Interactive Pricing Model

Methodology & Assumptions

This model blends three independent price estimates and weights them differently depending on where AMP is in its lifecycle. Early on, collateral value (CLW) matters most. As the network matures, fee revenue via discounted cash flow (DCF) becomes dominant. The buy-pressure model (ARMA) captures momentum from protocol-driven buybacks. None of these models predict market sentiment or speculative trading; they estimate fundamental value only.

1. Three-Model Synthesis. Following the Amp Whitepaper's framework (§4–5), we estimate token price as a time-weighted blend of three independent models. Let w_CLW(t), w_ARMA(t), w_DCF(t) denote year-dependent weights that sum to 1. Early years favor the collateral-locked-value model (CLW: 60%→5%); maturity shifts weight toward discounted cash flow (DCF: 30%→70%). The ARMA buy-pressure model (10%→25%) captures momentum from protocol-driven demand. The blended price at year t is P(t) = Σ_i w_i(t) · P_i(t).

2. CLW Asset Pricing (§5.2.1, Eq. 5). Network value is V(t) = β_NV(t) · GMV(t) · c(t), where β_NV is the network-to-volume ratio and c(t) = 1 − r − β_NV^t · f is a carry-cost discount incorporating the hurdle rate r = 4.5% and fee rate f = 0.6375%. β_NV decays from 0.85 → 0.55 via a sigmoid schedule: β(t) = β_start − (β_start − β_end) · σ(t), where σ(t) = 1/(1 + e^{−0.35·(t−10)}). This captures the whitepaper's expectation that collateral efficiency improves as the network matures (§3.2.2). Price per liquid token:P_CLW = V(t) / L(t).

3. ARMA Buy Pressure (§5.2.2, Eq. 6–8). We model purchase intensity as an AR(1) process: μ_t = β · μ_t−1 + λ₀(1 − β), where λ₀ = (annual buyback) / (total supply) is the base purchase rate. β uses the same sigmoid decay as CLW, ensuring consistency across modules. Time-varying intensity λ(t) = (1 + μ_t) · λ₀ generates a premium ratio λ(t)/λ₀, dampened at 40% pass-through to CLW price: P_ARMA = P_CLW · (1 + 0.4 · (premium − 1)).

4. DCF Maturity (Gordon Growth). At maturity, token value converges to a yield-based valuation. We apply the Gordon Growth Model:P = (buyback / staked supply) / (r − g), where the discount rate r decays linearly from 8% → 6% over 20 years (reflecting decreasing risk as the network proves itself) and terminal growth g = 2%. The result is adjusted by staked fraction to reflect dilution across total supply.

5. Supply Schedule (FSInsight). Circulating supply follows the FSInsight token release curve with linear interpolation between anchors: 51.2B (2025), 60.7B (2030), 67.5B (2035), 78B (2040), 99.72B (2045). This replaces earlier models that assumed a constant 84.28B circulating supply. Total supply is fixed at 99,720,005,508 AMP per the token contract.

6. Dynamic Staking with Yield Feedback (§5.3, §3.2.2). Base staking rate follows an S-curve from the initial rate (default 28%) to ceiling (50%), parameterized by midpoint = 10 and steepness k = 0.35. A yield-feedback mechanism implements the whitepaper's prediction that staker yields converge to a hurdle rate (§5.3): when prior-year staker yield falls below the DeFi benchmark (4.5%), yield-sensitive stakers (15% of the base) exit proportionally. Pullback = 0.15 · min(1, |spread| / benchmark). Year 1 uses no feedback (no prior yield data). This creates the self-correcting collateral dynamic described in §3.2.2.

7. GMV Assumptions. Year 1 default: $1B. Year 10: $250B. Year 20: $3.14T. Interpolated via logistic S-curve with floor growth rate of 5%. These are user-adjustable. Year 1 is informed by Flexa's confirmed merchant footprint (~$360B combined merchant revenue × estimated capture rate). All GMV figures are modeling assumptions. Actual network adoption is unknown and will vary.

8. Fee Economics. Merchant fee rate: 0.6375% blended average, derived from publicly available merchant category data, not a figure stated in the whitepaper. Buyback allocation: 90% of fee revenue purchases AMP from open market. This is informed by the whitepaper's reward mechanism (§4) but is a modeling choice, not a fixed protocol parameter. Slippage (price impact of large trades): 0% (conservative; actual slippage depends on market depth).

9. Market Context. US Credit Card TAM: $10.77T (Year 0), growing 5.5%/yr per Nilson Report. Global Card TAM: $28.26T, growing 7%/yr. Market share figures show what fraction of card payments would flow through Flexa at each GMV level. They are not adoption forecasts.

10. Scenario Bands. Bear = 40% of base price; Bull = 160% of base price. These are symmetric scenario multipliers reflecting model uncertainty, not probabilistic confidence intervals.

Note: This model operates in a vacuum. It projects price based solely on the flywheel mechanism (GMV, fee revenue, buyback, staking dynamics). Real-world price will also be affected by external market forces: speculative buying/selling, broader crypto market cycles, macroeconomic conditions, and exchange liquidity, none of which are captured here.

This model is for educational and research purposes only and does not constitute financial advice. All revenue estimates are unverified projections. The model implements the theoretical framework of the Amp Whitepaper (2020) with the assumptions enumerated above. Source: "Amp: Digital Collateral for Real-World Transfers" (2020).