Staking Rewards

A reward model where the protocol issues new tokens at a predetermined, constant annual rate. This creates a predictable supply expansion to fund staking rewards.

• Example: Ethereum's original Proof-of-Work issuance and early Proof-of-Stake designs often used fixed annual inflation rates (e.g., 4-5%).
• Rewards are distributed proportionally among all active validators from the new token mint.
• The model provides simple, predictable staking yields but can lead to dilution if not balanced by demand.

A reward model where the rate of new token issuance adjusts algorithmically based on the total proportion of the token supply that is staked. This aims to incentivize a target staking ratio.

• Primary Example: Ethereum's current consensus layer uses this model. The issuance rate decreases as the staked ratio increases beyond a target (~33%), and increases if it falls below.
• This creates economic equilibrium, balancing network security (high stake) with liquidity (unstaked tokens).
• Formulas like the inverse square root of total stake are commonly used for calculation.

A model where validators earn rewards solely or primarily from the transaction fees (gas fees) paid by users, with little to no new token issuance.

• Example: Ethereum's fee burn (EIP-1559) and subsequent tip to validators. After the Merge, validators earn priority fees and MEV (Maximal Extractable Value).
• This model can be deflationary if base fees are burned, as on Ethereum.
• Rewards are variable and directly tied to network usage and congestion.

A fee-based structure within delegated Proof-of-Stake (DPoS) and liquid staking systems, where professional node operators charge a commission on the rewards they earn for stakers.

• Example: Cosmos Hub validators set a commission rate (e.g., 5-20%) on the block rewards and fees they receive.
• Delegators receive the remaining portion. This incentivizes professional node operation and infrastructure maintenance.
• Commission rates are a key competitive factor among validators.

A model where users stake tokens and receive a liquid, tradable derivative token (e.g., stETH, rETH) that accrues staking rewards automatically through rebasing or price appreciation.

• Examples: Lido Finance (stETH), Rocket Pool (rETH), and Coinbase's cbETH.
• Rewards are embedded in the exchange rate between the derivative and the native asset.
• This model enables composability, allowing staked assets to be used simultaneously in DeFi protocols for additional yield.

The inverse of a reward model: a system of penalties that reduce a validator's staked balance for malicious behavior (e.g., double-signing) or downtime. This is a critical component of Proof-of-Stake security.

• Slashing is a severe penalty for provable attacks, often resulting in a large stake deduction and ejection from the validator set.
• Inactivity leaks (on Ethereum) are smaller, proportional penalties for being offline, which gradually reduce stake until the validator is active again.
• These mechanisms financially disincentivize bad actors and network failures.

A penalty mechanism where a validator's staked assets are partially or fully destroyed for protocol violations. Common slashable offenses include:

• Double signing: Proposing or attesting to two conflicting blocks.
• Downtime: Being offline and failing to perform validation duties.
• Governance attacks: Malicious voting or censorship. The severity and conditions for slashing are defined by the specific consensus algorithm (e.g., Ethereum's Proof-of-Stake, Cosmos SDK).

The concentration of staked assets among a small number of validators or staking pools, which threatens network decentralization and censorship resistance. Risks include:

• Single point of failure: A major provider's failure could destabilize the chain.
• Governance capture: Concentrated voting power can sway protocol decisions.
• Reduced liveness: Coordinated downtime among large validators can halt block production. This is often measured by the Gini coefficient or Nakamoto Coefficient of the validator set.

Exposure to bugs or exploits in the staking contract code on which delegated assets are locked. This is a primary risk for staking on liquid staking derivatives (e.g., Lido's stETH) or via DeFi staking pools. Vulnerabilities can lead to:

• Fund theft: Direct draining of the staking pool.
• Logic errors: Incorrect reward distribution or unbonding logic.
• Upgrade risks: Malicious or buggy contract upgrades by governance. Mitigation involves audits, bug bounties, and using time-locked multi-sig for upgrades.

The mandatory locking period during which staked assets cannot be transferred or sold after an unstake request. This creates opportunity cost and market risk.

• Duration: Ranges from days (e.g., Cosmos 21 days) to weeks (Ethereum ~5-6 days).
• Impermanent loss risk: Asset value can decline significantly during the unbonding period.
• Liquidity crisis: In a market downturn, users cannot exit positions quickly. Liquid staking tokens aim to solve this by providing a tradable representation of the staked asset.

The risk that staking rewards, often paid in newly minted tokens, exceed the utility growth of the network, leading to value dilution. Key factors:

• Inflation schedule: A fixed or algorithmic rate of new token issuance to reward stakers.
• Real Yield: Rewards must be evaluated against the inflation rate to determine real return.
• Sustainability: High inflation can pressure token price if demand doesn't keep pace with supply increase. This is a fundamental tokenomics consideration separate from slashing or technical failure.

The technical and administrative hazards of running validator node infrastructure. This risk is borne directly by solo stakers or delegated to staking service providers. Includes:

• Infrastructure failure: Server downtime, DDoS attacks, or cloud provider issues.
• Key management: Loss or compromise of the validator's private keys or mnemonic seed phrase.
• Software updates: Failure to promptly upgrade node software for hard forks or security patches. Professional providers use high-availability setups, HSMs, and 24/7 monitoring to mitigate these risks.