Pricing the DeFi Tail: Do Protocols or Depositors Price Operational Risk?

Battle Count: 75

The paper is primarily about operational risk quantification and regulatory policy rather than trading strategies. However, it is relevant to quantitative trading in DeFi contexts: understanding tail risk in lending pools informs position sizing, the risk premium analysis is relevant for yield-farming strategies, and the VaR-based capital framework could inform risk-adjusted return calculations for DeFi strategies. The finding that depositors are undercompensated for operational risk has implications for any strategy that supplies liquidity to DeFi lending markets.

The LDA methodology is well-established in banking risk management. Implementation requires: (1) multi-source data consolidation and deduplication with regex filtering and manual curation, (2) POT-GPD fitting with threshold selection via plateau-stability rule, (3) Negative Binomial frequency fitting, (4) Monte Carlo compound aggregation with exposure caps, (5) per-protocol TVL-share allocation. The statistical methods are standard but the data engineering (deduplication, Basel taxonomy tagging, sector inference) is labor-intensive. No ML training is required; the complexity lies in data preparation and the multi-step statistical fitting pipeline.

The paper assembles a publicly reproducible dataset from seven public sources (DefiLlama, rekt.news, SunWeb3Sec, kismp123, BlockSec, de.fi, SlowMist). Methodology is standard Basel LDA. However, no explicit GitHub repository or code link is provided. The dataset construction involves manual curation (regex filters, curated exclusion lists, per-sector audits) which introduces some subjectivity. The analysis window and data cutoff are clearly specified (2020-01-01 to 2026-05-29).