Pairs Trading via Nonlinear Autoregressive GARCH Models

Abstract

Pairs trading is a well-established speculative investment strategy in financial markets. However, the presence of extreme structural change in economy and financial markets might cause simple pairs trading signals to be wrong. To overcome this problem in detecting the buy/sell signals, we propose the use of three non-linear models consisting of Kink, Threshold and Markov Switching models. We would like to model the return spread of potential stock pairs by these three models with GARCH effects and the upper and lower regimes in each model are used to find the trading entry and exit signals. We also identify the best fit nonlinear model using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). An application to the Dow Jones Industrial Average (DJIA), New York Stock Exchange (NYSE), and NASDAQ stock markets are presented and the results show that Markov Switching model with GARCH effects can perform better than other models. Finally, the empirical results suggest that the regime-switching rule for pairs trading generates positive returns and so it offers an interesting analytical alternative to traditional pairs trading rules.