Path-dependent Fractional Volterra Equations and the Microstructure of Rough Volatility Models driven by Poisson Random Measures

TL;DR

This paper develops a microstructure-based rough volatility model driven by Poisson random measures, capturing persistent and temporary order impacts, and shows convergence to a fractional Heston model with spike clustering.

Contribution

It introduces a novel framework for rough volatility driven by Poisson measures, including existence and uniqueness results for path-dependent Volterra equations, and demonstrates convergence to a fractional Heston model.

Findings

Volatility exhibits spike clustering due to Poisson-driven jumps.

Model captures both persistent and temporary order impacts.

Convergence to fractional Heston model established.

Abstract

We consider a microstructure foundation for rough volatility models driven by Poisson random measures. In our model the volatility is driven by self-exciting arrivals of market orders as well as self-exciting arrivals of limit orders and cancellations. The impact of market order on future order arrivals is captured by a Hawkes kernel with power law decay, and is hence persistent. The impact of limit orders on future order arrivals is temporary, yet possibly long-lived. After suitable scaling the volatility process converges to a fractional Heston model driven by an additional Poisson random measure. The random measure generates occasional spikes and clusters of spikes in the volatility process. Our results are based on novel existence and uniqueness of solutions results for stochastic path-dependent Volterra equations driven by Poisson random measures.