Every Armitage vault depositor is exposed to the following categories of risk. Partial or full loss of funds is possible. Review each category (and how we mitigate it) before allocating capital.
Risk categories
How we mitigate and monitor
Our risk monitoring philosophy is centered on maximum control over the exposure carried by every vault. It splits into two parts: an initial onboarding process, and continuous monitoring of the asset after onboarding.
Initial onboarding
The onboarding process starts with uncovering the core dependencies of a market.
Oracle setup
How well the oracle reflects the liquidatable value of the asset, its security model, and its reliability under stress.
Collateral asset
Analysis of the asset's core dependencies and any underlying assets that affect its valuation; liquidatability; and any off-chain assumption components.
Market state
Health of existing borrower positions and liquidation clusters.
Our due diligence process helps, to a certain extent, minimize smart contract, collateral asset and oracle risk, while liquidity and bad debt risks are managed through ongoing monitoring and structural defenses (allocation strategy, caps, LLTV selection based on the tier's risk profile).
Continuous monitoring
If an asset clears due diligence, we then continuously monitor its health, dependencies, and other relevant metrics through two pillars:
Hypernative
Automated exploit detection used as a security-first signal. Triggers prompt deallocation from affected markets in the event of an asset or dependency exploit, mitigating depositor risk.
Internal risk engine
Internal monitoring focused on a complementary set of metrics, including indicators that can reflect an exploit (e.g. severe divergence between reserves and total supply, or rapid changes in either). Triggers feed into internal review and/or automated action.
Each pillar is focused on minimizing depositor losses when an exploit or economic risk materializes, by reacting and deallocating the vault's assets from the affected market. Together they combine real-time detection of materializing events with early-warning signals on developing risks, with the goal of acting before losses are realized when possible, and minimizing them when they cannot be prevented.