Rebalancing-versus-Rebalancing: Improving the fidelity of Loss-versus-Rebalancing

Abstract

Automated Market Makers (AMMs) hold assets and are constantly being rebalanced by external arbitrageurs to match external market prices. Loss-versus-rebalancing (LVR) is a pivotal metric for measuring how an AMM pool performs for its liquidity providers (LPs) relative to an idealised benchmark where rebalancing is done not via the action of arbitrageurs but instead by trading with a perfect centralised exchange with no fees, spread or slippage. This renders it an imperfect tool for judging rebalancing efficiency between execution platforms. We introduce Rebalancing-versus-rebalancing (RVR), a higher-fidelity model that better captures the frictions present in centralised rebalancing. We perform a battery of experiments comparing managing a portfolio on AMMs vs this new and more realistic centralised exchange benchmark-RVR. We are also particularly interested in dynamic AMMs that run strategies beyond fixed weight allocations-Temporal Function Market Makers. This is particularly important for asset managers evaluating execution management systems. In this paper we simulate more than 1000 different strategies settings as well as testing hundreds of different variations in centralised exchange (CEX) fees, AMM fees & gas costs. We find that, under this modeling approach, AMM pools (even with no retail/noise traders) often offer superior execution and rebalancing efficiency compared to centralised rebalancing, for all but the lowest CEX fee levels. We also take a simple approach to model noise traders & find that even a small amount of noise volume increases modeled AMM performance such that CEX rebalancing finds it hard to compete. This indicates that decentralised AMM-based asset management can offer superior performance and execution management for asset managers looking to rebalance portfolios, offering an alternative use case for dynamic AMMs beyond core liquidity providing.