Microstructural Foundation of Rough Log-Normal Volatility Models

TL;DR

This paper derives a microstructural basis for the rough Bergomi volatility model by showing how a sequence of order-driven market models converges to a log-normal rough volatility process, with implications for simulation accuracy.

Contribution

It introduces a microstructure model with Poisson-driven order arrivals that converges to the rough Bergomi model, providing new error estimates and a practical simulation approach.

Findings

Convergence of microstructure models to rough volatility.

Novel weak error rates based on Poisson dynamics.

Viable alternative simulation schemes for rough volatility.

Abstract

We establish a microstructural foundation of the rough Bergomi model. Specifically, we consider a sequence of order driven financial market models where orders to buy or sell an asset arrive according to a Poisson process and have a long lasting impact on volatility. Using a recently established C-tightness result for c\`adl\`ag processes we establish the weak convergence of the price-volatility process to a log-normal rough volatility model. Our weak convergence result is accompanied by weak error rates that employ a recently established Clark-Ocone formula for Poisson processes and turn our microstructure model into viable alternative to classical simulation schemes. The weak error rates strongly hinge on Poisson arrival dynamics and are novel to the rough microstructure literature.