Research on High-Frequency Stock Pair Trading Strategy Based on MS-GARCH Model

Abstract

With the rapid development of high-frequency trading, pair trading, a classic statistical arbitrage strategy, has gradually become a widely applied trading method in the market. This paper focuses on the construction and application of high-frequency stock pair trading strategies based on the MS-GARCH (Markov Switching Generalized Autoregressive Conditional Heteroskedasticity) model. First, we select suitable stock pairs for pair trading by performing cointegration tests on high-frequency stock data. Then, the MS-GARCH model is used to model the price volatility of these stock pairs, and a high-frequency pair trading strategy is designed based on this model. Through simulation experiments, the effectiveness and robustness of the strategy under different market conditions are verified. The research results show that the MS-GARCH model can effectively capture the volatility characteristics of the market, helping investors achieve better returns and risk control in high-frequency trading. Finally, the paper discusses the potential applications and improvements of high-frequency stock pair trading strategies.