Semi Markov model for market microstructure

TL;DR

This paper introduces a Markov renewal process-based model for tick-by-tick asset prices that captures microstructure noise, volatility clustering, and dependence between price jumps and timing, supported by statistical estimation methods and empirical validation.

Contribution

It presents a novel Markov renewal process model for market microstructure, incorporating price jump dependence, volatility clustering, and providing estimation procedures.

Findings

Model reproduces microstructure noise and volatility clustering.

Closed-form formulas for the mean signature plot.

Consistent with empirical Euribor future data.

Abstract

We introduce a new model for describing the fluctuations of a tick-by-tick single asset price. Our model is based on Markov renewal processes. We consider a point process associated to the timestamps of the price jumps, and marks associated to price increments. By modeling the marks with a suitable Markov chain, we can reproduce the strong mean-reversion of price returns known as microstructure noise. Moreover, by using Markov renewal processes, we can model the presence of spikes in intensity of market activity, i.e. the volatility clustering, and consider dependence between price increments and jump times. We also provide simple parametric and nonparametric statistical procedures for the estimation of our model. We obtain closed-form formula for the mean signature plot, and show the diffusive behavior of our model at large scale limit. We illustrate our results by numerical simulations, and that our model is consistent with empirical data on the Euribor future.