Crypto Assets multiplier

Author | Rodney J. Garratt, Maarten R.C. van Oordt Source | Journal of Corporate Finance Compiled | Ji Ru Yu

In October 2025, the Journal of Corporate Finance published an article titled “The Crypto Multiplier”. The article focuses on the phenomenon of high volatility in the cryptocurrency market and introduces the concept of the “crypto multiplier” to measure the amplification effect of net inflows or outflows of investor funds on the equilibrium market value of cryptocurrencies. The article posits that the size of this multiplier depends on the circulation ratio of cryptocurrencies as a means of payment: the lower the proportion of tokens used for payments, the stronger the multiplier effect, and the greater the price volatility. The authors validate the positive correlation between the proportion of speculative holdings and future exchange rate fluctuations through theoretical derivation and empirical analysis, using blockchain data. The research findings provide significant insights for market participants in assessing the liquidity risks of holding large amounts of cryptocurrency, particularly in the contexts of collateral financing and startup financing, where one must be cautious of the significant gap between market value and liquidation value. The core sections of the study have been translated by the Institute of Fintech Research at Renmin University of China.

Introduction

Since the inception of cryptocurrencies, their price volatility has far exceeded that of traditional fiat currencies, becoming a focal point of attention both in academia and industry. As shown in Figure 1, the daily return standard deviation of mainstream cryptocurrencies such as Bitcoin and Ethereum often exceeds 10%, while the volatility of major fiat currencies mostly remains below 1%. This extreme volatility not only affects investors' risk expectations but also prompts regulatory bodies such as the Basel Committee on Banking Supervision to impose the highest risk weights on cryptocurrencies held by banks. Traditional research attributes volatility to the lack of elasticity in cryptocurrency supply or the convenience of converting between different currencies, but these perspectives fail to reveal the intrinsic connection between holder motivations and market structure. Based on this, this paper introduces the core concept of “crypto multiplier”, aiming to characterize the systemic impact of investor capital flows on the market value of cryptocurrencies from an equilibrium perspective.

The theoretical construction of the crypto multiplier is based on a key observation: although cryptocurrencies can serve as payment tools, they are rarely used as units of account. In actual transactions, the prices of goods are usually quoted in fiat currency, while the amount of cryptocurrency paid is adjusted in real-time according to the exchange rate. This characteristic makes the exchange rate formation mechanism of cryptocurrencies different from that of traditional currencies, and provides a fertile ground for the emergence of the multiplier effect. The size of the crypto multiplier reflects the market's sensitivity to investment demand: when the vast majority of tokens are hoarded rather than used for payments, even small amounts of capital flow can trigger drastic changes in market value. The article further validates this theory using blockchain data and points out that among the current mainstream cryptocurrencies, over 75% of Bitcoin and 60% of Ethereum have not been used for payments in the past six months, suggesting that their multipliers could be extremely high.

Related literature

The study of the economics of cryptocurrencies has experienced explosive growth in recent years, covering multiple dimensions such as price formation, platform token financing, and consensus mechanism design. In terms of price theory, scholars like Athey et al. (2016) and Schilling & Uhlig (2019) have pointed out that the lack of a unit of account function in cryptocurrencies is a key premise for understanding their price behavior. This hypothesis also serves as the basis for deriving the crypto multiplier in this article. Additionally, Bolt & Van Oordt (2020) established a theoretical link between cryptocurrency exchange rates and payment demand by extending the Fisher equation, providing important references for this article.

In terms of token economics and financing models, Cong et al. (2021) and Garratt & Van Oordt (2022) studied how companies finance themselves through the issuance of tokens. On the other hand, economic incentives related to consensus mechanisms and blockchain security are also research hotspots. Scholars such as Budish (2018), Prat & Walter (2021) discussed the stability issues of blockchain from the perspectives of computational power competition and node behavior. It is worth noting that recent asset pricing literature has introduced concepts similar to the crypto multiplier, such as the “demand multiplier” for stock and bond portfolios proposed by Gabaix & Koijen (2021), whose estimated values range from 3 to 8. However, the uniqueness of the cryptocurrency multiplier lies in its direct connection to payment functions: investment holdings crowd out the supply of tokens for payment purposes, thereby amplifying the price's response to capital flows. In the most simplified version of our model, the cryptocurrency multiplier is theoretically equal to the inverse of the proportion of payment tokens.

Theoretical Derivation of the Crypto Multiplier

1 Characteristics of Cryptocurrencies as Non-Valuation Units

The derivation of the crypto multiplier begins with a basic fact: cryptocurrencies are rarely used as a unit of account in reality. The prices of goods and services are typically marked in fiat currencies such as the dollar, and consumers convert them into the corresponding amount of tokens based on the real-time exchange rate when making payments. For example, a car priced at $60,000 can be paid with 2 bitcoins when the bitcoin exchange rate is $30,000; if the exchange rate drops to $20,000, then 3 bitcoins would be required for payment. This price flexibility stems from modern communication technology that allows merchants to adjust the number of tokens for payment in real-time, or rely on third-party payment service providers to complete the conversion and settlement. Therefore, cryptocurrencies primarily serve as a medium of payment rather than a measure of value, and this characteristic has profound implications for the exchange rate formation mechanism.

2 Establishment of Exchange Rate Equation

To characterize the exchange rate determination mechanism of cryptocurrencies, the author introduces the classic quantity equation: MV=PT. Here, P represents the average number of tokens per transaction, T is the number of transactions, M is the total supply of tokens, and V represents the velocity of money. In the context of cryptocurrencies, tokens can be divided into an active part used for payments and a non-active part serving as a store of value. Let Z represent the number of tokens not used for payments, which has a velocity of zero; the remaining M−Z tokens have an average velocity of V*. Substituting into the quantity equation gives:

Telecommunication technology allows merchants to update the number of coins customers need to pay in near real-time at checkout. When accepting cryptocurrency payments, merchants can calculate the number of cryptocurrency units S that the customer needs to pay by dividing the purchase amount (P dollars) by the latest exchange rate of that cryptocurrency (P dollars/unit), which is:

Define Ts=Ps*T as the total payment amount in US dollars, ultimately deriving the exchange rate equation for cryptocurrencies:

This equation indicates that the exchange rate is directly proportional to the payment demand and inversely proportional to the number of tokens available for payment. Its validity relies solely on two fundamental assumptions: the number of tokens used for payment is determined by the fiat currency price and the exchange rate (Assumption 1), and cryptocurrencies are at least used as a payment tool in certain transactions (Assumption 2).

Derivation and Meaning of the 3 Multiplier Formula

Based on the exchange rate equation, the author further introduces two assumptions: the total supply of tokens M is inelastic to market conditions (Assumption 3, applicable to cryptocurrencies with preset issuance rules like Bitcoin), and changes in speculative holdings Z will not permanently affect the payment demand measured in dollars Ts/V* (Assumption 4). Under these conditions, the expression for the crypto multiplier can be derived:

The multiplier measures the magnifying effect of a net inflow of 1 dollar on the balanced market value of cryptocurrencies. Its intuitive economic implication is that after an investor purchases 1 dollar worth of tokens, the number of tokens available for payment decreases. To maintain the dollar value of payment demand, the exchange rate must rise to increase the total value of the remaining payment tokens by 1 dollar. Since payment tokens account for only M−Z of the total, to increase their total value by 1 dollar, the total value of all M tokens must increase by M/(M−Z) dollars. The minimum value of the multiplier is 1 (when all tokens are used for payment), and it rises sharply as the proportion of payment tokens decreases. For example, if only 5% of the tokens are used for payment, the multiplier can reach as high as 20. Blockchain data shows that the payment ratio of mainstream cryptocurrencies is extremely low, suggesting that their multiplier may far exceed that of traditional assets.

4 Multipliers and Endogenous Payment Demand

The basic multiplier model assumes that the demand for payment is not affected by speculative behavior. However, in reality, speculative activities may have a lasting impact on the use of payments through network effects or changes in transaction costs. To capture this complexity, the author expands the model to allow the demand for payments to respond endogenously.

The extended multiplier has an additional component, the sign of which depends on the relationship between speculative holdings and payment demand in equilibrium. If the two are positively correlated (e.g., speculative activities raise the visibility of cryptocurrencies), the multiplier will be further amplified; if negatively correlated (e.g., speculation drives up transaction costs), the multiplier may weaken. However, theoretical analysis shows that for the multiplier to be below 1, strict conditions must be met, namely that a $1 influx of funds leads to a reduction in payment demand exceeding the Z/M ratio. Given the extremely high Z/M ratio of mainstream cryptocurrencies, the likelihood of the multiplier being below 1 is low.

5 Applicable Scenarios of Theoretical Models

The effectiveness of the cryptocurrency multiplier model depends on the extent to which the aforementioned three core assumptions hold, and thus its applicability is limited. Firstly, the model is only applicable to cryptocurrencies that are used as a means of payment in at least some transactions (satisfying assumption 2). Native tokens like Bitcoin and Ethereum naturally meet this condition as they are used for paying transaction fees or executing smart contracts on the blockchain. Secondly, the model assumes that the total supply of tokens is inelastic (assumption 3), thus it is not applicable to stablecoins, as their supply adjusts with demand to maintain exchange rate stability. Lastly, if there are significant frictions in the payment process (such as exchange rate premiums or transaction fees), assumption 1 may be violated. The author points out that if frictions are modeled as a fixed proportion of costs, the multiplier expression remains unchanged, but if fund flows lead to continuously changing costs, the actual multiplier may diverge from the theoretical value.

Empirical Analysis of Speculative Holding and Volatility

1. Data and Methods

To verify the real-world relevance of the cryptocurrency multiplier, the author selected 24 types of blockchain-native tokens, collecting their quarterly data from 2014 to 2023, covering information such as price, blockchain transactions, and address balances. The explained variable is the annualized standard deviation of future 180-day returns, and the core explanatory variables are three proxy variables for speculative holding ratios, namely: the share of tokens held by addresses exceeding 0.1% of total supply; the share of tokens held by the top 100 addresses; and the principal component variable constructed based on the above variables and the “share of addresses exceeding $1 million in balance” and “the number of small addresses.”

The control variables include on-chain transaction frequency, market capitalization, average transaction amount, and Google search index. All explanatory variables are lagged by one period to mitigate endogeneity issues.

2. Main Findings

The regression results show that all speculative proxy variables are significantly positively correlated with future volatility. Taking the principal component variable as an example, its coefficient in the fixed effects model is 0.683, which means that an increase in this variable from the 10th percentile to the 90th percentile will lead to an increase of about 3 percentage points in future 180-day volatility. This result remains robust even after changing the volatility measurement (such as mean absolute deviation, value at risk), adjusting the forecast horizon (90 days or 26 weeks), and converting the proxy variables into multiplicative forms.

In the controlled variables, the Google search index is positively correlated with volatility, echoing the literature findings that investor attention drives price fluctuations; market capitalization is negatively correlated with volatility, indicating that larger cryptocurrencies have lower volatility.

3. Impact on Valuation of Large Positions

The core policy implication of the cryptocurrency multiplier is to warn market participants to prudently assess the liquidity risks of holding large amounts of cryptocurrency. Theory suggests that the liquidation of large speculative positions can have a significant impact on prices unless there are other speculators to take them on. This risk is evidenced by real cases: in 2014, Ripple co-founder McCaleb announced plans to sell his 9% holding of XRP tokens, and despite the supply increase being only 10%, the XRP exchange rate plummeted by over 40% after the announcement. Ripple ultimately extended its sale plan through legal agreements for more than seven years to alleviate market pressure.

Similarly, celebrity endorsements or regulatory developments (such as the U.S. SEC approving a Bitcoin spot ETF) may trigger a massive influx of funds, driving up market value. However, if the cryptocurrency lacks substantial payment demand, its high multiplier characteristics will lead to prices being overly sensitive to capital flows, thereby amplifying volatility. Investors need to be acutely aware of the potential significant gap between market value and liquidation value when accepting large amounts of cryptocurrency as collateral or financing consideration.

Conclusion and Insights

This article systematically explains the structural causes of the high volatility of cryptocurrencies by constructing a theoretical framework of the multiplier effect. The size of the multiplier is directly related to the proportion of a token's payment usage: the weaker the payment function, the higher the multiplier, and the more intense the price response to capital flows. Empirical evidence further confirms that the proportion of speculative holdings has predictive power for future exchange rate volatility.

The research suggests that the volatility of the cryptocurrency market is not accidental, but rather a result of its nature as an investment asset rather than a payment tool. Unless the mainstream use of cryptocurrencies shifts from speculation to payment, high volatility is likely to persist. Investors need to be cautious of the liquidity risks associated with high-multiplicity cryptocurrencies and avoid simply equating market value with liquidation value. For regulators, understanding the multiplicity mechanism can help in formulating more prudent risk management standards. Future research could further explore the dynamic changes of multiplicity in different market cycles and policy environments, as well as the potential weakening effects of payment technology innovations on the multiplicity effect.