How to Value Digital Tokens: A 5-Step Fair Value Framework

The development of digital financial assets has fundamentally changed the financial ecosystem, challenging traditional valuation methodologies and introducing new complexities for both analysts and investors. Digital assets — which include cryptocurrencies, stablecoins, non-fungible tokens (NFTs), and tokenized securities — are now used in business transactions, investment portfolios, and capital formation. Even with their growing use, valuation remains clouded with uncertainty due to the absence of standardized valuation frameworks and methods, a market infrastructure that is often fragmented, and limited technological transparency.

For financial analysts, this evolution presents both an opportunity and a challenge. Traditional valuation concepts still apply, but they must be adapted to a market where observable inputs, governance structures, and trading conventions differ sharply from established asset classes. This post explains how to approach fair value measurement for digital tokens under ASC 820 and IFRS 13, highlighting key areas of professional judgment such as identifying principal markets, determining exit prices, and assessing discounts for illiquidity or lock-ups. The discussion is organized into five steps that mirror the valuation process: from identifying the token to determining its fair value under varying market and liquidity conditions.

Unlike traditional financial assets, many digital instruments often lack established market oversight, observable market inputs, or common and consistent rights of ownership. Tokenized securities may represent beneficial interests in special purpose vehicles, fractional equity, or synthetic exposures, each with distinct legal and economic implications.

Cryptocurrencies and NFTs, by contrast, are traded across decentralized exchanges with varying degrees of price transparency and custody risk, and can be susceptible to manipulation. These factors complicate the application of established valuation methods such as those described in ASC 820 and IFRS 13 Fair Value Measurements, which rely on market participant assumptions and observable inputs. These criteria may be absent or unreliable with digital assets.

Even with these significant challenges, the traditional valuation approaches still apply to the valuation of digital assets. Tokens that generate cash flows to their holder may lend themselves to the use of a discounted cash flow method of valuation. Certain digital assets are actively traded on certain exchanges, which may be useful to provide inputs for relative valuation methodologies. Finally, developers commonly track the costs to tokenize a security, which can be useful in applying methods of valuation under the cost approach.

This post explores the valuation challenges posed by digital assets, with a focus on fair value measurement, marketability discounts, legal structure, and technological risk. It proposes a structured approach to valuation that integrates traditional financial principles with emerging practices in blockchain analytics and decentralized finance.

Through practical examples and a methodological analysis of tokens that are traded on major digital exchanges such as Coinbase and Binance, it aims to equip financial analysts with the tools necessary to navigate the valuations within this evolving asset class with rigor and clarity, with a focus on the market approach.

Depending on trading volume and market characteristics, these tokens would typically qualify as Level 1 or Level 2 assets under the ASC 820/IFRS 13 fair value standards. We conclude with some notes on Simple Agreement for Future Tokens (SAFTs) as a type of contract (Level 3) that is becoming increasingly common in token-based fund raising as an alternative to actual token issuance for early-stage projects.

Step 1: Identify the Token You’re Valuing

As a first step in the valuation process, it is critical to identify the key technical features of the digital asset being valued. Some common types include:

• Cryptocurrencies (ex: Bitcoin, Ethereum, Solana). Cryptocurrencies typically have a dedicated blockchain and are used for peer-to-peer payments.
• Stablecoins (ex: Theter’s USDT and USDC). Stablecoins are used as a step in the conversion of other digital tokens into a fiat currency such as the US dollar or the Euro. They typically trade at a price close to par (1 USDT = 1 USD), but, similarly to certain money market funds, this parity should not be taken for granted, as it can break in periods of market disruption and may affect the proceeds at exit in an underlying digital token sale.
• Utility tokens (for example, Ethereum’s Ether, Solana’s Chainlink). Utility tokens operate above an underlying primary blockchain. They may be used to pay for services provided by the issuing platform (Service Tokens), exercise voting rights in the operations of the underlying business (Government Tokens), or for a variety of other functions. They could also be purchased as an investment to gain exposure to the underlying platform. While a token does not provide equity participation rights, the traded price of a utility token will typically benefit from progress made in the development of the underlying platform’s business plan and, more generally, from improvements in the underlying platform’s operations.

An understanding of the token’s technical features is critical to assess the token’s risk profile, identify comparable tokens, and identify the drivers of supply and demand which ultimately determine the token’s market performance. Tokens that operate on the same blockchain may belong to different layers.

Native Layer-1 tokens are the primary cryptocurrencies of independent blockchain networks, such as Bitcoin (BTC) and Ethereum (ETH). Layer 2 tokens strive to extend the capabilities of the underlying base layer network. Tokens on the same blockchain may also differ based on their use of standards. For instance, Binance USD (BUSD) operates using the ERC-20 standard on Ethereum, while NTFs typically use ERC-721.

Other important features to consider include the total supply of tokens and number of tokens in circulation, the characteristics of the initial coin offering, and the token’s regulatory background. The token’s whitepaper will provide relevant information on the project behind the token’s issuance and will help identify its key technical features.

Step 2: Determine the Principal Market

According to ASC 820 and IFRS 13, the fair value of an asset should be measured based on pricing information obtained from its “principal market,” defined as “the market with the greatest volume and level of activity for an asset or liability.” It is common for digital tokens to trade on multiple exchanges.

For example, based on information from Coinmarketcap (Exhibit 1), the top 10 exchanges for the trading of Ethereum include Binance, Bybit, Coinbase Exchange, Upbit, OKX, Bitget, Gate,  MexC and KuCoin. The reported prices vary according to the exchange, in some cases significantly (UpBit at $4,148.91 versus the other exchanges in the range $3,977.42 to $3,994.30).

In a valuation report, it is important to identify the reference exchange, and/or whether a composite price such as the “Close” price reported by Coinmarketcap is used instead.

Exhibit 1: Top 10 Exchanges for Trading Ethereum.

Source: Coinmarketcap, October 29, 2025 10:11 a.m. EST.

Step 3: Identify the Exit Price

Under ASC 820 and IFRS 13, fair value is meant to reflect an “exit price,” namely the price that would be received to sell an asset in an orderly transaction between market participants on the measurement date. Cryptocurrencies trade continuously and typically exhibit high intra-day volatility. Which point in time should we use to identify our exit price for the token on the valuation date?

In answering this question, analysts may want to refer to any guidance provided in contractual agreements. For instance, they may turn to the valuation policy for an investment company, a limited partnership agreement, or a fund private placement memorandum. In the absence of a valuation policy and contractual guidance, analysts may select a valuation time, often aligned with the token holder’s location. After choosing that point in time (e.g., 12 a.m. EST, 5 p.m. EST, 12 a.m. PST), analysts should use it consistently.

Alternatively, analysts may consider using the average daily price (simple or volume-weighted) from a specified source, the “close price” per Coinmarketcap or another data provider, the price from a particular exchange, or the average of daily high and low as reported by Coinmarketcap or another pricing sources.

As analysts utilize prices from crypto markets in their analysis, it is important to keep in mind that the decentralized nature of the market for digital tokens makes them especially prone to the risk of pricing manipulation. In October 2024, the US Securities and Exchange Commission brought fraud charges against ZM Quant Investment Ltd. and certain other “market makers” for engaging in schemes to manipulate the markets for various crypto assets being offered and sold as securities to retail investors. The schemes were allegedly intended to induce investor victims to purchase the crypto assets by creating the false appearance of an active trading market for them.[1]

In the cryptoasset markets, it is often the developer of a digital token platform (the “offeror”) who pays the market maker a monthly fee. A token offeror may wish to have one or more market makers create artificial volume to meet minimum requirements for having their crypto assets on their trading platforms A market maker may accomplish this by using one or more accounts it directly or indirectly controls to trade against its own quotation.

As noted in the SEC complaint against ZM Quant Investment Ltd., “here there is no change in beneficial ownership of the asset traded, but the trade creates the appearance of a market-driven transaction.” The practice of “wash trading” could give the cryptoasset greater prominence and potentially attract more natural buyers and sellers, which would tend to push up the price of the underlying token.

The risk of pricing manipulation is especially high at or around the time of a token Initial Coin Offering. The founders and initial investors in a token platform may hold a large portion of the assets at inception and may have a strong incentive in generating public interest in the tokens so they can discharge their position at favorable prices. From a fair value perspective, it is important to stay vigilant about the quality of the information provided by digital token exchanges and whether such information is indeed coming from an “orderly market” led by “market participants” in arms’ length transactions, especially around ICO events.

Step 4: Identify Applicable Discounts

An investor in digital tokens may hold a position that is large in relation to the volume traded on the principal exchange and be concerned about the impact that selling such a large “block” of tokens may have on token prices. Should a discount to the market price be applied? Under the fair value standard of ASC 820 and IFRS 13, blockage discounts are not permitted. There is, however, the possibility of applying a discount for lack of marketability when the tokens themselves carry restriction features that would transfer from the seller to the buyer upon the token sale.

Step 5: Quantify a Discount for Lack of Marketability

It is common for digital tokens that were acquired directly from the issuing platform to carry lock-up provisions that restrict token sales over certain periods of time (“Vested Tokens”). In such cases, the fair value of the token would typically include a discount for lack of marketability (DLOM). Table 1 provides an example of a vesting schedule, in which the underlying tokens are unlocked over a 12- month period.

Table 1: Example of Digital Token Vesting Schedule.

In Table 2, the DLOM for the vesting schedule above is estimated using the Ghaidarov Average Strike Protective Put Option Model.

Table 2: Ghaidarov Average Strike Put Option Model.

Given a volatility of 140% and an average time to maturity of 0.5 years, the vesting schedule in Table 2 results in a DLOM of 23.4% over the market price of the token at the valuation date. Rather than calculating an average term and using the average term in the put option pricing model, analysts could also estimate a separate DLOM for each tranche in dollar value, and sum up the results.

The results in Table 3 are heavily reliant on the volatility input. The estimate of volatility is one of the most challenging aspects of the valuation of digital tokens with vesting provisions. To the extent the subject token is thinly traded or may have traded in a market that is not orderly, it might be appropriate to consider the volatility of a selection of guideline comparable tokens or a reference index with appropriate layer and standard characteristics.

For tokens that are close to their Initial Coin Offering, the term used to calculate the volatility of the selected comparables may have to be adjusted to consider the period from a date that is equidistant from the date of their respective ICO as the reference date of the subject token, rather than using the same calendar term.  

Table 3 shows the volatility of a selection of Layer 1 tokens from their ICO date to a reference date that is 127 days from the ICO date (the “Reference Volatility”) and the related one-month to three-year forward volatility. The volatility declines significantly for all tokens as we move past the six-month forward period.

Accordingly, it would typically not be appropriate to compare, say, the 127-day historical volatility for a token starting from its ICO date with the volatility over the most recent 127 days for an established token like Bitcoin or Ethereum. A comparative analysis that considers the 127 days volatility of Bitcoin or Ethereum starting from their own respective ICO date may be more meaningful under such circumstances.

Table 3: Historical Volatility Comparison.

It is important to choose a suitable put option pricing model in a digital token DLOM analysis. The Black-Scholes framework has certain conceptual limitations in the estimate of a DLOM for tokens that have volatility in the high double digits or sometimes even in the triple digits. At extreme volatility levels (e.g., >150%), the Black-Scholes model tends to produce skewed and unstable outputs, which tend to diverge from observable market behavior.

The Ghaidarov model that we have used in Exhibit 3 extends the option-pricing framework by introducing forward-starting and sequential-input options, allowing for partial exercise throughout the illiquidity period, and dynamic strike-price adjustments that reflect evolving liquidity. It also constrains discount growth at extreme volatility, preventing unrealistic results.

While it has its own limitation, the Ghaidarov model has the advantage of being specifically designed for multi-period illiquidity scenarios, such as staggered vesting, and may provide a more robust alternative to the Black-Scholes model in high-volatility, non-hedgeable environments.

Valuing SAFTs (Simple Agreements for Future Tokens)

Simple Agreements for Future Tokens (SAFTs) are investment agreements offered by crypto developers whereby investors provide capital to the developers in exchange for digital tokens at a future date. Like Simple Agreements for Future Equity (SAFEs) in the venture capital world, SAFTs typically have a discount provision and may have a valuation cap. SAFTs may vary in terms of what happens if the trigger event does not occur. Possible scenarios include: 1) give capital back to investors ahead of other stakeholders in the enterprise and 2) render the SAFT worthless.

A scenario analysis is often a suitable way to approach the valuation of SAFTs. Once the conditions for performance are defined at the inception of the deal (the “Calibration Date”), the value is adjusted at subsequent dates based on the assessment of deal performance relative to initial expectations.

Conclusion: Applying Fair Value in a Fragmented Market

In today’s market, the value of digital assets must be captured in investment and business valuation. Analysts must expand the range of data sources and techniques they use in valuation and develop methodologies that are suitable to the digital asset being valued for more reliable valuation results. Analysts should maintain professional skepticism and remain alert to potential market manipulation for tokens in lightly regulated, private token markets. The results of our valuation analysis are heavily reliant on the quality of information that we consider and on our understanding of the technical features of the tokens and the markets in which they trade.

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References

[1] U.S. Securities and Exchange Commission v. ZM Quant Investment Ltd, Baijun Ou, a.k.a. Eric Ou, and Ruiqi Lau, a.k.a. Ricky Liu, filed October 9, 2024 in U.S. District Court, District of Massachusetts.

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