Apparent criticality and calibration issues in the Hawkes self-excited point process model: application to high-frequency financial data
Vladimir Filimonov, Didier Sornette

TL;DR
This paper critically examines biases and calibration issues in applying Hawkes processes to high-frequency financial data, revealing that previous claims of market criticality near n~1 are unsupported due to methodological flaws.
Contribution
It identifies intrinsic biases in estimating the branching ratio with power law kernels and demonstrates how regime shifts and data quality affect calibration, challenging prior claims of market criticality.
Findings
Calibration biases lead to spurious criticality claims.
Regime shifts cause upward bias in branching ratio estimates.
Data quality issues significantly impact model calibration.
Abstract
We present a careful analysis of possible issues on the application of the self-excited Hawkes process to high-frequency financial data. We carefully analyze a set of effects leading to significant biases in the estimation of the "criticality index" n that quantifies the degree of endogeneity of how much past events trigger future events. We report a number of model biases that are intrinsic to the estimation of brnaching ratio (n) when using power law memory kernels. We demonstrate that the calibration of the Hawkes process on mixtures of pure Poisson process with changes of regime leads to completely spurious apparent critical values for the branching ratio (n~1) while the true value is actually n=0. More generally, regime shifts on the parameters of the Hawkes model and/or on the generating process itself are shown to systematically lead to a significant upward bias in the estimation…
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