The StableSims project set out to determine optimal parameters for the new auction mechanism, Liquidations 2.0, used by MakerDAO, a protocol built on Ethereum offering a decentralized, collateralized stablecoin called Dai. We developed an agent-based simulation that emulates both the Maker protocol smart contract logic, and how profit-motivated agents ("keepers") will act in the real world when faced with decisions such as liquidating "vaults" (collateralized debt positions) and bidding on collateral auctions. This research focuses on the incentive structure introduced in Liquidations 2.0, which implements both a constant fee (tip) and a fee proportional to vault size (chip) paid to keepers that liquidate vaults or restart stale collateral auctions. We sought to minimize the amount paid in incentives while maximizing the speed with which undercollateralized vaults were liquidated. Our findings indicate that it is more cost-effective to increase the constant fee, as opposed to the proportional fee, in order to decrease the time it takes for keepers to liquidate vaults.
Optimal simple rules for the monetary policy of the first stochastically dominant crypto-currency are derived in a Dynamic Stochastic General Equilibrium (DSGE) model, in order to provide optimal responses to changes in inflation, output, and other sources of uncertainty. The optimal monetary policy stochastically dominates all the previous crypto-currencies, thus the efficient portfolio is to go long on the stochastically dominant crypto-currency: a strategy-proof arbitrage featuring a higher Omega ratio with higher expected returns, inducing an investment-efficient Nash equilibrium over the crypto-market. Zero-knowledge proofs of the monetary policy are committed on the blockchain: an implementation is provided.
We propose and study a new class of polynomial voting rules for a general decentralized decision/consensus system, and more specifically for the proof-of-stake protocol. The main idea, inspired by the Penrose square-root law and the more recent quadratic voting rule, is to differentiate a voter’s voting power and the voter’s share (fraction of the total in the system). We show that, whereas voter shares form a martingale process that converges to a Dirichlet distribution, their voting powers follow a supermartingale process that decays to zero over time. This prevents any voter from controlling the voting process and, thus, enhances security. For both limiting results, we also provide explicit rates of convergence. When the initial total volume of votes (or stakes) is large, we show a phase transition in share stability (or the lack thereof), corresponding to the voter’s initial share relative to the total. We also study the scenario in which trading (of votes/stakes) among the voters is allowed and quantify the level of risk sensitivity (or risk aversion) in three categories, corresponding to the voter’s utility being a supermartingale, a submartingale, and a martingale. For each category, we identify the voter’s best strategy in terms of participation and trading. Funding: W. Tang gratefully acknowledges financial support through the National Science Foundation [Grants DMS-2113779 and DMS-2206038] and through a start-up grant at Columbia University. D. D. Yao’s work is part of a Columbia–City University/Hong Kong collaborative project that is supported by InnoHK Initiative, the Government of Hong Kong Special Administrative Region, and the Laboratory for AI-Powered Financial Technologies.
Brett Hemenway, Bin Gu, Gerry Tsoukalas, Niuniu Zhang
Non-Fungible Tokens (NFTs) are transforming how content creators, such as artists, price and sell their work. A key feature of NFTs is the inclusion of royalties, which grant creators a share of all future resale proceeds. Although widely used, critics argue that sophisticated speculators, who dominate NFT markets, simply price in royalties upfront, neutralizing their impact. We show this intuition holds only under perfect, frictionless markets. Under more realistic market conditions, royalties enable creators to capitalize on the presence of speculators in at least three ways: They can enable risk sharing (under risk aversion), mitigate information asymmetry (when speculators are better informed), and unlock price discrimination benefits (in multi-unit settings). Moreover, in all three cases, royalties meaningfully expand trade, implying increased transaction volume for platforms. These results offer testable predictions that can guide both empirical research and platform design.
The Sandbox metaverse LAND non-fungible token (NFT) prices increased by than 300 times (in USD) between December 2019 and January 2022, but when measured in its native utility token (SAND), the increase is only 3 times. Depending on how prices are denominated, investment returns and effective transaction prices vary. We analyze more than 71,000 transactions and find that users are willing to pay 3-4% more when transactions are settled in SAND, and 30% less when settled in wETH (a smart contract version of ETH) when compared to ETH, so unit of account matters. Our results contribute to the discussions of blockchain-based, virtual economy management and the digitalization of money (Brunnermeier et al., 2019).
Kanis Saengchote, Tālis J. Putniņš, Krislert Samphantharak
Decentralized Finance (DeFi) is built on a fundamentally different paradigm: rather than having to trust individuals and institutions, participants in DeFi potentially only have to trust computer code that is enforced by a decentralized network of computers. We examine a natural experiment that exogenously stress tests this alternative paradigm by revealing the identities of individuals associated with a DeFi protocol, including a convicted criminal. We find that, in practice, DeFi does not (yet) fully remove the need for trust in individuals. Our findings suggest that that because smart contracts are incomplete, they are subject to run risk (Allen and Gale, 2004) and personal character and trust of individuals are still relevant in this alternative financial system.
Abstract We develop a continuous‐time control approach to optimal trading in a Proof‐of‐Stake (PoS) blockchain, formulated as a consumption‐investment problem that aims to strike the optimal balance between a participant's (or agent's) utility from holding/trading stakes and utility from consumption. We present solutions via dynamic programming and the Hamilton–Jacobi–Bellman (HJB) equations. When the utility functions are linear or convex, we derive close‐form solutions and show that the bang‐bang strategy is optimal (i.e., always buy or sell at full capacity). Furthermore, we bring out the explicit connection between the rate of return in trading/holding stakes and the participant's risk‐adjusted valuation of the stakes. In particular, we show when a participant is risk‐neutral or risk‐seeking, corresponding to the risk‐adjusted valuation being a martingale or a sub‐martingale, the optimal strategy must be to either buy all the time, sell all the time, or first buy then sell, and with both buying and selling executed at full capacity. We also propose a risk‐control version of the consumption‐investment problem; and for a special case, the “stake‐parity” problem, we show a mean‐reverting strategy is optimal.
This paper is concerned with the stability of shares in a cryptocurrency where the new coins are issued according to the Proof of Stake protocol. We identify large, medium and small investors under various rewarding schemes, and show that the limiting behaviors of these investors are different -- for large investors their shares are stable, while for medium to small investors their shares may be volatile or even shrink to zero. For instance, with a geometric reward there is chaotic centralization, where all the shares will eventually concentrate on one investor in a random manner. This leads to the phase transition phenomenon, and the thresholds for stability are characterized. In response to the increasing activities in blockchain networks, we also propose and analyze a dynamical population model for the PoS protocol, which allows the number of investors to grow over the time. Numerical experiments are provided to corroborate our theory.
Online marketplaces are the main engines of legal and illegal e-commerce, yet their empirical properties are poorly understood due to the absence of large-scale data. We analyze two comprehensive datasets containing 245M transactions (16B USD) that took place on online marketplaces between 2010 and 2021, covering 28 dark web marketplaces, i.e., unregulated markets whose main currency is Bitcoin, and 144 product markets of one popular regulated e-commerce platform. We show that transactions in online marketplaces exhibit strikingly similar patterns despite significant differences in language, lifetimes, products, regulation, and technology. Specifically, we find remarkable regularities in the distributions of transaction amounts, number of transactions, inter-event times and time between first and last transactions. We show that buyer behavior is affected by the memory of past interactions and use this insight to propose a model of network formation reproducing our main empirical observations. Our findings have implications for understanding market power on online marketplaces as well as inter-marketplace competition, and provide empirical foundation for theoretical economic models of online marketplaces.
The properties of money commonly referenced in the economics literature were originally identified by Jevons (1876) and Menger (1892) in the late 1800s and were intended to describe physical currencies, such as commodity money, metallic coins, and paper bills. In the digital era, many non-physical currencies have either entered circulation or are under development, including demand deposits, cryptocurrencies, stablecoins, central bank digital currencies (CBDCs), in-game currencies, and quantum money. These forms of money have novel properties that have not been studied extensively within the economics literature, but may be important determinants of the monetary equilibrium that emerges in the forthcoming era of heightened currency competition. This paper makes the first exhaustive attempt to identify and define the properties of all physical and digital forms of money. It reviews both the economics and computer science literatures and categorizes properties within an expanded version of the original functions-and-properties framework of money that includes societal and regulatory objectives.
Vincent Schlatt, Johannes Sedlmeir, Simon Feulner, Nils Urbach
Know your customer (KYC) processes place a great burden on banks, because they are costly, inefficient, and inconvenient for customers. While blockchain technology is often mentioned as a potential solution, it is not clear how to use the technology's advantages without violating data protection regulations and customer privacy. We demonstrate how blockchain-based self-sovereign identity (SSI) can solve the challenges of KYC. We follow a rigorous design science research approach to create a framework that utilizes SSI in the KYC process, deriving nascent design principles that theorize on blockchain's role for SSI.
We mine the leaked history of trades on Mt. Gox, the dominant Bitcoin exchange from 2011 to early 2014, to detect the triangular arbitrage activity conducted within the platform. The availability of user identifiers per trade allows us to focus on the historical record of 440 investors, detected as arbitrageurs, and consequently to describe their trading behavior. We begin by showing that a considerable difference appears between arbitrageurs when indicators of their expertise are taken into account. In particular, we distinguish between those who conducted arbitrage in a single or in multiple markets: using this element as a proxy for trade ability, we find that arbitrage actions performed by expert users are on average non-profitable when transaction costs are accounted for, while skilled investors conduct arbitrage at a positive and statistically significant premium. Next, we show that specific trading strategies, such as splitting orders or conducting arbitrage non aggressively, are further indicators of expertise that increase the profitability of arbitrage. Most importantly, we exploit within-user (across hours and markets) variation and document that expert users make profits on arbitrage by reacting quickly to plausible exogenous variations on the official exchange rates. We present further evidence that such differences are chiefly due to a better ability of the latter in incorporating information, both on the transactions costs and on the exchange rates volatility, eventually resulting in a better timing choice at small time scale intervals. Our results support the hypothesis that arbitrageurs are few and sophisticated users.
We review different classes of cryptocurrencies with emphasis on their economic properties. Pure-asset coins such as Bitcoin, Ethereum and Ripple are characterized by not being a liability of any economic agent and most resemble commodities such as gold. Central bank digital currencies, at the other end of the economic spectrum, are liabilities of a Central Bank and most resemble cash. In between, there exist a range of so-called stable coins, with varying degrees of economic complexity. We use balance sheet operations to highlight the properties of each class of cryptocurrency and their potential uses. In addition, we propose the basic structure for a macroeconomic model incorporating all the different types of cryptocurrencies under consideration.
JUBILEE is a securely computed mechanism for debt relief and forgiveness in a frictionless manner without involving trusted third parties, leading to more harmonious debt settlements by incentivising the parties to truthfully reveal their private information. JUBILEE improves over all previous methods: - individually rational, incentive-compatible, truthful/strategy-proof, ex-post efficient, optimal mechanism for debt relief and forgiveness with private information - by the novel introduction of secure computation techniques to debt relief, the "blessing of the debtor" is hereby granted for the first time: debt settlements with higher expected profits and a higher probability of success than without using secure computation A simple and practical implementation is included for "The Secure Spreadsheet". Another implementation is realised using Raziel smart contracts on a blockchain with Pravuil consensus.
Blockchains are traditionally blind to the real world. This implies reliance on third parties called oracles when extrinsic data are needed for smart contracts. Oracle implementation, however, is still controversial and debated due to the reintroduction of trust and a single point of failure. The blindness to the real world also makes blockchains unable to communicate with each other, preventing any form of interoperability. This limitation prevents, for example, liquidity held in Bitcoin from flowing into DeFi applications. An early approach to the interoperability issue is constituted by “wrapped tokens”, representing blockchain native tokens issued on a non-native blockchain. Similar to how oracles reintroduce trust and a single point of failure, the issuance of wrapped tokens involves third parties whose characteristics need to be considered when evaluating the advantages of “crossing-chains”. This paper provides an overview of the available wrapped tokens and the main issuing procedures. Benefits, limitations, and implications for trust are listed and discussed.
In this paper, we construct a decentralized clearing mechanism which endogenously and automatically provides a claims resolution procedure. This mechanism can be used to clear a network of obligations through blockchain. In particular, we investigate default contagion in a network of smart contracts cleared through blockchain. In so doing, we provide an algorithm which constructs the blockchain so as to guarantee the payments can be verified and the miners earn a fee. We, additionally, consider the special case in which the blocks have unbounded capacity to provide a simple equilibrium clearing condition for the terminal net worths; existence and uniqueness are proven for this system. Finally, we consider the optimal bidding strategies for each firm in the network so that all firms are utility maximizers with respect to their terminal wealths. We first look for a mixed Nash equilibrium bidding strategies, and then also consider Pareto optimal bidding strategies. The implications of these strategies, and more broadly blockchain, on systemic risk are considered.
We present a systematic approach to detect fake transactions on cryptocurrency exchanges by exploiting robust statistical and behavioral regularities associated with authentic trading. Our sample consists of 29 centralized exchanges, among which the regulated ones feature transaction patterns consistently observed in financial markets and nature. In contrast, unregulated exchanges display abnormal first-significant-digit distributions, size rounding, and transaction tail distributions, indicating widespread manipulation unlikely driven by specific trading strategy or exchange heterogeneity. We then quantify the wash trading on each unregulated exchange, which averaged over 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and user base), market conditions, and regulation. Overall, our study cautions against potential market manipulations on centralized crypto exchanges with concentrated power and limited disclosure requirements, and highlights the importance of FinTech regulation.
Background: During the last years, there has been a lot of discussion and estimations on the energy consumption of Bitcoin miners. However, most of the studies are focused on estimating energy consumption, not in exploring the factors that determine it. Goal: To explore the factors that determine maximum energy consumption of Bitcoin miners. In particular, analyze the limits of energy consumption, and to which extent variations of the factors could produce its reduction. Method: Estimate the overall profit of all Bitcoin miners during a certain period of time, and the costs (including energy) that they face during that time, because of the mining activity. The underlying assumptions is that miners will only consume energy to mine Bitcoin if they have the expectation of profit, and at the same time they are competitive with respect of each other. Therefore, they will operate as a group in the point where profits balance expenditures. Results: We show a basic equation that determines energy consumption based on some specific factors: minting, transaction fees, exchange rate, energy price, and amortization cost. We also define the Amortization Factor, which can be computed for mining devices based on their cost and energy consumption, helps to understand how the cost of equipment influences total energy consumption. Conclusions: The factors driving energy consumption are identified, and from them, some ways in which Bitcoin energy consumption could be reduced are discussed. Some of these ways do not reduce the most important properties of Bitcoin, such as the chances of control of the aggregated hashpower, or the fundamentals of the proof of work mechanism. In general, the methods presented can help to predict energy consumption in different scenarios, based on factors that can be calculated from available data, or assumed in scenarios.
Blockchains are still perceived chiefly as a new technology. But each blockchain is also a community and a social experiment, built around social consensus. Here I discuss three examples showing how collective intelligence can help, threat or capitalize on blockchain-based ecosystems. They concern the immutability of smart contracts, code transparency and new forms of property. The examples show that more research, new norms and, eventually, laws are needed to manage the interaction between collective behaviour and the blockchain technology. Insights from researchers in collective intelligence can help society rise up to the challenge.
This discussion applies quantitative finance methods and economic arguments to cryptocurrencies in general and bitcoin in particular -- as there are about $10,000$ cryptocurrencies, we focus (unless otherwise specified) on the most discussed crypto of those that claim to hew to the original protocol (Nakamoto 2009) and the one with, by far, the largest market capitalization. In its current version, in spite of the hype, bitcoin failed to satisfy the notion of "currency without government" (it proved to not even be a currency at all), can be neither a short nor long term store of value (its expected value is no higher than $0$), cannot operate as a reliable inflation hedge, and, worst of all, does not constitute, not even remotely, a safe haven for one's investments, a shield against government tyranny, or a tail protection vehicle for catastrophic episodes. Furthermore, bitcoin promoters appear to conflate the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, with the speculative variations in the price of a zero-sum maximally fragile asset with massive negative externalities. Going through monetary history, we show how a true numeraire must be one of minimum variance with respect to an arbitrary basket of goods and services, how gold and silver lost their inflation hedge status during the Hunt brothers squeeze in the late 1970s and what would be required from a true inflation hedged store of value.
Pravuil is a robust, secure, and scalable consensus protocol for a permissionless blockchain suitable for deployment in an adversarial environment such as the Internet. Pravuil circumvents previous shortcomings of other blockchains: - Bitcoin's limited adoption problem: as transaction demand grows, payment confirmation times grow much lower than other PoW blockchains - higher transaction security at a lower cost - more decentralisation than other permissionless blockchains - impossibility of full decentralisation and the blockchain scalability trilemma: decentralisation, scalability, and security can be achieved simultaneously - Sybil-resistance for free implementing the social optimum - Pravuil goes beyond the economic limits of Bitcoin or other PoW/PoS blockchains, leading to a more valuable and stable crypto-currency
Centralized monetary policy, leading to persistent inflation, is often inconsistent, untrustworthy, and unpredictable. Algorithmic stablecoins enabled by blockchain technology are promising in solving this problem. Algorithmic stablecoins utilize a monetary policy that is entirely rule-based. However, there is little understanding of how to optimize the rule. We propose a model that trade-off the price for supply stability. We further study the comparative statics by varying several design features. Finally, we discuss the empirical implications for designing stablecoins by the private sector and Central Bank Digital Currency (CBDC) by the public sector.
The Internet of Value (IOV) with its distributed ledger technology (DLT) underpinning has created new forms of lending markets. As an integral part of the decentralised finance (DeFi) ecosystem, lending protocols are gaining tremendous traction, holding an aggregate liquidity supply of over $40 billion at the time of writing. In this paper, we enumerate the challenges of traditional money markets led by banks and lending platforms, and present advantageous characteristics of DeFi lending protocols that might help resolve deep-rooted issues in the conventional lending environment. With the examples of Maker, Compound and Aave, we describe in detail the mechanism of DeFi lending protocols. We discuss the persisting reliance of DeFi lending on the traditional financial system, and conclude with the outlook of the lending market in the IOV era.
The initial purpose of the study is to search whether the market exhibits herd behaviour or not by examining the crypto-asset market in the context of behavioural finance. And the second purpose of the study is to measure whether the financial information stimulates the herd behaviour or not. Within this frame, the announcements of the Federal Open Market Committee (FOMC), Governing Council of European Central Bank (ECB) and Policy Board of Bank of Japan (BOJ) for interest change, and S&P 500, Nikkei 225, FTSE 100 and GOLD SPOT indices data were used. In the study, the analyses were made over 100 cryptocurrencies with the highest trading volume by the use of the 2014:5 - 2019:12 period. For the analysis, the Markov Switching approach, as well as loads of empiric models developed by Chang et al. (2000), were used. According to the results obtained, the presence of herd behaviour in the crypto-asset market was determined in the relevant period. But it was found that interest rate announcements and stock exchange performances had no effect on herd behaviour.