Navigating Risk Through HyperLend’s Growth Phase

An Overview of Block Analitica’s Risk Management at HyperLend

Apr 29, 2025

We at Block Analitica have been working closely with HyperLend since February 2025. Since their mainnet launch on March 24, HyperLend has achieved more than $100 million in total market size and over 6,000 onchain users.

HyperLend’s rapid expansion in a new HyperEVM environment increases the need for disciplined risk management. This post outlines how Block Analitica has overseen HyperLend’s risk management framework throughout its growth phase.

Prioritizing Safety While Bootstrapping Growth

Given the early-stage liquidity of Hyperliquid’s HyperEVM, we decided to start conservatively with only $HYPE and $wstHYPE as supported assets, proposing the following initial parameters:

Borrow caps: 60K wstHYPE / 130K HYPE
LTV: 50% for both assets

Acting as a lifeline for timely liquidations, DEX liquidity can change right before market shocks. The chart below shows an example of how significantly the price impact in stHYPE/HYPE HyperSwap pool can change, from early April.

Besides standard protocol-incentivized liquidations, DEX liquidity plays a crucial role in providing the possibility for partial unwinding of (leveraged) CDPs (i.e., partial self-liquidations) to mitigate liquidations (and thus liquidation fees). We therefore monitor the collateral-ratio distribution of retail wallets that are using HyperLend for looping, making sure that there is enough liquidity to unwind positions that have a high collateral ratio.

Scaling Responsibly with Demand

As supply and demand on HyperLend grew, we incrementally raised the borrow caps based on the latest available onchain data. For example, over a two week period, borrow caps were raised over 6x, from 130K HYPE to 788K HYPE. Throughout this process, we closely monitored onchain liquidity and simulated how the stHYPE/HYPE peg responded to shocks in secondary markets.

Optimizing Primary Use Cases

Although HyperLend has gained new use cases over time, such as the onboarding of Hyperunit’s UBTC and UETH, we initially chose to concentrate on optimising the wstHYPE/HYPE looping strategy. We managed risk parameters to achieve:

Sustainability and profitability by keeping the borrow rate < stHYPE’s APY
Systemic risk prevention by discouraging unsustainably high leverage

Risk Modeling

We are still in the early stages of the HyperEVM. While the ecosystem is thriving with new ideas, protocols, and applications, liquidity remains sparse and concentrated in a few areas. To account for this, we have utilized Monte Carlo simulations to stress-test:

wstHYPE peg stability (temporary depegs, secondary market TSI)
Partial looping behaviors (users reserving collateral for future loops)
DEX liquidity shocks (impact on DEX liquidity when market isn't performing well and LP behavior)