Identifying financial crises in real time

Abstract

Following the thermodynamic formulation of multifractal measure that was shown to be capable of detecting large fluctuations at an early stage, here we propose a new index which permits us to distinguish events like financial crisis in real time . We calculate the partition function from where we obtain thermodynamic quantities analogous to free energy and specific heat. The index is defined as the normalized energy variation and it can be used to study the behavior of stochastic time series, such as financial market daily data. Famous financial market crashes - Black Thursday (1929), Black Monday (1987) and Subprime crisis (2008) - are identified with clear and robust results. The method is also applied to the market fluctuations of 2011. From these results it appears as if the apparent crisis of 2011 is of a different nature from the other three. We also show that the analysis has forecasting capabilities.Comment: 8 pages, 6 figure

Multifractal analysis of financial markets: a review

“…Cristescu et al explored the possibility of using intermittency to predict the occurrence of financial crises [1097]. da Fonseca et al defined the area variation rate based on the analogous specific heat −τ (q) and suggested that it has potential power in identifying historical crashes [1098]. Morales et al calculated the dynamical generalized Hurst exponents through the weighted structure function approach and found hints that the multifractal strength ∆H(1, 2) = H(1) − H (2) can be a tool to monitor unstable periods in financial time series [1014,1015].…”

Multifractal analysis of financial markets

“…Cristescu et al explored the possibility of using intermittency to predict the occurrence of financial crises [1039]. da Fonseca et al defined the area variation rate based on the analogous specific heat −τ ′′ (q) and suggested that it has potential power in identifying historical crashes [1040]. Morales et al calculated the dynamical generalized Hurst exponents through the weighted structure function approach and found hints that the multifractal strength ∆H(1, 2) = H(1) − H( 2) can be a tool to monitor unstable periods in financial time series [955,956].…”

Using trading strategies to detect phase transitions in financial markets

“…Usually the province of finance, trading strategies are here proposed as genuine nonlinear transformations mapping an input time series (here a price) onto an output profit-and-loss time series that, when coupled with physical mechanism(s), may reveal novel properties of the studied system [20][21][22],…”