Narrow Results

We apply two nonparametric methods to further test the hypothesis that log-periodicity characterizes the detrended price trajectory of large financial indices prior to financial crashes or strong corrections. The term "parametric" refers here to the use of the log-periodic power law formula to fit the data; in contrast, "nonparametric" refers to the use of general tools such as Fourier transform, and in the present case the Hilbert transform and the so-called (H, q)-analysis. The analysis using the (H, q)-derivative is applied to seven time series ending with the October 1987 crash, the October 1997 correction and the April 2000 crash of the Dow Jones Industrial Average (DJIA), the Standard & Poor 500 and Nasdaq indices. The Hilbert transform is applied to two detrended price time series in terms of the ln(t_c-t) variable, where t_c is the time of the crash. Taking all results together, we find strong evidence for a universal fundamental log-frequency f=1.02±0.05 corresponding to the scaling ratio λ=2.67±0.12. These values are in very good agreement with those obtained in earlier works with different parametric techniques. This note is extracted from a long unpublished report with 58 figures available at , which extensively describes the evidence we have accumulated on these seven time series, in particular by presenting all relevant details so that the reader can judge for himself or herself the validity and robustness of the results.

The static topology properties of financial networks have been widely investigated since the work done by Mantegna, yet their dynamic evolution with time is little considered. In this paper, we comprehensively study the dynamic evolution of financial network by a sliding window technique. The vertices and edges of financial network are represented by the stocks from S&P500 components and correlations between pairs of daily returns of price fluctuation, respectively. Furthermore, the duration of stock price fluctuation, spanning from January 4, 1985 to September 14, 2009, makes us to carefully observe the relation between the dynamic topological properties and big financial crashes. The empirical results suggest that the financial network has the robust small-world property when the time evolves, and the topological structure drastically changes when the big financial crashes occur. This correspondence between the dynamic evolution of financial network and big financial crashes may provide a novel view to understand the origin of economic crisis.

At odds with the common “rational expectations” framework for bubbles, economists like Hyman Minsky, Charles Kindleberger and Robert Shiller have documented that irrational behavior, ambiguous information or certain limits to arbitrage are essential drivers for bubble phenomena and financial crises. Following this understanding that asset price bubbles are generated by market failures, we present a framework for explosive semimartingales that is based on the antagonistic combination of (i) an excessive, unstable pre-crash process and (ii) a drawdown starting at some random time. This unifying framework allows one to accommodate and compare many discrete and continuous time bubble models in the literature that feature such market inefficiencies. Moreover, it significantly extends the range of feasible asset price processes during times of financial speculation and frenzy and provides a strong theoretical background for future model design in financial and risk management problem settings. This conception of bubbles also allows us to elucidate the status of rational expectation bubbles, which, by design, suffer from the paradox that a rational market should not allow for misvaluation. While the discrete time case has been extensively discussed in the literature and is most criticized for its failure to comply with rational expectations equilibria, we argue that this carries over to the finite time “strict local martingale”-approach to bubbles.

Following the victory of the Congress Party, the Indian SENSEX index crashed in May 2004. We present a brief analysis of the crash, showing that very likely the crash was due to the outcome of the Indian elections, but in a situation of very high instability, with the market already past a transition point, well described by the Sornette-Johansen model.