Dynamic modeling of mean-reverting spreads for statistical arbitrage

TL;DR

This paper develops a real-time, adaptive state-space modeling approach for mean-reverting spreads in statistical arbitrage, enabling quick detection of market inefficiencies and providing uncertainty estimates.

Contribution

It introduces a time-dependent, online estimation algorithm within a state-space framework for spreads, enhancing adaptability and computational speed for high-frequency trading.

Findings

Effective real-time spread estimation demonstrated with Monte Carlo simulations.

Application to historical equity data shows accurate mean-reversion detection.

Provides uncertainty measures for model parameters, aiding risk assessment.

Abstract

Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is a noisy realization of some hidden states. Real-time estimation of the unobserved spread process can reveal temporary market inefficiencies which can then be exploited to generate excess returns. Building on previous work, we embrace the state-space framework for modeling spread processes and extend this methodology along three different directions. First, we introduce time-dependency in the model parameters, which allows for quick adaptation to changes in the data generating process. Second, we provide an on-line estimation algorithm that can be constantly run in real-time. Being computationally fast, the algorithm is particularly suitable for building aggressive trading strategies based on high-frequency data and may be used as a monitoring device for mean-reversion. Finally, our framework naturally provides informative uncertainty measures of all the estimated parameters. Experimental results based on Monte Carlo simulations and historical equity data are discussed, including a co-integration relationship involving two exchange-traded funds.