3.5 $BRICS's fair value
Accounting for portfolio risk and expected cashflows
What is $BRICS’s fair (arbitrage-fee) value, and under what assumptions can we estimate this?
$0.446-$1.176 per token at maturity: this is the potential market value investors may realise within the medium term, i.e., assuming key protocol attributes are fully developed and credit origination facilities are fully unlocked in accordance with sovereign guarantee. This valuation differs from the arbitrage price (and fundamental value) offered to early investors at token launch, which is benchmarked to meet Pump.fun's standard circulating supply for "fair launches" and an initial bank origination target of $ 10 million as agreed with Underwriters.
In order to estimate $BRICS's fair market value, we first consider the underwriter's perspective. The underwriter bootstraps the token by fully funding the 'first swap' and absorbing all portfolio losses up to $500 million before (and despite) crypto investors. From this perspective, $BRICS is a “fully funded CDO” referencing trade receivables in a single bespoke tranche.
We then consider the pricing implications for on-chain investors who purchase a second swap from the Underwriter (i.e., this is akin to a "CDO-squared") that enables on-chain investors to earn the full waterfall of spreads from the “inner CDO”.
Features and assumptions for pricing follow:
Token pricing is a dynamic exercise. However, we take a static approach to provide an overview of the methodology.
For simplicity we assume a uni-tranche "outer CDO" structure that abstracts from the "equity" retianed by banks.
We assume that banks originate a notional amount of $2 billion in CDS with a maturity of 3 years. We find this reasonable: underwriters already provide a $500 million guarantee for the protocol to self-fund the balance and fully support the notional.
At maturity, we can assume that the portfolio comprises 50 obligors (i.e., banked exporters) with equal exposure (e.g. $40 million each).
The assets referenced are trade receivables or corporate loans of short duration: < 6 months.
The default probability per company in the portfolio is calculated using econometric and machine learning models – the process is described thoroughly in a separate note. Mining bank data, ChinaAI targets obligors with PD (probability of default) estimates of 0.02%. For caution, we’ll assume 5x that estimate, or 1%, in this exercise. We use static PD estimates based on 5-year panel data (250k observations per year).
The average default correlation between Obligors is 0.2. Due to common macro effects, we assume default probabilities cluster at the tails during severe systemic stress (e.g., the Global Pandemic, the 2008 Crises, etc.). The short duration of the exposure to macro factors mitigates this. Nevertheless, we will consider the impact of a high correlation of 0.4 for stress testing.
Loss Given Default is 40%: i.e., the average recovery rate is 60% across the portfolio – this corroborates findings from the WTO.
Since this inner CDO structure requires a single tranche (equity aside), cumulative losses or correlations across tranches need not be accounted for, and base correlation estimates are unnecessary.
Due to the extensive trade from and to emerging markets, investors expect an annual premium rate of more than 8%. As a stress test, we will consider a high of 12% (US risk-free rate of 4% + Country risk of 4% + credit risk of 2% + liquidity Premium of 1% + 1% buffer). This aligns with monthly premiums of 1% observed and presented in our business model.
The Discount Rate is 3%.
The protocol will issue 1 billion tokens. The arbitrage-free token value is calculated as follows:
Step 1: Calculate Expected Loss
100k Monte Carlo simulations modelled a t-Copula default correlation between the 50 obligors, accounting for individual default probabilities and joint default correlations.
For each default simulation, we then calculated the portfolio loss.
The “expected loss” is the mean of the simulated loss distributions. Moreover, this accounts for tail dependencies.
Step 2: Determine CDO Premium
Annual Premium Rate = 8% of the portfolio’s notional value.
Discounted Premium Income Over 3 Years:
Step 3: Calculate the Net Value of the CDO
Step 4: Price the Token
Step 5: Adjust for bespoke factors
We may consider adjusting the token price based on the following:
Liquidity and risk appetite: We increase the expected annual premium rate from 8% to 12% to account for (a) the lack of liquidity at launch and (b) investors’ perception of emerging market portfolio risk. Applying a 12% premium increases the required token price to $0.672
Correlation risk: Despite accounting for tail dependencies, we might consider even higher correlation risk for scenarios where the portfolio is highly concentrated within a region or industry. Therefore, we might double the default correlation risk to 0.4 and apply the t-copula, which would decrease the token price marginally to $0.670.
$500 million credit enhancement: If TGE investors fully funded the token, what would be the impact of an incremental $500 million credit enhancement via Treasury Bills? This would raise the token’s value to $1.167, even after accounting for the joint default probability (the double copula).
In sum, we estimate a low value of $0.446 and a high of $1.167. Even at the lower bound, given a 3-year horizon and the abovementioned features, early TGE investors are offered $BRICS at 3.4% of its market value. Note that this based on a fundamentals-based prediction — this does not account for the token's specualtive value.
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