Statistical arbitrage began in the 1980s and 1990s on Wall Street. Financial experts at firms like Morgan Stanley used basic statistical techniques. They paired stocks to exploit price discrepancies. Technology advancements increased sophistication. The rise of machine learning and enhanced strategies. High-Frequency Trading (HFT) allows trades to execute in microseconds. This significantly improved efficiency and profitability.
In cryptocurrency markets, statistical arbitrage has gained traction. Several key factors contribute:
Pair trading involves selecting two historically correlated cryptocurrencies, such as Bitcoin (BTC) and Ethereum (ETH). When their prices diverge, traders buy the underperforming asset and short the overperforming one. They bet on convergence.
Triangular arbitrage exploits price discrepancies between three different cryptocurrency pairs. Traders execute a series of trades that result in a net profit when inconsistencies in exchange rates are corrected.
Market making involves continuously providing buy and sell orders to capture the bid-ask spread. Combined with STAT ARB, it uses statistical models to optimize order placements and predict price movements.
Learn more about 3 core statistical arbitrage strategies in crypto.