Detecting Depegs: Towards Safer Passive Liquidity Provision on Curve Finance

TL;DR

This paper develops a Bayesian changepoint detection method to identify potential stablecoin and LSD depegs on Curve's pools, providing early alerts to liquidity providers to enhance pool safety.

Contribution

It introduces a suite of metrics and a Bayesian detection algorithm specifically designed for early depeg detection in stablecoin pools, with real-time alert capabilities.

Findings

Successfully detected the USDC depeg 5 hours before it dipped below 99 cents.

Achieved few false alarms over 17 months of testing.

Demonstrated applicability to multiple stablecoins and LSDs.

Abstract

We consider a liquidity provider's (LP's) exposure to stablecoin and liquid staking derivative (LSD) depegs on Curve's StableSwap pools. We construct a suite of metrics designed to detect potential asset depegs based on price and trading data. Using our metrics, we fine-tune a Bayesian Online Changepoint Detection (BOCD) algorithm to alert LPs of potential depegs before or as they occur. We train and test our changepoint detection algorithm against Curve LP token prices for 13 StableSwap pools throughout 2022 and 2023, focusing on relevant stablecoin and LSD depegs. We show that our model, trained on 2022 UST data, is able to detect the USDC depeg in March of 2023 at 9pm UTC on March 10th, approximately 5 hours before USDC dips below 99 cents, with few false alarms in the 17 months on which it is tested. Finally, we describe how this research may be used by Curve's liquidity providers, and how it may be extended to dynamically de-risk Curve pools by modifying parameters in anticipation of potential depegs. This research underpins an API developed to alert Curve LPs, in real-time, when their positions might be at risk.