Enjoyoors
  • WHITEPAPER
    • Introduction
    • User-abstracted rehypothecation
      • Giga CDP of Enjoyoors
      • Giga CDP design
      • Deep secondary liquidity for gigaAssets
    • Protocol stability
      • Efficient portfolio management
      • Supply regulation for gigaAssets
      • System-wide insurance
    • Risk management framework
      • Market risks
      • Technical risks
      • gigaAsset allocation rules
    • Decentralized system architecture
      • Public chain infrastructure
      • Orchestrator appchain
      • Oracles
      • Interchain communications
    • Key protocol features
      • Epochs
      • Reward auctions
      • Intelligent peg adapters
    • Further considerations
      • Making RWAs work harder
      • Own DeFi ecosystem
      • Our priorities
  • SYSTEM ARCHITECTURE
    • Overview
    • Public Blockchain Infrastructure
      • Vaults
      • gigaAsset Manager
      • Target Protocols
      • Target Protocol Adapters
      • Intelligent Peg Adapters
      • AMM Pools
      • Rewards Treasury
    • AVS Relayer
      • Relayers
    • Enjoyoors Orchestrator AppChain (L3)
      • Enjoyoors Management System
      • Orchestrator AppChain Layers
      • Security Mechanisms
      • Price Oracle
      • Governance
      • gigaCDP
      • Portfolio Management System
      • Auctions
      • Insurance Pool
    • gigaAsset Bridge
    • gigaAssets
    • Epochs
  • PROTOCOL FLOWS
    • Deposit
    • Withdraw
    • Auction
  • RISKS
    • Protocol risks
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  1. WHITEPAPER
  2. Risk management framework

Market risks

This section examines the risks associated with potential adverse market conditions that could impact the protocol's portfolio of assets. Since these risks are external and unavoidable, Enjoyoors must be thoroughly prepared to mitigate their effects and maintain resilience if they materialize.

Risk

Description

Risk mitigation strategy

Volatility Risk

The higher the volatility of the collateral, the less likely it is to support the entire synthetic debt assigned to it

Introduce a statistical modeling framework to calculate historical volatility measures and quantify risk based on returns distribution. Perform VaR (ES) and scenario analysis to identify applicable Portfolio and Market LTV ratios.

Correlation Risk

The higher the correlation between collateral assets, the more it is susceptible to adverse value changes

Track the historical correlation between collateral assets and synthetic liability. Score assets based on how well they are cointegrated with the synthetic liability. Diversify collateral portfolio based on historical correlations, preferring assets with higher diversification benefits.

Liquidity Risk

The less liquidity available in the market, the more likely the price impact will work against realizable value.

Develop a scoring-based factor model considering liquidity on-chain and off-chain, as well as price impact information.

Exposure Risk

The higher the aggregate relative exposure to the supply of any single asset, the more risk in trying to realize its value

Define allocation limits on a per-asset basis. Take into account liquidity scores and volatility measures calculated above. Additionally, circulating supply data should be considered to identify allocation hard caps.

Synthetic supply shock

A big chunk of gigaAsset supply entering the market in a short period could affect its peg. This could happen due to a major slashing event, liquidation of the whale position on the lending protocol, or just because a malicious actor decided to attack the stability of Enjoyoors’ gigaAsset

Since Enjoyoors protocol controls gigaAssets’ circulating supply - it can decide how and on what terms to bootstrap the Сurve pool (making sure that substantial synths supply chunk in the pool is controlled by the IPA).

Additionally, Enjoyoors governance can limit liquidity exposure to specific protocols to cap the amount of potential gigaAsset circulating supply entering the market.

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Last updated 3 months ago