Evidence of a worldwide stock market log-periodic anti-bubble since mid-2000

Following our investigation of the USA Standard and Poor index anti-bubble that started in August 2000 (Quant. Finance 2 (2002) 468), we analyze 38 world stock market indices and identify 21 “bearish anti-bubbles” and six “bullish anti-bubbles”. An “anti-bubble” is defined as a self-reinforcing price trajectory with self-similar expanding log-periodic oscillations. Mathematically, a bearish anti-bubble is characterize by a power law decrease of the price (or of the logarithm of the price) as a function of time and by expanding log-periodic oscillations. We propose that bearish anti-bubbles are created by positive price-to-price feedbacks feeding overall pessimism and negative market sentiment further strengthened by inter-personal interactions. Bullish anti-bubbles are here identified for the first time. The most striking discovery is that the majority of European and Western stock market indices as well as other stock indices exhibit practically the same log-periodic power law anti-bubble structure as found for the USA S&P500 index. These anti-bubbles are found to start approximately at the same time, August 2000, in all these markets. This shows a remarkable degree of worldwide synchronization. The descent of the worldwide stock markets since 2000 is thus an international event, suggesting the strengthening of globalization.     

“String” formulation of the dynamics of the forward interest rate curve

We propose a formulation of the term structure of interest rates in which the forward curve is seen as the deformation of a string. We derive the general condition that the partial differential equations governing the motion of such string must obey in order to account for the condition of absence of arbitrage opportunities. This condition takes a form similar to a fluctuation-dissipation theorem, albeit on the same quantity (the forward rate), linking the bias to the covariance of variation fluctuations. We provide the general structure of the models that obey this constraint in the framework of stochastic partial (possibly non-linear) differential equations. We derive the general solution for the pricing and hedging of interest rate derivatives within this framework, albeit for the linear case (we also provide in the appendix a simple and intuitive derivation of the standard European option problem). We also show how the “string” formulation simplifies into a standard N-factor model under a Galerkin approximation. Received: 30 January 1998 / Revised: 12 February 1998 / Accepted: 16 February 1998     

Dragon-kings: Mechanisms,statistical methods and empirical evidence

This introductory article presents the special Discussion and Debate volume “From black swans to dragon-kings, is there life beyond power laws?” We summarize and put in perspective the contributions into three main themes: (i) mechanisms for dragon-kings, (ii) detection of dragon-kings and statistical tests and (iii) empirical evidence in a large variety of natural and social systems. Overall, we are pleased to witness significant advances both in the introduction and clarification of underlying mechanisms and in the development of novel efficient tests that demonstrate clear evidence for the presence of dragon-kings in many systems. However, this positive view should be balanced by the fact that this remains a very delicate and difficult field, if only due to the scarcity of data as well as the extraordinary important implications with respect to hazard assessment, risk control and predictability.     

”Illusion of control” in Time-Horizon Minority and Parrondo Games

Human beings like to believe they are in control of their destiny. This ubiquitous trait seems to increase motivation and persistence, and is probably evolutionarily adaptive [J.D. Taylor, S.E. Brown, Psych. Bull. 103, 193 (1988); A. Bandura, Self-efficacy: the exercise of control (WH Freeman, New York, 1997)]. But how good really is our ability to control? How successful is our track record in these areas? There is little understanding of when and under what circumstances we may over-estimate [E. Langer, J. Pers. Soc. Psych. 7, 185 (1975)] or even lose our ability to control and optimize outcomes, especially when they are the result of aggregations of individual optimization processes. Here, we demonstrate analytically using the theory of Markov Chains and by numerical simulations in two classes of games, the Time-Horizon Minority Game [M.L. Hart, P. Jefferies, N.F. Johnson, Phys. A 311, 275 (2002)] and the Parrondo Game [J.M.R. Parrondo, G.P. Harmer, D. Abbott, Phys. Rev. Lett. 85, 5226 (2000); J.M.R. Parrondo, How to cheat a bad mathematician (ISI, Italy, 1996)], that agents who optimize their strategy based on past information may actually perform worse than non-optimizing agents. In other words, low-entropy (more informative) strategies under-perform high-entropy (or random) strategies. This provides a precise definition of the “illusion of control” in certain set-ups a priori defined to emphasize the importance of optimization. An erratum to this article is available at .     

Generation-by-generation dissection of the response function in long memory epidemic processes

In a number of natural and social systems, the response to an exogenous shock relaxes back to the average level according to a long-memory kernel ~1/t^1+θ with 0 ≤ θ < 1. In the presence of an epidemic-like process of triggered shocks developing in a cascade of generations at or close to criticality, this “bare” kernel is renormalized into an even slower decaying response function ~1/t^1-θ. Surprisingly, this means that the shorter the memory of the bare kernel (the larger 1+θ), the longer the memory of the response function (the smaller 1-θ). Here, we present a detailed investigation of this paradoxical behavior based on a generation-by-generation decomposition of the total response function, the use of Laplace transforms and of “anomalous” scaling arguments. The paradox is explained by the fact that the number of triggered generations grows anomalously with time at ~ t^θ so that the contributions of active generations up to time t more than compensate the shorter memory associated with a larger exponent θ. This anomalous scaling results fundamentally from the property that the expected waiting time is infinite for 0 ≤ θ ≤ 1. The techniques developed here are also applied to the case θ > 1 and we find in this case that the total renormalized response is a constant for t < 1/(1-n) followed by a cross-over to ~1/t^1+θ for t ≫ 1/(1-n).     

Heavy-tailed distribution of cyber-risks

With the development of the Internet, new kinds of massive epidemics, distributed attacks, virtual conflicts and criminality have emerged. We present a study of some striking statistical properties of cyber-risks that quantify the distribution and time evolution of information risks on the Internet, to understand their mechanisms, and create opportunities to mitigate, control, predict and insure them at a global scale. First, we report an exceptionnaly stable power-law tail distribution of personal identity losses per event, Pr(ID loss ≥ V) ~ 1/V^b, with b = 0.7 ± 0.1. This result is robust against a surprising strong non-stationary growth of ID losses culminating in July 2006 followed by a more stationary phase. Moreover, this distribution is identical for different types and sizes of targeted organizations. Since b < 1, the cumulative number of all losses over all events up to time t increases faster-than-linear with time according to ≃ t^1/b, suggesting that privacy, characterized by personal identities, is necessarily becoming more and more insecure. We also show the existence of a size effect, such that the largest possible ID losses per event grow faster-than-linearly as ~S^1.3 with the organization size S. The small value b ≃ 0.7 of the power law distribution of ID losses is explained by the interplay between Zipf’s law and the size effect. We also infer that compromised entities exhibit basically the same probability to incur a small or large loss.     

Multifractal scaling of thermally activated rupture processes

We propose a "multifractal stress activation" model combining thermally activated rupture and long memory stress relaxation, which predicts that seismic decay rates after mainshocks follow the Omori law approximately 1/t(p) with exponents p linearly increasing with the magnitude M(L) of the mainshock. We carefully test this prediction on earthquake sequences in the Southern California earthquake catalog: we find power law relaxations of seismic sequences triggered by mainshocks with exponents p increasing with the mainshock magnitude by approximately 0.1-0.15 for each magnitude unit increase, from p(M(L) = 3) approximately 0.6 to p(M(L) = 7) approximately 1.1, in good agreement with the prediction of the multifractal model.