Scaling limits for Hawkes processes and application to financial statistics

Abstract

We prove a law of large numbers and a functional central limit theorem for multivariate Hawkes processes observed over a time interval $[0,T]$ in the limit $T\to \infty$. We further exhibit the asymptotic behaviour of the covariation of the increments of the components of a multivariate Hawkes process, when the observations are imposed by a discrete scheme with mesh $\Delta$ over $[0,T]$ up to some further time shift $\tau$. The behaviour of this functional depends on the relative size of $\Delta$ and $\tau$ with respect to $T$ and enables to give a full account of the second-order structure. As an application, we develop our results in the context of financial statistics. We introduced in a previous work a microscopic stochastic model for the variations of a multivariate financial asset, based on Hawkes processes and that is confined to live on a tick grid. We derive and characterise the exact macroscopic diffusion limit of this model and show in particular its ability to reproduce important empirical stylised fact such as the Epps effect and the lead-lag effect. Moreover, our approach enable to track these effects across scales in rigorous mathematical terms.

References in corpus (2)

• Non-parametric kernel estimation for symmetric Hawkes processes. Application to high frequency financial data
• High Frequency Lead/lag Relationships - Empirical facts

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• Limit theorems for a discrete-time Hawkes process
• Market impact as anticipation of the order flow imbalance
• Some asymptotic results for nonlinear Hawkes processes
• Ergodicity and scaling limit of a constrained multivariate Hawkes process
• Fine-Grained $\epsilon$-Margin Closed-Form Stabilization of Parametric Hawkes Processes