What is Statistical Arbitrage?

Here's a quick overview of how statistical arbitrage works:

Pair Selection: Quants select a pair (or sometimes a group) of related securities that they believe exhibit a statistical relationship. This relationship can be based on factors such as historical price patterns, correlation analysis, or fundamental characteristics.

Model Development: A quantitative model is developed to estimate the expected behavior of the selected pair of securities. This model can be based on statistical techniques, time series analysis, machine learning algorithms, or other quantitative methods.

Deviation Detection: The model continuously monitors the prices or other relevant indicators of the selected securities. When a deviation from the expected relationship is detected, a trading signal is generated. The deviation can be measured by statistical metrics such as z-scores, moving averages, or other quantitative indicators.

Trade Execution: When a trading signal is generated, the strategy triggers trades to profit from the expected convergence or divergence of prices. The strategy can involve buying one security and simultaneously selling the other in the pair, aiming to capture the price discrepancy.

Risk Management: As with any trading strategy, risk management is crucial. Statistical arbitrage strategies often employ risk controls, such as stop-loss orders, position sizing rules, and portfolio diversification, to manage the potential downside risks.

It's important to note that statistical arbitrage strategies are typically executed with high-speed trading systems and rely on the ability to process large volumes of data in real-time. Quants continuously refine their models and strategies to adapt to changing market conditions and to stay ahead of the competition. These strategies can be applied to various financial instruments, such as stocks, futures, options, or currencies. 

Statistical arbitrage is covered in more detail in module 6 of the CQF program.