Originally posted on Tuesday, February 5th, 2013
Written by Ralph J. Benko
Federal Reserve Notes, backed by “the full faith and credit of the United States,” is, in a way, the ultimate derivative.
How to Lose $3 million in 1 second,
an article by Chris Arnade, who
“received a PhD in Physics from Johns Hopkins University. He joined Salomon Brothers in the Bond Portfolio group in 93 where he built credit models. In 95 he moved to the Emerging Markets trading desk where he traded options, bonds, credit derivatives, interest rates, and FX. He focused on proprietary trading from 06 until six months ago,”
Image by puzzlemepuzzle under creative commons license
in Scientific American, provides a fascinating, “from the battlefront,” view of the markets meltdown of 2008, and its cause:
The months following Lehman’s collapse saw the entire financial system start to fail, in a cascade of interconnected plummeting securities, and with them, the world economy. Major banks were broke, hedge funds decimated, and the average investor left feeling fleeced. The process only ended when the Federal Reserve stepped in, bailed out banks, and acted as the buyer and lender of last resort.
Those months reminded many that liquidity, how much of something you can trade per day and convert into cash, without affecting its price, is the most misunderstood variable in finance.
How did we get here?
Finance now is a complex field buttressed by hundreds of mathematical models. At its heart is the Black-Scholes equation for pricing of options.
Published in 1973, it slowly revolutionized finance, leading to a boom in financial contracts and new way of looking at markets in terms of relative value and models. It also changed the type of person who worked on Wall Street. It became people like me, with a PhD in Physics, who could build models, like Black-Scholes, to price complex products like options.
Hubris rarely is associated with math, but Black-Scholes gave the markets a sense of greater control, greater clarity. Most important, it gave the markets the belief that risk could be understood and lessened, and value better understood. Over time some traders started to focus only on using models to buy cheap assets and sell rich ones.
Mathematical hubris reached its zenith with the founding of LTCM, a hedge fund started in 1994 by John Meriwether and based in Greenwich, Connecticut.
“Mathematical hubris” is a perfect description for the overreliance on models of reality rather than reality itself.
“The expression ‘the map is not the territory’ first appeared in print in a paper that Alfred Korzybski gave at a meeting of the American Association for the Advancement of Science in New Orleans, Louisiana in 1931: In Science and Sanity, Korzybski acknowledges his debt to mathematician Eric Temple Bell, whose epigram “the map is not the thing mapped” was published in Numerology,” as Wikipedia summarizes. The Federal Reserve Board itself relies heavily on modeling to set monetary policy.
Federal Reserve Notes, backed by “the full faith and credit of the United States,” is, in a way, the ultimate derivative. Fed officials certainly make their modeling sound impressive, such as this abstract from a presentation on Macro Models and Monetary Policy Analysis, Presented by Charles I. Plosser, President and Chief Executive Officer, Federal Reserve Bank of Philadelphia, Bundesbank — Federal Reserve Bank of Philadelphia Spring 2012 Research Conference, Eltville, Germany, May 25, 2012
The current generation of macro models, referred to as New Keynesian DSGE models, rely on real and nominal frictions to transmit not only unanticipated but also systematic changes in monetary policy to the economy. Unexpected monetary shocks drive movements in output, consumption, investment, hours worked, and employment in DSGE models. However, in contrast to the earlier literature, it is the relevance of systematic movements in monetary policy that makes these models of so much interest for policy analysis. Systematic policy changes are represented in these models by Taylor-type rules, in which the policy interest rate responds to changes in inflation and a measure of real activity, such as output growth. Armed with forecasts of inflation and output growth, a central bank can assess the impact that different policy rate paths may have on the economy. The ability to do this type of policy analysis helps explain the widespread use of New Keynesian DSGE models at central banks around the world.
These modern macro models stress the importance of credibility and systematic policy, as well as forward-looking rational agents, in the determination of economic outcomes. In doing so, they offer guidance to policymakers about how to structure policies that will improve the policy framework and, therefore, economic performance. Nonetheless, I think there is room for improving the models and the advice they deliver on policy options. Before discussing several of these improvements, it is important to appreciate the “rules of the game” of the New Keynesian DSGE framework.
The classical gold standard is grounded in the territory, not the map. Sophisticated models notwithstanding, fiduciary currency managed by a panel of elite specialists has been inferior in every respect to the predecessor gold-based systems. That was the finding of a thorough analysis by the Bank of England published in its Financial Stability Report No. 13. Rather than dealing from mathematical hubris, time for monetary officials to restore a modern classical gold standard. The classical gold standard allows the market, rather than career civil servants, to determine appropriate liquidity balances demanded, directly, by the market participants.
The data show that the market will do a better job than central planners.
Dr. Arnade helps us understand why.
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