Impact of Volatility on Time-Based Transaction Ordering Policies

Battle Count: 84

Highly relevant to quantitative trading in DeFi/MEV context. The paper directly addresses how volatility uncertainty affects the value of latency advantages in CEX-DEX arbitrage, which is a core strategy for HFT firms operating in decentralized exchanges. The variance risk premium concept is directly applicable to understanding why arbitrageurs may underbid relative to expected profits. The findings have implications for: (1) optimal bidding strategies in latency auctions, (2) understanding MEV extraction efficiency, (3) designing better transaction ordering mechanisms, (4) risk management for volatility-dependent trading strategies. The connection between integrated variance and arbitrage profit (LVR theory) is fundamental to DeFi market making and arbitrage strategies.

The theoretical model is relatively straightforward (mean-variance utility in a second-price auction). The empirical implementation requires: (1) heteroskedastic Tobit regression with Student-t errors, which is non-trivial but standard in econometrics; (2) Newey-West variance estimation; (3) proper data matching between auction rounds and high-frequency price data; (4) handling of censored observations at the reserve price; (5) numerical scaling considerations for variables spanning many orders of magnitude. The log-log specification adds robustness but also complexity. Overall, the econometric methods are well-established but require careful implementation due to the extreme scaling of variables and censoring issues.

The paper provides detailed model specifications, data sources (Arbitrum database for bids, Binance for ETH prices), and estimation procedures (Newey-West with T=60, L=5). However, no code repository is mentioned. The data is publicly accessible via Arbitrum's database. The Tobit model with Student-t errors and heteroskedasticity specification is fully described. Key parameters like scaling factors (×10^-15 for bids, ×10^9 for E[IV]√P, ×10^12 for Var(IV)) are provided. The main limitation for reproducibility is the lack of published code and the specific data processing pipeline.