Tag Archives: financial resilience

Homer-Dixon on Risk, Uncertainty and Crises

Think Globally Radio recently posted a number of great interviews. Here is one interesting one with political scientist, and renown author Thomas Homer-Dixon from University of Waterloo (Canada) – one of the world’s leading scholars on the intersection of environment, security and crisis.

Direct link to the interview can be found here.

Financial resilience – Taleb and Mandelbrot reflect on crisis

Nassim “Black Swan” Taleb writes on Edge about an unwillingness or consider and remember extreme events leads to financial disaster in The Fourth Quadrant: A map of the limits of statistics.

Statistical and applied probabilistic knowledge is the core of knowledge; statistics is what tells you if something is true, false, or merely anecdotal; it is the “logic of science”; it is the instrument of risk-taking; it is the applied tools of epistemology; you can’t be a modern intellectual and not think probabilistically—but… let’s not be suckers. The problem is much more complicated than it seems to the casual, mechanistic user who picked it up in graduate school. Statistics can fool you. In fact it is fooling your government right now. It can even bankrupt the system (let’s face it: use of probabilistic methods for the estimation of risks did just blow up the banking system).

The current subprime crisis has been doing wonders for the reception of any ideas about probability-driven claims in science, particularly in social science, economics, and “econometrics” (quantitative economics). Clearly, with current International Monetary Fund estimates of the costs of the 2007-2008 subprime crisis, the banking system seems to have lost more on risk taking (from the failures of quantitative risk management) than every penny banks ever earned taking risks. But it was easy to see from the past that the pilot did not have the qualifications to fly the plane and was using the wrong navigation tools: The same happened in 1983 with money center banks losing cumulatively every penny ever made, and in 1991-1992 when the Savings and Loans industry became history.

It appears that financial institutions earn money on transactions (say fees on your mother-in-law’s checking account) and lose everything taking risks they don’t understand. I want this to stop, and stop now— the current patching by the banking establishment worldwide is akin to using the same doctor to cure the patient when the doctor has a track record of systematically killing them. And this is not limited to banking—I generalize to an entire class of random variables that do not have the structure we thing they have, in which we can be suckers.

And we are beyond suckers: not only, for socio-economic and other nonlinear, complicated variables, we are riding in a bus driven a blindfolded driver, but we refuse to acknowledge it in spite of the evidence, which to me is a pathological problem with academia. After 1998, when a “Nobel-crowned” collection of people (and the crème de la crème of the financial economics establishment) blew up Long Term Capital Management, a hedge fund, because the “scientific” methods they used misestimated the role of the rare event, such methodologies and such claims on understanding risks of rare events should have been discredited. Yet the Fed helped their bailout and exposure to rare events (and model error) patently increased exponentially (as we can see from banks’ swelling portfolios of derivatives that we do not understand).

Are we using models of uncertainty to produce certainties?

…So the good news is that we can identify where the danger zone is located, which I call “the fourth quadrant”, and show it on a map with more or less clear boundaries. A map is a useful thing because you know where you are safe and where your knowledge is questionable. So I drew for the Edge readers a tableau showing the boundaries where statistics works well and where it is questionable or unreliable. Now once you identify where the danger zone is, where your knowledge is no longer valid, you can easily make some policy rules: how to conduct yourself in that fourth quadrant; what to avoid.

Now it lets see where the traps are:

First Quadrant: Simple binary decisions, in Mediocristan: Statistics does wonders. These situations are, unfortunately, more common in academia, laboratories, and games than real life—what I call the “ludic fallacy”. In other words, these are the situations in casinos, games, dice, and we tend to study them because we are successful in modeling them.

Second Quadrant: Simple decisions, in Extremistan: some well known problem studied in the literature. Except of course that there are not many simple decisions in Extremistan.

Third Quadrant: Complex decisions in Mediocristan: Statistical methods work surprisingly well.

Fourth Quadrant: Complex decisions in Extremistan: Welcome to the Black Swan domain. Here is where your limits are. Do not base your decisions on statistically based claims. Or, alternatively, try to move your exposure type to make it third-quadrant style (“clipping tails”).

Below I’ve redrawn Taleb’s figure.  His article provides a fuller picture.Taleb's quadrants

Similarly, Scientific American reprints Benoit Mandelbrot’s 1999  How Fractals Can Explain What’s Wrong with Wall Street:

Individual investors and professional stock and currency traders know better than ever that prices quoted in any financial market often change with heart-stopping swiftness. Fortunes are made and lost in sudden bursts of activity when the market seems to speed up and the volatility soars. Last September, for instance, the stock for Alcatel, a French telecommunications equipment manufacturer, dropped about 40 percent one day and fell another 6 percent over the next few days. In a reversal, the stock shot up 10 percent on the fourth day.

The classical financial models used for most of this century predict that such precipitous events should never happen. A cornerstone of finance is modern portfolio theory, which tries to maximize returns for a given level of risk. The mathematics underlying portfolio theory handles extreme situations with benign neglect: it regards large market shifts as too unlikely to matter or as impossible to take into account. It is true that portfolio theory may account for what occurs 95 percent of the time in the market. But the picture it presents does not reflect reality, if one agrees that major events are part of the remaining 5 percent. An inescapable analogy is that of a sailor at sea. If the weather is moderate 95 percent of the time, can the mariner afford to ignore the possibility of a typhoon?

Paul Krugman on Resilience Economics

On Paul Krugman’s Blog he presents a graphical model of the current financial crisis in the US that implicitly discusses how the system lost resilience. He identifies leveraged investments as a slow variable which can lead to the creation of alternative regimes, the possibility for a shock to flip the system from one regime to another, and now possibly a new regime.

Krugman RS

The other day I realized how much the Fed’s attempts to resolve the financial mess resemble sterilized foreign exchange intervention. That set me thinking about other parallels — and I realized how much the stories now being told about “systemic margin calls” and all that resemble the stories we all tried to tell about the Asian financial crisis of 1997-98. Leverage, balance sheet effects, self-reinforcing financial collapse — the details are different, but there are some clear common themes.

…Think of the demand for “securities” — lumping together all the stuff that’s in trouble, from subprime to Alt-A to corporate bonds, as if it were all the same. Ordinarily we’d think of a downward sloping demand curve. At a given point in time, there’s a fixed supply of these securities that has to be held by someone [Normal Situation]

But in the current situation, a lot of securities are held by market players who have leveraged themselves up. When prices fall beyond a certain point, they get calls from Mr. Margin, and have to sell off some of their holdings to meet those calls. The result can be a stretch of the demand curve that’s sloped the “wrong way”: falling prices actually reduce demand.

In this case, there are two equilibria, H and L. (there’s one in the middle, but it’s unstable) And this introduces the possibility of self-fulfilling panic: if something spooks the market, you can get a “systemic margin call” that causes the whole financial market to go to L, and causes a big, unnecessary price decline. [Highly leveraged investment]

Implicitly, Fed policy seems to be based on the view that if only they can restore confidence — with extra liquidity to the banks, Fed fund rate cuts, whatever — they can get us out of L and back to H. That’s the LTCM model: Rubin and Greenspan met a crisis with a rate cut and a show of confidence, and the whole thing went away.

But at this point a series of rate cuts and other stuff just hasn’t done the trick — which suggests that maybe there isn’t a high-price equilibrium out there at all. Maybe the underlying losses in housing and elsewhere are sufficiently large that the situation really looks like this [current situation?]

And in that case, the Fed can’t rescue the financial markets. All it — and the feds in general — can do is to try to limit the effects of financial crisis on the rest of the economy.