Currency Wars_ The Making of the Next Global Crisis - James Rickards [109]
A similar fractal pattern appears whether the chart is magnified to cover hours, days, months or years, and similar results come from looking at other charts in currency, bond and derivatives markets. Such charts show price movements, and therefore risk, distributed according to a power law and chart patterns with a fractal dimension significantly greater than 1.0. These features are at odds with a normal distribution of risk and are consistent with the power-law degree distribution of events in complex systems. While more work needs to be done in this area, so far the case for understanding capital markets as complex systems with power-law degree distributions is compelling.
This brings the analysis back to the question of scale. What is the scale of currency and capital markets, and how does it affect risk? If catastrophic collapses are an exponential function of scale, then every increase in scale causes a much greater increase in risk. Capital markets continually increase in scale, which is why the black swans keep coming in greater numbers and intensity.
Thinking about scale in capital markets today is like trying to measure the size of a field before the invention of the foot, the yard or the meter. There is no commonly agreed scaling metric for computing market risk using complexity and critical state dynamics. This lack is not unprecedented. Earthquakes have been known throughout history, yet the Richter scale used to measure the intensity and frequency of earthquakes was invented only in 1935. Earthquakes are phase transitions in complex tectonic plate systems, and their frequency and intensity measured by the Richter scale also correspond to a power law. The similarity of stock market charts to seismographic readings (seen in Figure 3 below) is not coincidental.
FIGURE 3: A sample seismograph reading
It will take some time for empirical work to catch up to theoretical work in this field. However, Nobel Prizes in economics likely await those who discover the best scaling metrics and accurately compute the slope of the power curve. But there is no need to wait for that work before drawing sound conclusions from the theory. Putting buildings on a known fault line was a bad idea even before the Richter scale was invented. Ignoring complexity and power laws in capital markets is a bad idea today even in the absence of empirical perfection. The edifice of capitalism may collapse in the meantime.
Even now one can make valuable inferences about the statistical properties of risk in capital and currency markets. There is no question that the scale of these markets, however best measured, has increased dramatically in the past ten years. A series of exchange mergers have created global megaexchanges. Deregulation has allowed commercial banks and investment banks to combine activities. Off–balance sheet activities and separate conduit vehicles have created a second shadow banking system as large as the visible system. Between June 2000 and June 2007, just prior to the start of the market collapse, the amount of over-the-counter foreign exchange derivatives went from $15.7 trillion to $57.6 trillion, a 367 percent increase. Between those same dates, the amount of over-the-counter interest rate derivatives went from $64.7 trillion to $381.4 trillion, a 589 percent increase. The amount of over-the-counter equity derivatives went from $1.9 trillion to $9.5 trillion in that same seven-year period, an increase of 503 percent.
Under Wall Street’s usual risk evaluation methods, these increases are not troubling. Because they consist of long and short positions, the amounts are netted against each other under the VaR method. For Wall Street, risk is always in the net position. If there is a $1 billion long position in a security and a $1 billion short position in a highly similar security, methods such as VaR will subtract the short from the long and conclude the risk is quite low, sometimes close to zero.