It is 2023, and we’re all aware of staking. It is 2023, and we’re all learning about restaking.

There is one key difference that separates staking from restaking. When you stake, there is only one way to do it - secure the base chain.

But, unlike staking, restaking requires you to pick and select which networks you want to secure or a combination of them.

These decentralized services that will inherit Ethereum's security via EigenLayer are called AVS - Actively Validated Services. Each AVS presents a unique entry point for rewards and relevant slashing risks.

We define restaking strategy as a unique position where the user decides to secure one of the many AVS or a combination of AVS. The number of strategies possible for securing an activity increases with the number of AVSs.

Moving on, the ideal case is one wherein users can 100% secure all AVSs, operators behave in an honest and all slashing risk is minimum. However, building a robust restaking system is one where restakers must be able to quantify slashing risk and choose to secure certain AVSs that have a favorable r/r while avoiding others that have a less attractive r/r. Consequentially, a restaker needs to construct a restaking portfolio optimizing for risk-adjusted returns based on their risk profile.

Let’s define the problem statement here first.

To quantify, the number of possible strategies is expressed with the equation:

2^n -1

This equation considers all possible combinations of securing AVS, from securing one to securing all AVS, n being the number of AVS.

Consider a scenario with just 3 AVS - A, B, C. Number of strategies possible are

1. AVS A: Securing only AVS A

2. AVS B: Securing only AVS B

3. AVS C: Securing only AVS C

4. AVS A and AVS B: Securing both AVS A and AVS B together

5. AVS A and AVS C: Securing both AVS A and AVS C together

6. AVS B and AVS C: Securing both AVS B and AVS C together

7. AVS A, AVS B, and AVS C: Securing all three AVS simultaneously

In this scenario, there are 7 different strategies to secure the activity, based on whether you secure each AVS or their combinations.

Now, as the number of AVS increases, so will the number of strategies. In fact, this increases exponentially.

At 15 AVS, we will have 32,767 strategies possible. As you can imagine, evaluating the right combination of AVS while optimizing for risk-reward and actively managing these positions gets more tenuous as more AVS join the network.

Risks of securing any AVS are mainly of 2 categories - Slashing Risk and Liquidity Risk (of AVS rewards). And while liquidity conditions help us estimate a cap on how much capital can we allocate to an AVS, it doesn’t directly cause a default/loss of principal. Hence, we prioritize slashing risks to construct a restaking portfolio of AVS.

We propose a new parameter called ‘MaxLoss’. MaxLoss will serve as an indicator for slashing risk for any strategy securing one or more AVS. Therefore, the higher the MaxLoss riskier the strategy.

MaxLoss = maximum percentage of stake that can be frozen/slashed by each AVS

E.g. If the network decides to freeze 2% of staked ETH for downtime and 7% for double signing, then MaxLoss = 7% for the network.

In many ways, we can compare MaxLoss to the probability of default in a standard debt position, but instead, you only lose a partial portion of your collateral. The ideal restaker will also unstake immediately after one slashing event, rather than hold & watch their stake slowly go to zero.

This means, MaxLoss of a strategy securing n AVSs is the summation of MaxLoss assigned to every individual AVS.

$MaxLoss (Strategy) = \sum_{i=1}^{n} MaxLoss(AVS_i)$

MaxLoss helps us quantify slashing risk for a restaking portfolio. It helps us identify the additional risk for every new AVS we secure, or the tradeoff when we pick one AVS over another.

Risk is, however, not the only metric we require when we construct a portfolio. The other component is (you guessed it!) rewards. Hence, enter RAR.

Let’s define the Sharpe ratio for a typical financial investment (Thanks ChatGPT):

The Sharpe ratio is a measure of risk-adjusted return in finance. It is used to evaluate the performance of an investment by taking into account both the return and the risk of that investment.

The higher the Sharpe ratio, the better the investment's risk-adjusted return. It allows investors to compare different investments and determine which one provides the best return for a given level of risk.

In other words, the Sharpe Ratio is an indicator of how fruitful your investment is for the risk you’re taking. Better the Sharpe Ratio the better your portfolio.

Now let’s try to estimate an equivalent Sharpe Ratio for restaking, or rather Risk-Adjusted Reward Ratio (RAR):

A user would want to maximize RAR of its restaking portfolio and allocate more to AVS that increase RAR i.e. offer higher rewards and pose lower slashing risk.

Thanks for reading. In the next few pieces, we will talk about we will talk about how we can allocate weights to individual strategies, construct a diverse portfolio, the covariance between restaking positions, and also simulating restaking strategies.

It is 2023, and we’re all aware of staking. It is 2023, and we’re all learning about restaking.

There is one key difference that separates staking from restaking. When you stake, there is only one way to do it - secure the base chain.

But, unlike staking, restaking requires you to pick and select which networks you want to secure or a combination of them.

These decentralized services that will inherit Ethereum's security via EigenLayer are called AVS - Actively Validated Services. Each AVS presents a unique entry point for rewards and relevant slashing risks.

We define restaking strategy as a unique position where the user decides to secure one of the many AVS or a combination of AVS. The number of strategies possible for securing an activity increases with the number of AVSs.

Moving on, the ideal case is one wherein users can 100% secure all AVSs, operators behave in an honest and all slashing risk is minimum. However, building a robust restaking system is one where restakers must be able to quantify slashing risk and choose to secure certain AVSs that have a favorable r/r while avoiding others that have a less attractive r/r. Consequentially, a restaker needs to construct a restaking portfolio optimizing for risk-adjusted returns based on their risk profile.

Let’s define the problem statement here first.

To quantify, the number of possible strategies is expressed with the equation:

2^n -1

This equation considers all possible combinations of securing AVS, from securing one to securing all AVS, n being the number of AVS.

Consider a scenario with just 3 AVS - A, B, C. Number of strategies possible are

1. AVS A: Securing only AVS A

2. AVS B: Securing only AVS B

3. AVS C: Securing only AVS C

4. AVS A and AVS B: Securing both AVS A and AVS B together

5. AVS A and AVS C: Securing both AVS A and AVS C together

6. AVS B and AVS C: Securing both AVS B and AVS C together

7. AVS A, AVS B, and AVS C: Securing all three AVS simultaneously

In this scenario, there are 7 different strategies to secure the activity, based on whether you secure each AVS or their combinations.

Now, as the number of AVS increases, so will the number of strategies. In fact, this increases exponentially.

At 15 AVS, we will have 32,767 strategies possible. As you can imagine, evaluating the right combination of AVS while optimizing for risk-reward and actively managing these positions gets more tenuous as more AVS join the network.

Risks of securing any AVS are mainly of 2 categories - Slashing Risk and Liquidity Risk (of AVS rewards). And while liquidity conditions help us estimate a cap on how much capital can we allocate to an AVS, it doesn’t directly cause a default/loss of principal. Hence, we prioritize slashing risks to construct a restaking portfolio of AVS.

We propose a new parameter called ‘MaxLoss’. MaxLoss will serve as an indicator for slashing risk for any strategy securing one or more AVS. Therefore, the higher the MaxLoss riskier the strategy.

MaxLoss = maximum percentage of stake that can be frozen/slashed by each AVS

E.g. If the network decides to freeze 2% of staked ETH for downtime and 7% for double signing, then MaxLoss = 7% for the network.

In many ways, we can compare MaxLoss to the probability of default in a standard debt position, but instead, you only lose a partial portion of your collateral. The ideal restaker will also unstake immediately after one slashing event, rather than hold & watch their stake slowly go to zero.

This means, MaxLoss of a strategy securing n AVSs is the summation of MaxLoss assigned to every individual AVS.

$MaxLoss (Strategy) = \sum_{i=1}^{n} MaxLoss(AVS_i)$

MaxLoss helps us quantify slashing risk for a restaking portfolio. It helps us identify the additional risk for every new AVS we secure, or the tradeoff when we pick one AVS over another.

Risk is, however, not the only metric we require when we construct a portfolio. The other component is (you guessed it!) rewards. Hence, enter RAR.

Let’s define the Sharpe ratio for a typical financial investment (Thanks ChatGPT):

The Sharpe ratio is a measure of risk-adjusted return in finance. It is used to evaluate the performance of an investment by taking into account both the return and the risk of that investment.

The higher the Sharpe ratio, the better the investment's risk-adjusted return. It allows investors to compare different investments and determine which one provides the best return for a given level of risk.

In other words, the Sharpe Ratio is an indicator of how fruitful your investment is for the risk you’re taking. Better the Sharpe Ratio the better your portfolio.

Now let’s try to estimate an equivalent Sharpe Ratio for restaking, or rather Risk-Adjusted Reward Ratio (RAR):

A user would want to maximize RAR of its restaking portfolio and allocate more to AVS that increase RAR i.e. offer higher rewards and pose lower slashing risk.

Thanks for reading. In the next few pieces, we will talk about we will talk about how we can allocate weights to individual strategies, construct a diverse portfolio, the covariance between restaking positions, and also simulating restaking strategies.