This paper develops a pairs trading framework based on a mean-reverting jump-diffusionmodel and applies it to minute-by-minute data of the S&P 500 oil companies from 1998to 2015. The established statistical arbitrage strategy enables us to perform intraday andovernight trading. Essentially, we conduct a 3-step calibration procedure to the spreads of allpair combinations in a formation period. Top pairs are selected based on their spreads’ mean reversion speed and jump behavior. Afterwards, we trade the top pairs in an out-of-sampletrading period with individualized entry and exit thresholds. In the back-testing study, thestrategy produces statistically and economically significant returns of 60.61 percent p.a. andan annualized Sharpe ratio of 5.30, after transaction costs. We benchmark our pairs tradingstrategy against variants based on traditional distance and time-series approaches and findits performance to be superior relating to risk-return characteristics. The mean-reversionspeed is a main driver of successful and fast termination of the pairs trading strategy.Keywords: Finance, statistical arbitrage, pairs trading, high-frequency data,jump-diffusion model, mean-reversion.