'Speculative Influence Network' During Financial Bubbles: Application to Chinese Stock Markets

38 Pages Posted: 5 Nov 2015

See all articles by Li Lin

Li Lin

East China University of Science and Technology (ECUST)

Didier Sornette

Risks-X, Southern University of Science and Technology (SUSTech); Swiss Finance Institute; ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Tokyo Institute of Technology

Date Written: October 28, 2015

Abstract

We introduce the Speculative Influence Network (SIN) to decipher the causal relationships between sectors (and/or firms) during financial bubbles. The SIN is constructed in two steps. First, we develop a Hidden Markov Model (HMM) of regime-switching between a normal market phase represented by a geometric Brownian motion (GBM) and a bubble regime represented by the stochastic super-exponential Sornette-Andersen (2002) bubble model. The calibration of the HMM provides the probability at each time for a given security to be in the bubble regime. Conditional on two assets being qualified in the bubble regime, we then use the transfer entropy to quantify the influence of the returns of one asset $i$ onto another asset $j$, from which we introduce the adjacency matrix of the SIN among securities. We apply our technology to the Chinese stock market during the period 2005-2008, during which a normal phase was followed by a spectacular bubble ending in a massive correction. We introduce the Net Speculative Influence Intensity (NSII) variable as the difference between the transfer entropies from $i$ to $j$ and from $j$ to $i$, which is used in a series of rank ordered regressions to predict the maximum loss (\%{MaxLoss}) endured during the crash. The sectors that influenced other sectors the most are found to have the largest losses. There is a clear prediction skill obtained by using the transfer entropy involving industrial sectors to explain the \%{MaxLoss} of financial institutions but not vice versa. We also show that the bubble state variable calibrated on the Chinese market data corresponds well to the regimes when the market exhibits a strong price acceleration followed by clear change of price regimes. Our results suggest that SIN may contribute significant skill to the development of general linkage-based systemic risks measures and early warning metrics.

Keywords: financial bubbles, super-exponential, systemic risks, Hidden Markov Modeling, transfer entropy, speculative influence network, early warning system, Chinese stock market

JEL Classification: C46, D85, G01, G17

Suggested Citation

Lin, Li and Sornette, Didier, 'Speculative Influence Network' During Financial Bubbles: Application to Chinese Stock Markets (October 28, 2015). Swiss Finance Institute Research Paper No. 15-45, Available at SSRN: https://ssrn.com/abstract=2686229 or http://dx.doi.org/10.2139/ssrn.2686229

Li Lin

East China University of Science and Technology (ECUST) ( email )

Shanghai
China

Didier Sornette (Contact Author)

Risks-X, Southern University of Science and Technology (SUSTech) ( email )

1088 Xueyuan Avenue
Shenzhen, Guangdong 518055
China

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC) ( email )

Scheuchzerstrasse 7
Zurich, ZURICH CH-8092
Switzerland
41446328917 (Phone)
41446321914 (Fax)

HOME PAGE: http://www.er.ethz.ch/

Tokyo Institute of Technology ( email )

2-12-1 O-okayama, Meguro-ku
Tokyo 152-8550, 52-8552
Japan

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