Decentralised Finance and Automated Market Making: Execution and Speculation

TL;DR

This paper analyzes optimal trading and arbitrage in decentralized automated market makers, focusing on convexity costs, and develops efficient strategies with empirical validation for different exchange scenarios.

Contribution

It introduces models for trading in CPMs and centralized exchanges, accounting for convexity costs, and proposes computational strategies with out-of-sample performance evaluation.

Findings

Convexity of trading functions accurately estimates execution costs.

Convexity costs are linear in trade size, nonlinear in liquidity and exchange rate.

Proposed strategies perform well out-of-sample in various market scenarios.

Abstract

Automated market makers (AMMs) are a new prototype of decentralised exchanges which are revolutionising market interactions. The majority of AMMs are constant product markets (CPMs) where exchange rates are set by a trading function. This work studies optimal trading and statistical arbitrage in CPMs where balancing exchange rate risk and execution costs is key. Empirical evidence shows that execution costs are accurately estimated by the convexity of the trading function. These convexity costs are linear in the trade size and are nonlinear in the depth of liquidity and in the exchange rate. We develop models for when exchange rates form in a competing centralised exchange, in a CPM, or in both venues. Finally, we derive computationally efficient strategies that account for stochastic convexity costs and we showcase their out-of-sample performance.