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Authors: James Rickards
Tags: #Business & Economics
Currency Wars: The Making of the Next Global Crisis (32 page)
BOOK: Currency Wars: The Making of the Next Global Crisis
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Much of the work on capital markets as complex systems is still theoretical. However, there is strong empirical evidence, first reported by Benoît Mandelbrot, that the magnitude and frequency of certain market prices plot out as a power-law degree distribution. Mandelbrot showed that a time series chart of these price moves exhibited what he called a “fractal dimension.” A fractal dimension is a dimension greater than one and less than two, expressed as a fraction such as 1½; the word “fractal” is just short for “fractional.” A line has one dimension (length) and a square has two dimensions (length and width). A fractal dimension of 1½ is something in between.
A familiar example is the ubiquitous stock market chart of the kind shown in daily papers and financial websites. The chart itself consists of more than a single line (it has hundreds of small lines) but is less than an entire square (there is lots of unfilled space away from the lines). So it has a fractal dimension between one and two. The irregular pattern of ups and downs is an emergent property and a sharp crash is a phase transition.
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.
subtract
the short from the long and conclude the risk is quite low, sometimes close to zero.
Under complexity analysis, the view is completely different. In complex systems analysis, shorts are not subtracted from longs—they are
added
together. Every dollar of notional value represents some linkage between agents in the system. Every dollar of notional value creates some interdependence. If a counterparty fails, what started out as a net position for a particular bank instantaneously becomes a gross position, because the “hedge” has disappeared. Fundamentally, the risk is in the gross position, not the net. When gross positions increase by 500 percent, the theoretical risk increases by 5,000 percent or more because of the exponential relationship between scale and catastrophic event size.
added
together. Every dollar of notional value represents some linkage between agents in the system. Every dollar of notional value creates some interdependence. If a counterparty fails, what started out as a net position for a particular bank instantaneously becomes a gross position, because the “hedge” has disappeared. Fundamentally, the risk is in the gross position, not the net. When gross positions increase by 500 percent, the theoretical risk increases by 5,000 percent or more because of the exponential relationship between scale and catastrophic event size.
This is why the financial system crashed so spectacularly in 2008. Subprime mortgages were like the snowflakes that start an avalanche. Actual subprime mortgage losses are still less than $300 billion, a small amount compared to the total losses in the panic. However, when the avalanche began, everything else was swept up in it and the entire banking system was put at risk. When derivatives and other instruments are included, total losses reached over $6 trillion, an order of magnitude greater than actual losses on real mortgages. Failure to consider critical state dynamics and scaling metrics explains why regulators “did not see it coming” and why bankers were constantly “surprised” at the magnitude of the problem. Regulators and bankers were using the wrong tools and the wrong metrics. Unfortunately, they still are.
When a natural system reaches the point of criticality and collapses through a phase transition, it goes through a simplification process that results in greatly reduced systemic scale, which also reduces the risk of another megaevent. This is not true in all man-made complex systems. Government intervention in the form of bailouts and money printing can temporarily arrest the cascade of failures. Yet it cannot make the risk go away. The risk is latent in the system, waiting for the next destabilizing event.
One solution to the problem of risk that comes from allowing a system to grow to a megascale is to make the system smaller, which is called descaling. This is why a mountain ski patrol throws dynamite on unstable slopes before skiing starts for the day. It is reducing avalanche danger by descaling, or simplifying, the snow mass. In global finance today, the opposite is happening. The financial ski patrol of central bankers is shoveling more snow onto the mountain. The financial system is now larger and more concentrated than immediately prior to the beginning of the market collapse in 2007.
In addition to global financial descaling, another solution to complexity risk is to maintain the system size but make it more robust by not letting any one component grow too large. The equivalent in banking would be to have more banks, but smaller ones with the same total system assets. It was not that many years ago that the current JPMorgan Chase existed as four separate banks: J. P. Morgan, Chase Manhattan, Manufacturers Hanover and Chemical. A breakup today would make the financial system more robust. Instead U.S. banks are bigger and their derivatives books are larger today than in 2008. This makes a new collapse, larger than the one in 2008, not just a possibility but a certainty. Next time, however, it really will be different. Based on theoretical scaling metrics, the next collapse will not be stopped by governments, because it will be larger than governments. The five-meter seawall will face the ten-meter tsunami and the wall will fall.
Complexity, Energy and MoneyUsing behavioral and complexity theory tools in tandem provides great insight into how the currency wars will evolve if money printing and debt expansion are not arrested soon. The course of the currency war will consist of a series of victories for the dollar followed by a decisive dollar defeat. The victories, at least as the Fed defines them, will arise as monetary ease creates inflation that forces other countries to revalue their currencies. The result will be a greatly depreciated dollar—exactly what the Fed wants. The dollar’s defeat will occur through a global political consensus to replace the dollar as the reserve currency and a private consensus to abandon it altogether.
When the dollar collapse comes, it will happen two ways—gradually and then suddenly. That formula, famously used by Hemingway to describe how one goes bankrupt, is an apt description of critical state dynamics in complex systems. The gradual part is a snowflake disturbing a small patch of snow, while the sudden part is the avalanche. The snowflake is random yet the avalanche is inevitable. Both ideas are easy to grasp. What is difficult to grasp is the critical state of the system in which the random event occurs.
In the case of currency wars, the system is the international monetary system based principally on the dollar. Every other market—stocks, bonds and derivatives—is based on this system because it provides the dollar values of the assets themselves. So when the dollar finally collapses, all financial activity will collapse with it.
Faith in the dollar among foreign investors may remain strong as long as U.S. citizens themselves maintain that faith. However, a loss of confidence in the dollar among U.S. citizens spells a loss of confidence globally. A simple model will illustrate how a small loss of faith in the dollar, for any reason, can lead to a complete collapse in confidence.
Start with the population of the United States as the system. For convenience, the population is set at 311,001,000 people, very close to the actual value. The population is divided based on individual critical thresholds, called a T value in this model. The critical threshold T of an individual in the system represents the number of other people who must lose confidence in the dollar before that individual also losses confidence. The value T is a measure of whether individuals react at the first potential sign of change or wait until a process is far advanced before responding. It is an individual tipping point; however, different actors will have different tipping points. It is like asking how many people must run from a crowded theater before the next person decides to run. Some people will run out at the first sign of trouble. Others will sit nervously but not move until most of the audience has already begun to run. Someone else will be the last one out of the theater. There can be as many critical thresholds as there are actors in the system.
The T values are grouped into five broad bands to show the potential influence of one group on the other. In the first case, shown in Table 1 below, the bands are divided from the lowest critical thresholds to the highest as follows:
Table 1: HYPOTHETICAL CRITICAL THRESHOLDS (T) FOR DOLLAR REPUDIATION IN U.S. POPULATION
The test case begins by asking what would happen if one hundred people suddenly repudiated the dollar. Repudiation means an individual rejects the dollar’s traditional functions as a medium of exchange, store of value and reliable way to set prices and perform other counting functions. These one hundred people would not willingly hold dollars and would consistently convert any dollars they obtained into hard assets such as precious metals, land, buildings and art. They would not rely on their ability to reconvert these hard assets into dollars in the future and would look only to the intrinsic value of the assets. They would avoid paper assets denominated in dollars, such as stocks, bonds and bank accounts.
BOOK: Currency Wars: The Making of the Next Global Crisis
9.33Mb size Format: txt, pdf, ePub
ads
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