Avareum
  • Introduction
  • Why Avareum?
  • How Avareum Works?
    • Fund Operations
    • Fund Subscription
    • Fund Redemption
    • Fund Rebalancing
    • Fee Management
    • Strategy-Aware Assets
    • Fund Tokens
    • Price Oracle
    • Risks and Mitigation Plans
  • FAQ
  • Investment Strategies
    • Introduction
      • Decentralized Finance
        • Features of Decentralized Finance
        • Decentralized Finance Architecture
        • Decentralized Finance Category
        • DeFi Service Incentive Scheme
      • Stablecoins
    • Avareum Stable Fund
      • Investment Objective
      • Investment Vehicles
      • Eligibility
        • Stable Asset Eligibility
        • Protocol Eligibility
        • Layer 1 Protocol Eligibility
        • Layer 2 Protocol Eligibility
      • Asset Allocation
    • Risk Management
      • Risk Identification
  • Tokenomics
    • AVAR Token
      • Advance Funds Investment Right
    • Token Allocation
      • Token Valuation
    • Token Auction Mechanism
    • Avareum Fundamentals
      • Supply Mechanism
      • Demand Mechanism
      • AVAR Burning Rate
      • Valuation Framework
    • Incentivization and APR
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  • Valuation Method
  • ​Expected Supply and Price Function
  • Visualizing the supply and price function

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  1. Tokenomics
  2. Token Allocation

Token Valuation

Valuation Method

Let price(t),token(t),asset(t)\mathtt{price}(t), \mathtt{token}(t), \mathtt{asset}(t)price(t),token(t),asset(t) be the token price, token supply, and asset under management (AUM), at time ttt, respectively. Since all the management fee is used for buying back and burning. Using the discounted cash flow (DCF) model, the following relation holds.

price(t)⋅token(t)=∫t∞ϕ⋅asset(x)⋅e−λxdx\mathtt{price}(t) \cdot \mathtt{token}(t) = \int_t^\infty \phi \cdot \mathtt{asset}(x) \cdot e^{-\lambda x} dxprice(t)⋅token(t)=∫t∞​ϕ⋅asset(x)⋅e−λxdx

where ϕ\phiϕ is the annual management fee rate and λ\lambdaλ is the discount rate or rate of return on risky assets.

Following the staking-reward model and the buyback-and-burn mechanism, we break down the token supply into

token(t)=mint(t)−burn(t)≥0\mathtt{token}(t) = \mathtt{mint}(t) - \mathtt{burn}(t) \ge 0 token(t)=mint(t)−burn(t)≥0

where mint(t),burn(t)\mathtt{mint}(t), \mathtt{burn}(t)mint(t),burn(t) are the total tokens minted and burned, respectively.

Assume that mint(t)\mathtt{mint}(t)mint(t) is monotonically increasing and bounded over [M0,M∞]⊆R+[M_0, M_\infty] \subseteq \mathcal{R}^+[M0​,M∞​]⊆R+. That is, M0M_0M0​ represents the initial token supply and M∞M_\inftyM∞​ the maximum token supply. The total tokens burned is given by

burn(t)=∫0tϕ⋅asset(x)price(t)dx\mathtt{burn}(t) = \int_0^t \phi \cdot \dfrac{\mathtt{asset}(x)}{\mathtt{price}(t)} dx burn(t)=∫0t​ϕ⋅price(t)asset(x)​dx

The equations above give

price(t)⋅{mint(t)−burn(t)}=∫t∞ϕ⋅asset(x)⋅e−λxdxprice(t)⋅{mint(t)−∫0tϕ⋅asset(x)price(t)dx}=∫t∞ϕ⋅asset(x)⋅e−λxdxprice(t)⋅mint(t)−∫0tϕ⋅asset(x)dx=∫t∞ϕ⋅asset(x)⋅e−λxdx\begin{align*} \mathtt{price}(t) \cdot \Big \{ \mathtt{mint}(t) - \mathtt{burn}(t) \Big \} &= \int_t^\infty \phi \cdot \mathtt{asset}(x) \cdot e^{-\lambda x} dx \\ \mathtt{price}(t) \cdot \Big \{ \mathtt{mint}(t) - \int_0^t \phi \cdot \dfrac{\mathtt{asset}(x)}{\mathtt{price}(t)} dx \Big \} &= \int_t^\infty \phi \cdot \mathtt{asset}(x) \cdot e^{-\lambda x} dx \\ \mathtt{price}(t) \cdot \mathtt{mint}(t) - \int_0^t \phi \cdot \mathtt{asset}(x) dx &= \int_t^\infty \phi \cdot \mathtt{asset}(x) \cdot e^{-\lambda x} dx \end{align*}price(t)⋅{mint(t)−burn(t)}price(t)⋅{mint(t)−∫0t​ϕ⋅price(t)asset(x)​dx}price(t)⋅mint(t)−∫0t​ϕ⋅asset(x)dx​=∫t∞​ϕ⋅asset(x)⋅e−λxdx=∫t∞​ϕ⋅asset(x)⋅e−λxdx=∫t∞​ϕ⋅asset(x)⋅e−λxdx​

​The generic model of the token price therefore is

price(t)=1mint(t){∫0tϕ⋅asset(x)dx+∫t∞ϕ⋅asset(x)⋅e−λxdx}\mathtt{price}(t) = \frac{1}{\mathtt{mint}(t)} \left \{ \int_0^t \phi \cdot \mathtt{asset}(x) dx + \int_t^\infty \phi \cdot \mathtt{asset}(x) \cdot e^{-\lambda x} dx \right \} price(t)=mint(t)1​{∫0t​ϕ⋅asset(x)dx+∫t∞​ϕ⋅asset(x)⋅e−λxdx}

​Expected Supply and Price Function

Assume the following parametric forms.

asset(t)=A⋅eαtmint(t)=M0⋅e−αt+M∞⋅(1−e−αt)\begin{align*} \mathtt{asset}(t) &= A \cdot e^{\alpha t} \\ \mathtt{mint}(t) &= M_0 \cdot e^{-\alpha t} + M_\infty \cdot (1 - e^{-\alpha t}) \end{align*} asset(t)mint(t)​=A⋅eαt=M0​⋅e−αt+M∞​⋅(1−e−αt)​

where α>0\alpha > 0α>0 is the expected (log) growth rate in AUM. Note that the initial and asymptotic conditions already hold.

mint(0)=M0lim⁡t→∞mint(t)=M∞\begin{align*} \mathtt{mint}(0) &= M_0 \\ \lim_{t \to \infty} \mathtt{mint}(t) &= M_\infty \end{align*}mint(0)t→∞lim​mint(t)​=M0​=M∞​​

To show that mint(t)\mathtt{mint}(t)mint(t) is monotonically increasing at a diminishing growth rate.

ddtmint(t)=−α⋅M0⋅e−αt+α⋅M∞⋅e−αt=α⋅(−M0+M∞)⋅e−αt≥0d2dt2mint(t)=α2⋅M0⋅e−αt−M∞⋅α2⋅e−αt≤0=α2⋅(M0−M∞)⋅e−αt≤0\begin{align*} \dfrac{d}{dt} \mathtt{mint}(t) &= -\alpha \cdot M_0 \cdot e^{-\alpha t} + \alpha \cdot M_\infty \cdot e^{-\alpha t} \\ &= \alpha \cdot (-M_0 + M_\infty) \cdot e^{-\alpha t} \ge 0 \\ \dfrac{d^2}{dt^2} \mathtt{mint}(t) &= \alpha^2 \cdot M_0 \cdot e^{-\alpha t} - M_\infty \cdot \alpha^2 \cdot e^{-\alpha t} \le 0 \\ &= \alpha^2 \cdot (M_0 - M_\infty) \cdot e^{-\alpha t} \le 0 \end{align*}dtd​mint(t)dt2d2​mint(t)​=−α⋅M0​⋅e−αt+α⋅M∞​⋅e−αt=α⋅(−M0​+M∞​)⋅e−αt≥0=α2⋅M0​⋅e−αt−M∞​⋅α2⋅e−αt≤0=α2⋅(M0​−M∞​)⋅e−αt≤0​

The initial AUM is targeted at 30 million USD and is expected to grow at a rate of 61.80% annually. Hence, α=log⁡(1+0.6180)≈0.4812\alpha = \log (1 + 0.6180) \approx 0.4812α=log(1+0.6180)≈0.4812. The tokens have an initial supply of 30 million and will be gradually released to reach the maximum supply of 100 million. Hence, in a scale of 10610^6106 (million), we have:

asset(t)=30⋅e0.4812tmint(t)=30⋅e−0.4812t+100⋅(1−e−0.4812t)\begin{align*} \mathtt{asset}(t) &= 30 \cdot e^{0.4812 t} \\ \mathtt{mint}(t) &= 30 \cdot e^{-0.4812 t} + 100 \cdot (1 - e^{-0.4812 t}) \end{align*}asset(t)mint(t)​=30⋅e0.4812t=30⋅e−0.4812t+100⋅(1−e−0.4812t)​

Based on the the generic pricing equation and assuming a discount rate of 100.00% annually i.e. λ=log⁡(1+1.0000)≈0.6931\lambda = \log (1 + 1.0000) \approx 0.6931λ=log(1+1.0000)≈0.6931, we have

price(t)=1mint(t){∫0t0.02⋅30⋅e0.4812xdx+∫t∞0.02⋅30⋅e−0.2119xdx}=1mint(t){1.2469⋅(e0.4812t−1)+2.8311⋅e−0.2119t}=1.2469⋅(e0.4812t−1)+2.8311⋅e−0.2119t30⋅e−0.4812t+100⋅(1−e−0.4812t) \begin{align*} \mathtt{price}(t) &= \dfrac{1}{\mathtt{mint}(t)} \left \{ \int_0^t 0.02 \cdot 30 \cdot e^{0.4812 x} dx + \int_t^\infty 0.02 \cdot 30 \cdot e^{-0.2119 x} dx \right \} \\ &= \dfrac{1}{\mathtt{mint}(t)} \left \{ 1.2469 \cdot (e^{0.4812 t} - 1) + 2.8311 \cdot e^{-0.2119 t} \right \} \\ &= \dfrac{ 1.2469 \cdot (e^{0.4812 t} - 1) + 2.8311 \cdot e^{-0.2119 t} }{ 30 \cdot e^{-0.4812 t} + 100 \cdot (1 - e^{-0.4812 t}) } \end{align*}price(t)​=mint(t)1​{∫0t​0.02⋅30⋅e0.4812xdx+∫t∞​0.02⋅30⋅e−0.2119xdx}=mint(t)1​{1.2469⋅(e0.4812t−1)+2.8311⋅e−0.2119t}=30⋅e−0.4812t+100⋅(1−e−0.4812t)1.2469⋅(e0.4812t−1)+2.8311⋅e−0.2119t​​

Visualizing the supply and price function

1. Expected supply over time

The token emission is scheduled based on all the assumptions above. This schedule represents only the emission plan yet does not take into account the burning mechanism.

The following plot shows the token supply function, designated by:

mint(t)=30⋅e−0.4812t+100⋅(1−e−0.4812t)\mathtt{mint}(t) = 30 \cdot e^{-0.4812 t} + 100 \cdot (1 - e^{-0.4812 t})mint(t)=30⋅e−0.4812t+100⋅(1−e−0.4812t)

2. Expected price over time with burning mechanism

The expected pricing is based on all the assumptions above and evaluated at a unit price-to-earning ratio (P/E). Note also that we assume that the tokens initially offered to Backers Sale and Avareum Lab are available without any promotion terms and conditions.

The following plot shows the token price function, designated by:

price(t)=1.2469⋅(e0.4812t−1)+2.8311⋅e−0.2119t30⋅e−0.4812t+100⋅(1−e−0.4812t)\mathtt{price}(t) = \dfrac{ 1.2469 \cdot (e^{0.4812 t} - 1) + 2.8311 \cdot e^{-0.2119 t} }{ 30 \cdot e^{-0.4812 t} + 100 \cdot (1 - e^{-0.4812 t}) }price(t)=30⋅e−0.4812t+100⋅(1−e−0.4812t)1.2469⋅(e0.4812t−1)+2.8311⋅e−0.2119t​

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Last updated 3 years ago

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