Traditional financial systems have limitations like centralized control, slow transactions, and lack of transparency. Emerging decentralized technologies offer an alternative model by giving users more direct control over their digital assets and identities. This chapter explores the potential of balancing centralized and decentralized elements in the evolution of digital currencies. It provides frameworks and real-world examples to examine how decentralized innovations can enhance speed, reduce costs, automate governance, and cut out intermediaries. The authors analyze the history of finance and argue that thoughtful integration of human oversight with decentralized automation can strengthen these systems. The goal is to inform policymakers, central bank experts, blockchain developers, and cryptography researchers on this key trend shaping the future of digital money.
We consider Geometric Mean Market Makers (G3Ms) – a special type of Decentralized Exchange – with two types of traders: liquidity takers and arbitrageurs. Liquidity takers use G3Ms to swap tokens and to speculate, while arbitrageurs exploit arbitrage opportunities arising from misalignments between the G3M's price and the external market price. We show that in continuous time, a G3M charging proportional transaction fees offers exchange rates that are of finite variation, and that the opportunity cost of providing liquidity relative to rebalancing a self-financing constant-weights portfolio is, in fact, a non-negative gain. Moreover, we demonstrate that Impermanent Loss can be super-hedged in continuous time by a model-free rebalancing strategy. We conclude with a numerical analysis discussing the approximative nature of our continuous-time results for trading in discrete time.
Abstract This study investigated the extent of currency competition within the cryptocurrency market through the Hayek’s concept of the denationalization of money. Hayek’s original analysis primarily centered on competition revolving around the medium of the exchange function. This study posited that cryptocurrencies compete across diverse monetary functions, particularly concerning their roles as speculative stores of value and exchange media. This assertion provided insight into the distinction between Hayek’s envisaged private currencies and the cryptocurrency paradigm. Utilizing an extensive dataset encompassing 101 cryptocurrencies spanning from 2016 to 2022, an empirical exploration was conducted to scrutinize the progression and intensity of competition within the broader cryptocurrency market and its submarkets. These findings reveal a robust competition among unpegged cryptocurrencies, predominantly contending for speculative investment purposes. Similarly, there is pronounced competition among stablecoins as stable stores of value. In contrast, competition is much less pronounced concerning the medium of the exchange function, potentially entailing network effects and the emergence of monopolistic tendencies within this specific submarket.
Staking has emerged as a crucial concept following Ethereum’s transition to Proof-of-Stake consensus. The introduction of Liquid Staking Derivatives (LSDs) has effectively addressed the illiquidity issue associated with solo staking, gaining significant market attention. This paper analyzes the LSD market dynamics from the perspectives of both liquidity takers (LTs) and liquidity providers (LPs). We first quantify the price discrepancy between the LSD primary and secondary markets. Then we investigate and empirically measure how LTs can leverage such discrepancy to exploit arbitrage opportunities, unveiling the potential barriers to LSD arbitrages. In addition, we evaluate the financial profit and losses experienced by LPs who supply LSDs for liquidity provision. Our results show that 66% of LSD liquidity positions generate returns lower than those from simply holding the corresponding LSDs.
We study the temporal evolution of the holding-time distribution of bitcoins and find that the average distribution of holding-time is a heavy-tailed power law extending from one day to over at least $200$ weeks with an exponent approximately equal to $0.9$, indicating very long memory effects. We also report significant sample-to-sample variations of the distribution of holding times, which can be best characterized as multiscaling, with power-law exponents varying between $0.3$ and $2.5$ depending on bitcoin price regimes. We document significant differences between the distributions of book-to-market and of realized returns, showing that traders obtain far from optimal performance. We also report strong direct qualitative and quantitative evidence of the disposition effect in the Bitcoin Blockchain data. Defining age-dependent transaction flows as the fraction of bitcoins that are traded at a given time and that were born (last traded) at some specific earlier time, we document that the time-averaged transaction flow fraction has a power law dependence as a function of age, with an exponent close to $-1.5$, a value compatible with priority queuing theory. We document the existence of multifractality on the measure defined as the normalized number of bitcoins exchanged at a given time.
The Proof of Efficient Liquidity (PoEL) protocol, designed for specialised Proof of Stake (PoS) consensus-based blockchains that incorporate intrinsic DeFi applications, aims to support sustainable liquidity bootstrapping and network security. This concept seeks to efficiently utilise budgeted staking rewards to attract and sustain liquidity through a risk-structuring engine and incentive allocation strategy, both of which are designed to maximise capital efficiency. The proposed protocol serves the dual objective of: (i) capital creation by attracting risk capital efficiently and maximising its operational utility for intrinsic DeFi applications, thereby asserting sustainability; and (ii) enhancing the adopting blockchain network's economic security by augmenting their staking (PoS) mechanism with a harmonious layer seeking to attract a diversity of digital assets. Finally, the protocol's conceptual framework, as detailed in the appendix, is extended to encompass service fee credits. This extension capitalises on the network's auxiliary services to disperse incentives and attract liquidity, ensuring the network achieves and maintains the critical usage threshold essential for its sustained operational viability and progressive growth.
The prevalence of maximal extractable value (MEV) in the Ethereum ecosystem has led to a characterization of the latter as a dark forest. Studies of MEV have thus far largely been restricted to purely on-chain MEV, i.e., sandwich attacks, cyclic arbitrage, and liquidations. In this work, we shed light on the prevalence of non-atomic arbitrage on decentralized exchanges (DEXes) on the Ethereum blockchain. Importantly, non-atomic arbitrage exploits price differences between DEXes on the Ethereum blockchain as well as exchanges outside the Ethereum blockchain (i.e., centralized exchanges or DEXes on other blockchains). Thus, non-atomic arbitrage is a type of MEV that involves actions on and off the Ethereum blockchain. In our study of non-atomic arbitrage, we uncover that more than a fourth of the volume on Ethereum's biggest five DEXes from the merge until 31 October 2023 can likely be attributed to this type of MEV. We further highlight that only eleven searchers are responsible for more than 80% of the identified non-atomic arbitrage volume sitting at a staggering $132 billion and draw a connection between the centralization of the block construction market and non-atomic arbitrage. Finally, we discuss the security implications of these high-value transactions that account for more than 10% of Ethereum's total block value and outline possible mitigations.
Depositing and borrowing in a currency other than the local currency is a well- documented phenomenon. The associated exchange rate and monetary risks, pivotal during the Asian Financial Crisis of 1997, prompted an academic discourse. Financial dollarization literature explains why individuals and corporations domiciled in emerg- ing markets deposit and borrow in hard currencies, mainly in U.S. dollars. This thesis proposes a model for cryptocurrency lending and assesses model predictions with a data set containing more than one million Ethereum transactions. The computational modelling and statistical analysis show that the main theoretical explanations provided by financial dollarization literature may not be directly transferable to cryptocurrencies. The results suggest that the popularity of depositing and borrowing in cryptocurrencies must be largely motivated by other factors.
Charles Bertucci, Louis Bertucci, Mathis Gontier Delaunay, Olivier Guéant · 5 authors
ABSTRACT Contrasting sharply with traditional money, bond, and bond futures markets, where interest rates emerge organically from participant interactions, DeFi lending platforms employ rule‐based interest rates that are algorithmically set. Thus, the selection of an effective interest rate model (IRM) is paramount for the success of a lending protocol. This paper investigates the modeling of agents' behaviors on lending platforms and proposes a theoretical framework for formulating optimal IRMs. We show that, under perfect information, an optimal control model with a state constraint generates an optimal interest rate policy that has a shape similar to that of popular markets. Furthermore, we formally analyze interest rate policies based on PID controllers, which work efficiently based on fewer assumptions. Using public data of popular markets on the Ethereum blockchain, we analyze agents' behavior, build a realistic simulation environment, and highlight the main tradeoffs in the design of interest rates for decentralized lending platforms.
This paper considers introducing asymmetric privacy in the design of central bank digital currencies (CBDC) and digital currencies more generally to preserve the privacy of money spent while keeping the benefits of digital records for money received. It is shown that this feature would help minimize real distortions between consumers, firms, and financiers while enabling tax optimization and better access to external financing. Protecting the privacy of consumers is desirable from a welfare and efficiency standpoint as long as there exist noticeable privacy concerns. Implementing asymmetric privacy is technologically feasible, using, for instance, zero-knowledge proofs or other privacy tools. This paper has been accepted by Lin William Cong for the Virtual Special Issue on Digital Finance. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2024.06830 .
Concentrated liquidity (CL) provisioning is a way how to improve the capital efficiency of Automated Market Makers (AMM). Allowing liquidity providers to use leverage is a step towards even higher capital efficiency. A number of Decentralized Finance (DeFi) protocols implement this technique in conjunction with overcollateralized lending. However, the properties of leveraged CL positions have not been formalized and are poorly understood in practice. This article describes the principles of a leveraged CL provisioning protocol, formally models the notions of margin level, assets, and debt, and proves that within this model, leveraged LP positions possess several properties that make them safe to use.
Decentralized Finance (DeFi) has reshaped the possibilities of reserve banking in the form of the Collateralized Debt Position (CDP). Key to the safety of CDPs is the money supply architecture that enables issued debt to maintain its value. In traditional markets, and with respect to the United States Dollar system, interest rates are set by the Federal Reserve in an attempt to influence the effects of excessive inflation. DeFi enables a more transparent approach that typically relies on interest rates or other debt recovery mechanisms being directly informed by asset price. This research investigates contemporary DeFi money supply and debt management strategies and their limitations. Furthermore, this paper introduces a time-weighted approach to interest rate management that implements a Proportional-Integral-Derivative control system to constantly adapt to market activities and protect the value of issued currency, while addressing observed limitations.
Decentralized Finance (DeFi) money markets have seen explosive growth in recent years, with billions of dollars borrowed in various cryptocurrency assets. Key to the safety of money markets is the implementation of interest rates that determine the cost of borrowing, and govern counterparty exposure and return. In traditional markets, interest rates are set by risk managers, portfolio managers, the Federal Reserve, and a myriad of other sources depending on the market function. DeFi enables an algorithmic approach that typically relies on interest rates being directly dependent on market utilization. The benefit of algorithmic interest rate management is the system's continual response to market behaviors in real time, and thus an inherent ability to mitigate risks on behalf of protocols and users. These interest rate strategies target an optimal utilization based on the protocol's risk threshold, but historically lack the ability to compensate for excessive or diminished utilization over time. This research investigates contemporary DeFi interest rate management strategies and their limitations. Furthermore, this paper introduces a time-weighted approach to interest rate management that implements a Proportional-Integral-Derivative (PID) control system to constantly adapt to market utilization patterns, addressing observed limitations.
Viraj Nadkarni, Sanjeev R. Kulkarni, Pramod Viswanath
Automated Market Makers (AMMs) are essential in Decentralized Finance (DeFi) as they match liquidity supply with demand. They function through liquidity providers (LPs) who deposit assets into liquidity pools. However, the asset trading prices in these pools often trail behind those in more dynamic, centralized exchanges, leading to potential arbitrage losses for LPs. This issue is tackled by adapting market maker bonding curves to trader behavior, based on the classical market microstructure model of Glosten and Milgrom. Our approach ensures a zero-profit condition for the market maker's prices. We derive the differential equation that an optimal adaptive curve should follow to minimize arbitrage losses while remaining competitive. Solutions to this optimality equation are obtained for standard Gaussian and Lognormal price models using Kalman filtering. A key feature of our method is its ability to estimate the external market price without relying on price or loss oracles. We also provide an equivalent differential equation for the implied dynamics of canonical static bonding curves and establish conditions for their optimality. Our algorithms demonstrate robustness to changing market conditions and adversarial perturbations, and we offer an on-chain implementation using Uniswap v4 alongside off-chain AI co-processors.
The rapid growth of crypto assets raises important questions about their cross-border usage. To gain a better understanding of cross-border Bitcoin flows, we use raw data covering both on-chain (on the Bitcoin blockchain) and off-chain (outside the Bitcoin blockchain) transactions globally. We provide a detailed description of available methodologies and datasets, and discuss the crucial assumptions behind the quantification of cross-border flows. We then present novel stylized facts about Bitcoin cross-border flows and study their global and domestic drivers. Bitcoin cross-border flows respond differently than capital flows to traditional drivers of capital flows, and differences appear between on-chain and off-chain Bitcoin cross-border flows. Off-chain cross-border flows seem correlated with incentives to avoid capital flow restrictions.