Interpreting financial market crashes as earthquakes: A new Early Warning System for medium term crashes

Abstract

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Short term prediction of extreme returns based on the recurrence interval analysis

“…These decreasing patterns in the hazard probability are also seen in energy futures (Xie et al, 2014), spot index and index futures (Suo et al, 2015), and stock returns (Ren and Zhou, 2010a;Jiang et al, 2016), indicating that the probability of observing a follow-up extreme return decreases as time t elapses. This reveals the existence of extreme return clustering and a potential dependent structure in the triggering processes of the extreme returns, which supports the argument that "many extreme price movements are triggered by previous extreme movements" and that "larger extremes occur more often after big events or frequent events than after tranquil periods" (Gresnigt et al, 2015). This is caused by the positive herding behavior of investors and the endogenous growth of instability in financial markets (Jiang et al, 2010;Gresnigt et al, 2015).…”

Multivariate dynamic intensity peaks‐over‐threshold models

“…On the one hand, we introduce an autoregressive conditional intensity peaks‐over‐threshold (ACI‐POT) model, which, in its most basic form, corresponds to the combination of two known models: the ACI model introduced by Russell () and the POT model by Davison and Smith (). Moreover, we propose a multivariate extension of a Hawkes‐POT model, introduced in a univariate context by Chavez‐Demoulin, Davison, and McNeil () and recently reviewed in different applications by Chavez‐Demoulin and McGill (), Herrera and Schipp (), and Gresnigt, Kole, and Franses ().…”

Specification Testing in Hawkes Models

“…The relevance of these tests is shown by Chavez-Demoulin et al (2005), Herrera and Schipp (2009) and Gresnigt et al (2015). They find that the probability that an extreme return on a stock market index triggers another extreme return, is larger when the initial event is larger.…”

“…When modelling earthquakes it can be useful to consider precursors such as unusual animal behaviour and temperature changes which can signal upcoming disruptions between tectonical plates (Rikitake, 1978, and Cicerone et al, 2009). Using the resemblance of financial crashes and earthquakes, as is done in Gresnigt et al (2015), there may be precursors that are of interest when modelling financial series.…”