The Case of FBA as a DEX Processing Model

TL;DR

This paper compares continuous and discrete order matching models in blockchain DEXs, showing that frequent batch auctions (FBA) reduce welfare loss and improve liquidity, especially under certain market conditions, with empirical cost reductions of up to 37%.

Contribution

It demonstrates that FBA, as a discrete matching model, outperforms continuous processing in welfare and liquidity, providing new insights into optimal DEX design.

Findings

FBA reduces welfare loss compared to continuous matching.

FBA improves liquidity in typical and specific market scenarios.

Empirical analysis shows FBA can lower transaction costs by up to 37%.

Abstract

We investigate the welfare loss of continuous and discrete order matching models in blockchain-based decentralized exchanges (DEX) that utilize order books to record outstanding orders. Continuous processing matches each incoming transaction against the current order book. The discrete processing model, i.e., frequent batch auction (FBA), executes transactions discretely in batches with a uniform price double auction: Orders are first matched according to price, then the exact transaction order if competing orders specify the same price. We find that FBA imposes less welfare loss and provides better liquidity than continuous processing in typical scenarios, e.g., when few parties are privately informed about asset valuations. Even otherwise, it achieves better social welfare and liquidity provision in the following settings: when price takers and public information reflecting asset value changes arrive sufficiently frequently compared to private information, when the priority fees (for faster transaction inclusion into blockchains) are small, or when the market is more balanced on both buy and sell sides. Our empirical analysis on the BTC-USD and ETH-USD transactions on a DEX named dYdX indicates that FBA can reduce transaction costs by $21\%-37\%$.