Classification of flash crashes using the Hawkes(p,q)framework

Abstract

We introduce a novel modeling framework-the Hawkes(p, q) process-which allows us to parsimoniously disentangle and quantify the time-varying share of high frequency financial price changes that are due to endogenous feedback processes and not exogenous impulses. We show how both flexible exogenous arrival intensities, as well as a time-dependent feedback parameter can be estimated in a structural manner using an Expectation Maximization algorithm. We use this approach to investigate potential characteristic signatures of anomalous market regimes in the vicinity of 'flash crashes'-events where prices exhibit highly irregular and cascading dynamics. Our study covers some of the most liquid electronic financial markets, in particular equity and bond futures, foreign exchange and cryptocurrencies. Systematically balancing the degrees of freedom of both exogenously driving processes and endogenous feedback variation using information criteria, we show that the dynamics around such events are not universal, highlighting the usefulness of our approach: (i) post-mortem, for developing remedies and better future processes-e.g. improving circuit breakers or latency floor designs-and potentially (ii) ex-ante, for short-term forecasts in the case of endogenously driven events. Finally, we test our proposed model against a process with refined treatment of exogenous clustering dynamics in the spirit of the recently proposed autoregressive moving-average (ARMA) point process.

The excess volatility puzzle explained by financial noise amplification from endogenous feedbacks

“…Our own estimates with suggest that volatility in the EUR/USD exchange rate is inflated by a factor of 2.5. We finally also note that previous estimates for the reflexivity of other financial markets, such as fixed income futures 61 or cryptocurrencies 61 , 62 , suggest that our theory and conclusions with respect to the generating mechanism for excess volatility apply very broadly.…”

“…The left panel shows this decomposition for the E-mini flash crash and the right panel for the crash in the GBP/USD exchange rate. Both events were also analyzed in detail in 61 . Each panel shows in the top row the price evolution in the sample.…”

Exogenous and endogenous factors affecting stock market transactions: A Hawkes process analysis of the Tokyo Stock Exchange during the COVID-19 pandemic

“…We modified the method of Wehrli and Sornette (2022) [ 8 ] to estimate the temporal changes in endogenous and exogenous factors with the transaction frequency. They proposed an EM algorithm that considered the branching structure [ 8 ], which represents the lineage relationship among events, namely, the “which event generated which” relationship [ 8 , 32 ].…”

“…We modified the method of Wehrli and Sornette (2022) [ 8 ] to estimate the temporal changes in endogenous and exogenous factors with the transaction frequency. They proposed an EM algorithm that considered the branching structure [ 8 ], which represents the lineage relationship among events, namely, the “which event generated which” relationship [ 8 , 32 ]. By referring to this method, we developed an algorithm to calculate the dynamic changes in the self-excitation strength and background intensity that are consistent with the hidden branching structure between events, causing a more feasible estimation.…”

Bounded strategic reasoning explains crisis emergence in multi-agent market games

“…The most serious specific critiques of the EMH include its inability to explain periods of out-of-equilibrium behaviour such as endogenous crises [11,12] or the ‘stylized facts’ present in economic markets such as volatility clustering where periods of high (low) volatility occur in bursts, in the absence of any significant external news [13]. Such crises are known to occur in different markets, from housing markets [14,15], to stock markets [16], and foreign exchange markets [17]. Likewise, volatility clustering is consistently observed in markets [18], with temporal correlations in volatility breaching the traditional economic assumption of heteroskedasticity.…”