Exogenous and endogenous price jumps belong to different dynamical classes

Abstract

Synchronising a database of stock specific news with 5 years worth of order book data on 300 stocks, we show that abnormal price movements following news releases (exogenous) exhibit markedly different dynamical features from those arising spontaneously (endogenous). On average, large volatility fluctuations induced by exogenous events occur abruptly and are followed by a decaying power-law relaxation, while endogenous price jumps are characterized by progressively accelerating growth of volatility, also followed by a power-law relaxation, but slower than for exogenous jumps. Remarkably, our results are reminiscent of what is observed in different contexts, namely Amazon book sales and YouTube views. Finally, we show that fitting power-laws to individual volatility profiles allows one to classify large events into endogenous and exogenous dynamical classes, without relying on the news feed.

Microfounding GARCH Models and Beyond: A Kyle-inspired Model with Adaptive Agents

“…The microfoundation we propose seems suitable to describe the large fraction of price jumps not directly linked to fundamental innovations' arrivals. In fact, it predicts symmetric price jumps, in line with empirical findings [36]; therefore, it points at the fact that symmetric GARCH models are more prone to describe price jumps not related to external fundamental innovations, at odds with the EMH story which relies on fundamental innovation clustering.…”

“…This explanation is in line with EMH, which assumes that all the available information is encoded in the price. However, starting from the work of Cutler, Poterba and Summers in 1988 [33], there is growing evidence that fundamental innovations only account for a small fraction of price jumps [34,35], and clusters of jumps [36]. Thus, an alternative explanation is needed for volatility clustering.…”

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

“…It is clear that the tests conducted here are far from a systematic study of self-excitation in extreme returns, which is out of the scope of this paper, but merits future analysis. Such a study could potentially also help reconcile the apparent paradox that volatility profiles around large volatility fluctuations are in line with predictions of a critical system with memory 50 , whereas modelling overall market activity using self-excited processes strongly rejects a criticality hypothesis 35,36 .…”

Nonequilibrium phase transitions in competitive markets caused by network effects

“…Following refs. 39 – 41 , a more quantitative check for whether fluctuations are endogenous (intrinsic to the market) or exogenous (driven by external shocks) is to study the time correlations of market properties such as Q ( t ). In particular, in our model, exogenous shocks in the PC phase cause rapid drops in Q ( t ) followed by slow increases as the market returns to equilibrium.…”