Report on the Effectiveness and Possible Side Effects of the Office of Financial Research (OFR)

The paper presents evidence that econometric techniques based on variance – L2 norm – are flawed and do not replicate. The result is un-computability of the role of tail events. The paper proposes a methodology to calibrate decisions to the degree (and computability) of forecast error. It classifies decision payoffs in two types: simple (true/false or binary) and complex (higher moments); and randomness into type-1 (thin tails) and type-2 (true fat tails), and shows the errors for the estimation of small probability payoffs for type 2 randomness. The fourth quadrant is where payoffs are complex with type-2 randomness. We propose solutions to mitigate the effect of the fourth quadrant, based on the nature of complex systems. c © 2009 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

[1]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[2]  Per Bak,et al.  How Nature Works , 1996 .

[3]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[4]  W. Hays Statistics, 4th ed. , 1988 .

[5]  George Chacko,et al.  Financial Derivatives: Pricing, Applications, and Mathematics , 2004 .

[6]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[7]  Alexander M. Millkey The Black Swan: The Impact of the Highly Improbable , 2009 .

[8]  Benoit B. Mandelbrot,et al.  Fractals and Scaling in Finance , 1997 .

[9]  Isaac Levi,et al.  The Paradoxes of Allais and Ellsberg , 1986, Economics and Philosophy.

[10]  N. Taleb Black Swans and the Domains of Statistics , 2007 .

[11]  N. Taleb Bleed or Blowup? Why Do We Prefer Asymmetric Payoffs? , 2004 .

[12]  Phhilippe Jorion Value at Risk: The New Benchmark for Managing Financial Risk , 2000 .

[13]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[14]  Benoit B. Mandelbrot,et al.  Mild vs. Wild Randomness: Focusing on those Risks that Matter , 2007 .

[15]  P. Gärdenfors,et al.  Unreliable probabilities, risk taking, and decision making , 1982, Synthese.

[16]  B. Mandelbrot The Variation of Certain Speculative Prices , 1963 .

[17]  G. Edelman,et al.  Degeneracy and complexity in biological systems , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[19]  Stanley,et al.  Statistical properties of share volume traded in financial markets , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[20]  Nassim Nicholas Taleb,et al.  Finiteness of variance is irrelevant in the practice of quantitative finance , 2009, Complex..

[21]  George Sugihara,et al.  Complex systems: Ecology for bankers , 2008, Nature.

[22]  V. Plerou,et al.  Scaling of the distribution of fluctuations of financial market indices. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[23]  V. Plerou,et al.  Scale invariance and universality of economic fluctuations , 2000 .

[24]  R. C. Merton,et al.  Option pricing when underlying stock returns are discontinuous , 1976 .

[25]  Daniel G. Goldstein,et al.  We Don't Quite Know What We Are Talking About , 2007 .

[26]  H. Olff,et al.  Spatial scaling laws yield a synthetic theory of biodiversity , 1999, Nature.

[27]  Victor DeMiguel,et al.  Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy? , 2009 .

[28]  Karl J. Niklas,et al.  Invariant scaling relations across tree-dominated communities , 2001, Nature.

[29]  D. Ellsberg Decision, probability, and utility: Risk, ambiguity, and the Savage axioms , 1961 .

[30]  B Burlando,et al.  The fractal geometry of evolution. , 1993, Journal of theoretical biology.

[31]  Didier Sornette,et al.  Stock market crashes are outliers , 1998 .

[32]  R. C. Merton,et al.  Continuous-Time Finance , 1990 .

[33]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[34]  Espen Gaarder Haug,et al.  Derivatives Models on Models , 2007 .

[35]  J. Bouchaud,et al.  Theory of financial risks : from statistical physics to risk management , 2000 .

[36]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[37]  Kinzig,et al.  Self-similarity in the distribution and abundance of species , 1999, Science.

[38]  Nassim Nicholas Taleb,et al.  Errors, Robustness, and the Fourth Quadrant , 2009 .

[39]  D. Sornette Critical Phenomena in Natural Sciences: Chaos, Fractals, Selforganization and Disorder: Concepts and Tools , 2000 .

[40]  Sergey V. Buldyrev,et al.  Scaling behavior in economics: I Epirical results for company growth , 1997, cond-mat/9702082.

[41]  C. Chatfield,et al.  The M2-competition: A real-time judgmentally based forecasting study , 1993 .

[42]  R. Weron Levy-stable distributions revisited: tail index > 2 does not exclude the Levy-stable regime , 2001, cond-mat/0103256.

[43]  D. Kahneman,et al.  Well-being : the foundations of hedonic psychology , 1999 .

[44]  Tang,et al.  Self-organized criticality. , 1988, Physical review. A, General physics.

[45]  Benton,et al.  Criticality and scaling in evolutionary ecology. , 1997, Trends in ecology & evolution.

[46]  J. Bouchaud,et al.  Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management , 2011 .

[47]  V. Plerou,et al.  A theory of power-law distributions in financial market fluctuations , 2003, Nature.

[48]  J. Corcoran Modelling Extremal Events for Insurance and Finance , 2002 .

[49]  P. Gopikrishnan,et al.  Inverse cubic law for the distribution of stock price variations , 1998, cond-mat/9803374.