A New Risk Management Model for Wall Street

By Rickards, James G. | The RMA Journal, March 2009 | Go to article overview
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A New Risk Management Model for Wall Street

Rickards, James G., The RMA Journal

Wall Street's risk management paradigm is a failure, says this observer, who argues that it should be replaced with an empirically robust model based on nonlinear complexity and critical-state dynamics. A clearinghouse for over-the-counter derivatives would improve transparency and manage failure in ways that can leave the system far healthier while avoiding systemic collapse.

Financial economics, over the past 50 years, has specialized in quantitative analysis of the problems of asset pricing, asset allocation, and risk management. Its contributions have been voluminous, leading to the creation of derivative products and enormous expansion of the markets in which those products are traded. Key contributions have included the Black-Scholes options pricing formula and the capital asset pricing model.

The general equilibrium paradigm that resulted underlies these developments and is based on two hypotheses:

1. The efficient market hypothesis (EMH): All available information is fully and rationally incorporated into market prices that move from one level to another based on "new" information without reference to the past. Therefore, no individual analysis can outperform the market since all insights are effectively "priced in."

2. Gaussian or "normal" distribution of price movements: Small fluctuations are common and extreme events are proportionately rare, with the overall degree distribution of such events falling in the familiar "bell curve" shape associated with random phenomena. In the late 1980s, substantial doubt began to emerge about this intellectual edifice. These doubts arose both deductively as the result of the new science of nonlinear physics, and inductively as the result of numerous empirical observations that failed to confirm either EMH or the Gaussian degree distribution.

In effect, a paradigm shift was under way in which the influence of behavioral economics, fractal geometry, complexity theory, heuristics, and related fields converged to demonstrate that not only did the general equilibrium paradigm fail to describe the reality of capital markets, but a more robust paradigm with powerful explanatory ability was waiting to take its place.

Extreme Events

The empirical failures of the general equilibrium paradigm are well known. Consider the following:

* The stock market crash of October 19, 1987, when the market fell 22.6% in one day.

* The "Tequila Crisis" of December 1994, when the Mexican peso plummeted 85% in one week.

* The Russian financial crisis and the failure of Long-Term Capital Management, a hedge fund, in September 1998, which caused the capital markets to almost cease functioning.

* The March 2000 dot-com collapse, during which the NASDAQ fell 80% over 30 months.

* The 9/11 attacks, after which the New York Stock Exchange closed and the value of its shares dropped 14.3% in the week following its reopening.

Of course, to this list of extreme events must now be added the financial crisis that began in July 2007. Events of this magnitude should, according to the general equilibrium paradigm, either not happen at all (because "rational" buyers will seek bargains once valuations deviate beyond a certain magnitude) or happen perhaps once every 100 years (because standard deviations of this degree lie extremely close to the x-axis on the bell curve, which corresponds to a value close to zero on the y-axis--that is, an extremely low frequency event). That all of these extreme events took place in just over 20 years is completely at odds with the predictions of stochastic methodology in a normally distributed paradigm.

Practitioners treated these observations not as fatal flaws in the general equilibrium paradigm, but rather as "anomalies" to be explained within the framework of the paradigm. Thus was born the "fat tail," which is simply an embellishment on the bell curve such that after approaching the x-axis (the extreme low frequency region), the curve "turns upward" again to intersect data points representing a cluster of highly extreme, but not so highly rare, events.

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A New Risk Management Model for Wall Street


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