movie is displayed in Exhibit 2.

 

The atoms (market participants) of financial models are not inert. They either ignore the weaknesses of the model or actively seek out and exploit the model’s weak links.

In the GFC it wasn’t CAPM, but rather models such as Value-at-Risk (VaR) that created problems. It is noteworthy that VaR has been a villain in financial crises before – it was, for example, LTCM’s chosen form of risk management. Its problems have been known for a long time. Indeed all of the “bads” identified in this note had been discussed by many, including me, ahead of the crisis, so this is far from an exercise in hindsight bias.

Using VaR is like buying a car with an airbag that is guaranteed to fail just when you need it, or relying upon body armour that you know keeps out 95% of bullets! VaR cuts off the very part of the distribution of returns that we should be worried about: the tails.

Exhibit 3 shows the feedback loops embedded within a typical VaR approach. Most VaR calculations use trailing volatility and correlation inputs. When these decrease, the calculated VaR falls, allowing the users to increase their leverage as they now have “less risk.” Of course, the reverse is also true. When volatilities and correlations rise, VaR will also increase, and the users will be forced to deleverage. If everyone is using VaR, the potential for system wide problems is clear, as the model itself acts as a transmission mechanism between institutions (a classic example of Minsky’s adage that stability begets instability).

The problems inherent in VaR are further amplified by the use of short runs of data to estimate the inputs. This creates an even more pro-cyclical element, adding to the problems of VaR. If the immediate past is a period of tranquillity, then the future is held to be the same. If a risky asset, let’s say a CDO, happens to have been less volatile than U.S. treasuries over the last couple of years, the model says (with a straight face) that the CDO is less risky than treasuries!

VaR is extremely vulnerable to peso problems. Peso problems are really just situations where the proverbial poop [Ed: No, James did not use this phrase.] has yet to hit the fan. For instance, if you are running a currency carry trade and buying currencies with high interest rates and shorting those with low interest rates, your returns may look great as long as no devaluations occur. However, the high interest rates may simply be compensation for an expected devaluation. When this occurs your returns are often annihilated (see Exhibit 4). Yet in the run-up to the devaluation, a VaR approach will say you have essentially no risk!

To continue reading, go here.

 

 

(c) GMO
www.gmo.com


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