Pairs Trading with Nonlinear and Non-Gaussian State Space Models

TL;DR

This paper introduces a nonlinear, non-Gaussian state-space model for pairs trading, capturing complex spread dynamics and improving trading performance with a new strategy and empirical validation.

Contribution

It develops a novel nonlinear, non-Gaussian state-space model for pairs trading and proposes an optimized trading strategy with empirical testing on multiple asset pairs.

Findings

Achieved up to 31.84% annualized return on tested pairs.

Significantly improved Sharpe ratios compared to existing methods.

Demonstrated effectiveness across diverse asset pairs.

Abstract

This paper studies pairs trading using a nonlinear and non-Gaussian state-space model framework. We model the spread between the prices of two assets as an unobservable state variable and assume that it follows a mean-reverting process. This new model has two distinctive features: (1) The innovations to the spread is non-Gaussianity and heteroskedastic. (2) The mean reversion of the spread is nonlinear. We show how to use the filtered spread as the trading indicator to carry out statistical arbitrage. We also propose a new trading strategy and present a Monte Carlo based approach to select the optimal trading rule. As the first empirical application, we apply the new model and the new trading strategy to two examples: PEP vs KO and EWT vs EWH. The results show that the new approach can achieve a 21.86% annualized return for the PEP/KO pair and a 31.84% annualized return for the EWT/EWH pair. As the second empirical application, we consider all the possible pairs among the largest and the smallest five US banks listed on the NYSE. For these pairs, we compare the performance of the proposed approach with that of the existing popular approaches, both in-sample and out-of-sample. Interestingly, we find that our approach can significantly improve the return and the Sharpe ratio in almost all the cases considered.