Fees & RWAs in Iran: How Fee Caps Shape the Economics of Fractional Units

If a unit is meant to be “small and accessible,” fees decide whether it actually trades—or just exists on paper. In fractional, asset-backed products, a fee cap is not a footnote: it can determine the smallest order that makes economic sense, how often participants can rebalance, and whether prices stay close to reference value.

Introduction

Fractional units are a practical way to broaden access to asset-backed exposure: instead of buying a whole asset (or a large lot), we buy smaller units that represent a share in an underlying pool. This logic is central to many RWA-style designs, where a real-world asset (RWA) is represented digitally so ownership and transfers can be tracked and automated more efficiently.

For fractional markets to work well, however, trading must be frequent enough that price discovery happens and deviations from reference value are corrected. Fees are the quiet variable that can make that possible—or make it uneconomic.

The problem

In many product discussions, we treat fees as “small percentages.” But fractional markets are not linear. A fee schedule that combines a percentage rate with a rial cap (a maximum fee per trade) creates a non-obvious threshold: below a certain trade size, the fee becomes the dominant cost.

This is why a seemingly modest change in fee caps can change the lived reality of a fractional product. The unit size can remain “small” on paper, but trading those units may no longer be rational for retail flows, arbitrageurs, or market makers.

What fee caps are (in plain language) and what a draft/consultation means

A fee cap is a maximum limit applied to transaction fees in a given market segment or instrument. It can appear as:

• a percentage fee that scales with trade value,
• plus (or subject to) a maximum rial amount per trade,
• sometimes with different allocations for buyer and seller.

Iranian media reported the publication of a draft proposal to revise fee caps in multiple market segments, alongside a call for public comments—an early consultation signal rather than a final rule.

We can read this stage as an opportunity to model outcomes before rules are finalized: how different cap levels, splits, and exemptions change behavior for small tickets.

Why small units feel fees more than large tickets

With fractional units, many trades are naturally small. That is the point. But fees do not “feel” proportional at small sizes because:

1. A capped schedule creates a friction floor. Even if the percentage rate is low, the practical, all-in round-trip cost (buy then sell) can be large relative to the small order value.

2. Small mispricings are not actionable. If the product price deviates slightly from its reference value, someone has to trade to close the gap. When fees are high relative to the ticket, the deviation must be larger to justify action.

3. Rebalancing becomes expensive. Retail participation often involves periodic small buys or partial sells. If each action carries meaningful friction, users trade less, and liquidity thins.

Fees, NAV, and “reference value” in fractional RWAs

Many asset-backed products have a reference value: a model-based fair value anchored to the underlying assets, sometimes described as NAV (net asset value) in fund-like structures. In RWA-style designs, the reference value typically depends on verified data about the underlying (holdings, prices, haircuts, costs), and the strongest implementations align that data layer with auditability, custody controls, and reliable price inputs.

Trading price staying close to reference value is not automatic. It usually depends on:

• Arbitrage around reference value (NAV-style arbitrage): participants buy when market price is below reference and sell when above.
• Market making: quoting on both sides and earning spread, while managing inventory and operational risks.

Fees directly reduce the net edge available to both.

A simple model: “minimum sensible order size” (variables, not numbers)

We can use a simple framework to reason about the minimum ticket that makes trading rational.

Let:

• V = trade value (rial value of the order)
• f% = proportional fee rate per side (could differ by side)
• C = fee cap per side (rial)
• s = expected gross price advantage as a fraction of V (e.g., the mispricing we expect to capture, or the spread we expect to earn)
• k = other per-trade costs (operational overhead, financing/inventory costs, latency, compliance operations), expressed in rial

A simplified round-trip fee cost (buy + sell) is:

• **Fees(V) = min(f% · V, C)
  • min(f% · V, C)**

A trade becomes sensible when the expected gross edge covers total costs:

• s · V ≥ Fees(V) + k

This inequality is the core insight:

• When V is small, Fees(V) + k can be a large share of V.
• When caps or side allocations change, Fees(V) changes shape, shifting the minimum V that satisfies the inequality.

In fractional products, this minimum sensible order size is the boundary between “units that trade” and “units that are merely divisible.”

How fee caps affect arbitrage, spreads, and market making incentives

Once we see the minimum sensible order size, three market-structure effects become easier to predict.

1) NAV-style arbitrage becomes weaker

If fees increase the break-even threshold, smaller deviations from reference value persist longer. That can translate into longer-lived premiums/discounts, which retail participants experience as “the unit price is not tracking the underlying.”

2) Spreads widen when net economics tighten

Market makers care about net spread after fees, not quoted spread. If caps or side splits increase the all-in cost, quoting tight markets becomes harder, and the displayed spread tends to widen.

3) Inventory and hedging appetite changes

In asset-backed fractional instruments, market makers often need to hedge or manage inventory using related markets. If those related trades face their own fee caps and frictions, the combined cost can reduce the willingness to provide size—especially at the small end.

Design levers for fractional RWA-style units under fee constraints

If we treat fees as a first-class design constraint, several practical levers become clearer.

1. Choose a unit size aligned with realistic order values. “Smaller” is not always better. A unit that pushes typical orders below the minimum sensible size can reduce effective liquidity.

2. Make fee impact legible in product disclosures. We can describe how fees affect small trades without promising returns: explain the friction floor, typical break-even logic, and scenarios.

3. Support market making with compatible operating layers. Tokenization can improve traceability and programmable settlement; its strongest results come when custody controls, identity/compliance processes, and reliable price/data feeds are aligned so market makers can operate with lower operational k.

4. Engineer for fewer, more meaningful transactions where appropriate. If fees dominate at small tickets, product UX can reduce unnecessary churn (for example, by batching certain actions where allowed by the market rules).

What to monitor in final rules (without assuming approval)

Because the reported change is at the draft/consultation stage, what matters most is the final shape of implementation. Key points to track include:

• Buyer vs seller fee split: changes net round-trip cost and behavior.
• Rial caps and how they apply per side: the cap is often what sets the friction floor.
• Instrument-specific exemptions or special cases: even narrow exemptions can change viability for fractional designs.
• Market-making related costs: quoting obligations, inventory financing, and the fee treatment of hedging legs.

A simple hypothetical example (Iran-relevant)

Assume we have a fractional, asset-backed unit with a published reference value derived from the underlying assets and a clear data methodology.

In normal conditions, the market price deviates slightly from reference value during the day. If fees create a meaningful friction floor for small orders, two things happen:

• Small retail orders stop reacting to small discounts/premiums because the edge does not cover round-trip fees.
• Market makers require a wider gross spread to earn a viable net spread after fees and operational overhead.

The visible symptom is not “the product failed.” The symptom is that price correction slows down and the minimum actionable deviation increases. The unit remains fractional, but fractional participation becomes less frequent.

Risks and limitations

This framework is intentionally simplified. Real markets also depend on:

• settlement cycles and operational timing,
• access to reliable reference-value data,
• inventory constraints and financing conditions,
• regulatory and compliance operations,
• and non-fee frictions such as minimum order rules, tick sizes, and market impact.

Still, the minimum sensible order size model is a useful first pass. It helps us separate what is fundamentally a fee-structure issue from what is a data, custody, or operating-control issue.

Conclusion

Fee caps shape fractional markets by creating a friction floor that small tickets cannot escape. In fractional asset-backed products, that floor can determine the minimum viable ticket size, reduce trading frequency, and weaken the mechanisms that keep prices close to reference value.

As fee-cap revisions are discussed at the draft/consultation stage, we can treat this as the right time to map scenarios and design fractional RWA-style units around realistic trading behavior. When we do, tokenization’s benefits—traceability, programmability, and cleaner operating workflows—land more reliably because the market structure supports them.

FAQ

1) Why do fee caps matter more for fractional units than for large trades?
Because a capped fee schedule creates a practical minimum cost per trade. For small tickets, that minimum cost becomes a large percentage of the order value.

2) How do fees affect NAV tracking in asset-backed products?
They raise the break-even threshold for arbitrage and market making, so small premiums/discounts can persist longer and require larger deviations to be corrected.

3) What is the simplest way to test whether a fractional unit is viable under a fee schedule?
Model a round trip (buy + sell) using the fee formula and compare it with the expected edge (mispricing captured or spread earned), plus operational per-trade costs. The smallest V that clears this is the minimum sensible order size.