Information dissipation as an early-warning signal for the Lehman Brothers collapse in financial time series

Abstract

In financial markets, participants locally optimize their profit which can result in a globally unstable state leading to a catastrophic change. The largest crash in the past decades is the bankruptcy of Lehman Brothers which was followed by a trust-based crisis between banks due to high-risk trading in complex products. We introduce information dissipation length (IDL) as a leading indicator of global instability of dynamical systems based on the transmission of Shannon information, and apply it to the time series of USD and EUR interest rate swaps (IRS). We find in both markets that the IDL steadily increases toward the bankruptcy, then peaks at the time of bankruptcy, and decreases afterwards. Previously introduced indicators such as ‘critical slowing down' do not provide a clear leading indicator. Our results suggest that the IDL may be used as an early-warning signal for critical transitions even in the absence of a predictive model.

The Chinese Correction of February 2007: How financial hierarchies change in a market crash

“…From Fig. 8(b), we see a large and predominantly Chinese supercluster made up of the Chinese Clusters 3,4,5,9,10,13,15, the hybrid Clusters 12, 14, the foreign Cluster 11, and the local Cluster 6, as well as a hybrid supercluster made up of the Chinese Clusters 2, 7, 8, and the local Cluster 1. The interaction between the two superclusters was primarily between Chinese Clusters 7 and 10.…”

“…In fact, this increase in cross correlations before the market crashes has been observed by many econophysicists for many market crashes [11][12][13][14]. Recently, Quax et al also took a big step forward to map out the 'reaction pathway' near the tipping point, by developing an information-theoretic approach to the market prediction problem [15].…”

Applying Complexity Theory to Interest Rates: Evidence of Critical Transitions in The Euro Area

“…In the literature, complexity theory is usually applied to research critical transitions in eco-systems (Scheffer et al, 2009). Some studies also apply it to financial data, by using complexity indicators to detect financial crises (Guttal et al, 2016, Diks et al 2015and Quax et al, 2013. The added value of our paper is that we apply complexity theory to regime shifts in the configuration of interest rates, linking them to the increase of excess liquidity.…”

Applying Information Theory to Neuronal Networks: From Theory to Experiments

“…For a technique to be considered for use with IT, it must be able to simultaneously measure both: (i) the dynamics of each individual cell as a time series, and (ii) the location of the corresponding individual cell in the microcircuit. This is because the developing "information processing" theory is concerned with explaining the role of the neuronal network structure on the dynamics of the network in a bottom-up manner: namely, concerning how the network induces a flow of individual bits [8,11,14,16,17,21], as well as the interaction between bits into higher-level information [11,16,18].…”