Quadratic Hawkes processes for financial prices

TL;DR

This paper introduces Quadratic Hawkes (QHawkes) models that extend traditional Hawkes processes by incorporating quadratic feedback in jump intensities, capturing complex market features like asymmetry and fat tails.

Contribution

The paper develops and analyzes QHawkes models with quadratic feedback, demonstrating their ability to reproduce key empirical features of financial prices not captured by standard models.

Findings

QHawkes models exhibit time-reversal asymmetry.

They generate multiplicative, fat-tailed volatility.

Calibrated models reproduce empirical market features.

Abstract

We introduce and establish the main properties of QHawkes ("Quadratic" Hawkes) models. QHawkes models generalize the Hawkes price models introduced in E. Bacry et al. (2014), by allowing all feedback effects in the jump intensity that are linear and quadratic in past returns. A non-parametric fit on NYSE stock data shows that the off-diagonal component of the quadratic kernel indeed has a structure that standard Hawkes models fail to reproduce. Our model exhibits two main properties, that we believe are crucial in the modelling and the understanding of the volatility process: first, the model is time-reversal asymmetric, similar to financial markets whose time evolution has a preferred direction. Second, it generates a multiplicative, fat-tailed volatility process, that we characterize in detail in the case of exponentially decaying kernels, and which is linked to Pearson diffusions in the continuous limit. Several other interesting properties of QHawkes processes are discussed, in particular the fact that they can generate long memory without necessarily be at the critical point. Finally, we provide numerical simulations of our calibrated QHawkes model, which is indeed seen to reproduce, with only a small amount of quadratic non-linearity, the correct magnitude of fat-tails and time reversal asymmetry seen in empirical time series.