Pairs Trading and Volatility

Abstract

The rise of the COVID-19 pandemic has brought newfound volatility to financial markets across the world. This thesis explores and evaluates pairs trading strategies, using distance, correlation, and cointegration as metrics to quantify comovement between US public equities. We test these algorithms’ performances on historical data between 1990 and 2022. This time horizon allows us to simulate these algorithms’ returns during the lead-up to and burst of the dot-com bubble, as well as during the global rise of COVID-19 and the present-day Russian invasion of Ukraine. We further demonstrate that our chosen methods remain robust to transaction costs during these historical periods of economic downturn and heightened financial market volatility. In the context of quantitative finance’s explosive growth, this thesis demonstrates that significant statistical arbitrage opportunities still endure in modern financial markets.