The Case of FBA as a DEX Processing Model

TL;DR

The paper addresses welfare loss and liquidity in DEXes by comparing continuous limit order book processing (CLOB) with frequent batch auction (FBA) in a blockchain context. It builds a theoretical model with validators, order fairness, and front-running considerations, and derives equilibrium characterizations for both processing modes, including OBE under FBA and MPE under CLOB, alongside explicit factors that drive markups and spreads. The empirical component analyzes BTC-USD and ETH-USD trades on dYdX, showing that FBA reduces transaction costs by about 21%–37% compared to CLOB, and that welfare losses tend to be lower for FBA in settings with substantial public information and balanced order flow. The findings suggest that FBA may be more suitable for open, information-disclosive blockchain markets, offering practical implications for DEX design to enhance welfare and liquidity.

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 $21\%-37\%$.

Figures (6)

• Figure 1: The markup difference between CLOB and FBA with respect to public and private information arrivals. Positive regions are where FBA has fewer markups, i.e., less welfare loss. The investor arrival rate ${\color{blue}\lambda_{i}}$ is set to $5000$. Increasing (decreasing) ${\color{blue}\lambda_{i}}$ pushes the surface up (down).
• Figure 3: The markup difference between CLOB and FBA with respect to private information and investor arrivals. The public information arrival rate ${\color{blue}\lambda_{pb}}$ is set to be $5000$. Increasing (decreasing) ${\color{blue}\lambda_{pb}}$ pushes the surface up (down).
• Figure 4: Distribution of realized spread (with outliers trimmed) from simulations on sampled transactions. The bars for depicting the four execution methods are placed on top of each other. The bars for FBA with 15 second auction frequency concentrate on the lower end, while the rest stretch to higher realized spread, with CLOB having the rightmost bars.
• Figure 5: Examples with small parameters. The dashed lines represent that given certain $I, {\color{blue}\lambda_{i}}$, the region above the line means FBA performs better than CLOB, and vise versa.
• Figure 6: The spread difference between CLOB and FBA with respect to public and private information arrivals. The investor arrival rate ${\color{blue}\lambda_{i}}$ is set to be $5000$. Increasing (decreasing) ${\color{blue}\lambda_{i}}$ pushes the surface up (down).

Theorems & Definitions (7)

• Definition 1: Differentially of-ABC cachin2021quick
• Definition 2: Stationary MPE maskin2001markov
• Definition 3: OBE budish2019theory
• Theorem 3.1
• Proof 1
• Theorem 3.2
• Proof 2