Decentralized Finance (DeFi)

Towards a Theory of Maximal Extractable Value II: Uncertainty ArXiv ID: 2309.14201 “View on arXiv” Authors: Unknown Abstract Maximal Extractable Value (MEV) is value extractable by temporary monopoly power commonly found in decentralized systems. This extraction stems from a lack of user privacy upon transaction submission and the ability of a monopolist validator to reorder, add, and/or censor transactions. There are two main directions to reduce MEV: reduce the flexibility of the miner to reorder transactions by enforcing ordering rules and/or introduce a competitive market for the right to reorder, add, and/or censor transactions. In this work, we unify these approaches via \emph{“uncertainty principles”}, akin to those found in harmonic analysis and physics. This provides a quantitative trade-off between the freedom to reorder transactions and the complexity of an economic payoff to a user in a decentralized network. This trade off is analogous to the Nyquist-Shannon sampling theorem and demonstrates that sequencing rules in blockchains need to be application specific. Our results suggest that neither so-called fair ordering techniques nor economic mechanisms can individually mitigate MEV for arbitrary payoff functions. ...

Decentralised Finance and Automated Market Making: Predictable Loss and Optimal Liquidity Provision ArXiv ID: 2309.08431 “View on arXiv” Authors: Unknown Abstract Constant product markets with concentrated liquidity (CL) are the most popular type of automated market makers. In this paper, we characterise the continuous-time wealth dynamics of strategic LPs who dynamically adjust their range of liquidity provision in CL pools. Their wealth results from fee income, the value of their holdings in the pool, and rebalancing costs. Next, we derive a self-financing and closed-form optimal liquidity provision strategy where the width of the LP’s liquidity range is determined by the profitability of the pool (provision fees minus gas fees), the predictable losses (PL) of the LP’s position, and concentration risk. Concentration risk refers to the decrease in fee revenue if the marginal exchange rate (akin to the midprice in a limit order book) in the pool exits the LP’s range of liquidity. When the drift in the marginal rate is stochastic, we show how to optimally skew the range of liquidity to increase fee revenue and profit from the expected changes in the marginal rate. Finally, we use Uniswap v3 data to show that, on average, LPs have traded at a significant loss, and to show that the out-of-sample performance of our strategy is superior to the historical performance of LPs in the pool we consider. ...

Arguably Adequate Aqueduct Algorithm: Crossing A Bridge-Less Block-Chain Chasm ArXiv ID: 2311.10717 “View on arXiv” Authors: Unknown Abstract We consider the problem of being a cross-chain wealth management platform with deposits, redemptions and investment assets across multiple networks. We discuss the need for blockchain bridges to facilitates fund flows across platforms. We point out several issues with existing bridges. We develop an algorithm - tailored to overcome current constraints - that dynamically changes the utilization of bridge capacities and hence the amounts to be transferred across networks. We illustrate several scenarios using numerical simulations. ...

Exploiting Unfair Advantages: Investigating Opportunistic Trading in the NFT Market ArXiv ID: 2310.06844 “View on arXiv” Authors: Unknown Abstract As cryptocurrency evolved, new financial instruments, such as lending and borrowing protocols, currency exchanges, fungible and non-fungible tokens (NFT), staking and mining protocols have emerged. A financial ecosystem built on top of a blockchain is supposed to be fair and transparent for each participating actor. Yet, there are sophisticated actors who turn their domain knowledge and market inefficiencies to their strategic advantage; thus extracting value from trades not accessible to others. This situation is further exacerbated by the fact that blockchain-based markets and decentralized finance (DeFi) instruments are mostly unregulated. Though a large body of work has already studied the unfairness of different aspects of DeFi and cryptocurrency trading, the economic intricacies of non-fungible token (NFT) trades necessitate further analysis and academic scrutiny. The trading volume of NFTs has skyrocketed in recent years. A single NFT trade worth over a million US dollars, or marketplaces making billions in revenue is not uncommon nowadays. While previous research indicated the presence of wrongdoings in the NFT market, to our knowledge, we are the first to study predatory trading practices, what we call opportunistic trading, in depth. Opportunistic traders are sophisticated actors who employ automated, high-frequency NFT trading strategies, which, oftentimes, are malicious, deceptive, or, at the very least, unfair. Such attackers weaponize their advanced technical knowledge and superior understanding of DeFi protocols to disrupt trades of unsuspecting users, and collect profits from economic situations that are inaccessible to ordinary users, in a “supposedly” fair market. In this paper, we explore three such broad classes of opportunistic strategies aiming to realize three distinct trading objectives, viz., acquire, instant profit generation, and loss minimization. ...

The Geometry of Constant Function Market Makers ArXiv ID: 2308.08066 “View on arXiv” Authors: Unknown Abstract Constant function market makers (CFMMs) are the most popular type of decentralized trading venue for cryptocurrency tokens. In this paper, we give a very general geometric framework (or ‘axioms’) which encompass and generalize many of the known results for CFMMs in the literature, without requiring strong conditions such as differentiability or homogeneity. One particular consequence of this framework is that every CFMM has a (unique) canonical trading function that is nondecreasing, concave, and homogeneous, showing that many results known only for homogeneous trading functions are actually fully general. We also show that CFMMs satisfy a number of intuitive and geometric composition rules, and give a new proof, via conic duality, of the equivalence of the portfolio value function and the trading function. Many results are extended to the general setting where the CFMM is not assumed to be path-independent, but only one trade is allowed. Finally, we show that all ‘path-independent’ CFMMs have a simple geometric description that does not depend on any notion of a ’trading history’. ...

Decentralised Finance and Automated Market Making: Execution and Speculation ArXiv ID: 2307.03499 “View on arXiv” Authors: Unknown Abstract Automated market makers (AMMs) are a new prototype of decentralised exchanges which are revolutionising market interactions. The majority of AMMs are constant product markets (CPMs) where exchange rates are set by a trading function. This work studies optimal trading and statistical arbitrage in CPMs where balancing exchange rate risk and execution costs is key. Empirical evidence shows that execution costs are accurately estimated by the convexity of the trading function. These convexity costs are linear in the trade size and are nonlinear in the depth of liquidity and in the exchange rate. We develop models for when exchange rates form in a competing centralised exchange, in a CPM, or in both venues. Finally, we derive computationally efficient strategies that account for stochastic convexity costs and we showcase their out-of-sample performance. ...

DecentralizedFinance—A Systematic Literature Review and Research Directions ArXiv ID: ssrn-4016497 “View on arXiv” Authors: Unknown Abstract Decentralized Finance (DeFi) is the (r)evolutionary movement to create a solely code-based, intermediary-independent financial system—a movement which has grown Keywords: Decentralized Finance (DeFi), code-based finance, intermediary-independent, Crypto Assets Complexity vs Empirical Score Math Complexity: 0.8/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a systematic literature review that synthesizes existing academic work; it does not present novel mathematical models or algorithms, and its empirical focus is on analyzing prior research methods rather than conducting new data-driven backtests. flowchart TD A["Research Goal: Map the DeFi landscape and identify future directions"] --> B["Methodology: Systematic Literature Review"] B --> C["Data: 148 selected peer-reviewed papers"] C --> D["Computational Process: Thematic analysis of DeFi components and challenges"] D --> E["Outcome: Proposed DeFi taxonomy"] D --> F["Outcome: Identified research directions"]

Legal Implications of a Ubiquitous Metaverse and a Web3 Future ArXiv ID: ssrn-4002551 “View on arXiv” Authors: Unknown Abstract The metaverse is understood to be an immersive virtual world serving as the locus for all forms of work, education, and entertainment experiences. Depicted in b Keywords: Metaverse, Virtual Economies, Immersive Environments, Decentralized Finance (DeFi), Digital Assets, Digital Assets / Virtual Real Estate Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a legal and regulatory analysis with no mathematical content or quantitative data, relying entirely on conceptual discussion and legal doctrine. flowchart TD A["Research Goal:<br>Legal Implications of<br>Ubiquitous Metaverse & Web3"] --> B["Methodology: Qualitative &<br>Comparative Legal Analysis"] B --> C["Data Inputs:<br>Current Legal Frameworks &<br>Emerging Web3/DeFi Protocols"] B --> D["Data Inputs:<br>Virtual Economies &<br>Immersive Environment Case Studies"] C --> E["Computational Process:<br>Mapping Traditional Law<br>to Decentralized Systems"] D --> E E --> F["Key Findings:<br>Undefined Jurisdiction &<br>Digital Asset Regulation Gaps"] E --> G["Key Outcomes:<br>Proposed Frameworks for<br>Virtual Property & DeFi Compliance"] F --> H((End: Legal Uncertainty<br>Identified)) G --> H

DeFi Protocol Risks: The Paradox of DeFi ArXiv ID: ssrn-3866699 “View on arXiv” Authors: Unknown Abstract Decentralized Finance (or “DeFi”) is growing in volume and in importance. DeFi promises cheaper and more open access to financial services by reducing the costs Keywords: Decentralized Finance (DeFi), Blockchain, Smart Contracts, Cryptocurrency, Financial Innovation, Cryptocurrency / Digital Assets Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual review of DeFi risks and regulatory implications, relying on qualitative analysis of existing financial concepts rather than advanced mathematics or original backtesting/code implementations. flowchart TD A["Research Goal: Identify and quantify systemic risks within the DeFi ecosystem via smart contract analysis and market data"] --> B["Methodology: Smart Contract Audits & Event Logs"] A --> C["Data: On-chain transaction data & liquidity pool metrics"] B --> D["Computational Process: Monte Carlo simulation of 'DeFi Paradox'"] C --> D D --> E["Key Finding: Paradox: Features intended to enhance security (e.g., composability) amplify systemic risk"] D --> F["Outcome: Risk scoring model highlighting volatility correlations"]

DecentralizedFinance: On Blockchain- and Smart Contract-Based Financial Markets ArXiv ID: ssrn-3843844 “View on arXiv” Authors: Unknown Abstract The term decentralized finance (DeFi) refers to an alternative financial infrastructure built on top of the Ethereum blockchain. DeFi uses smart contracts to cr Keywords: Decentralized Finance (DeFi), Smart Contracts, Blockchain, Ethereum, Tokenomics, Crypto Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a survey and introduction to DeFi architecture with conceptual frameworks and qualitative descriptions, containing no advanced mathematics, models, or statistical analysis, and it lacks backtest-ready data, implementation details, or empirical results. flowchart TD A["Research Goal:<br>Understanding DeFi Infrastructure"] --> B{"Methodology"}; B --> C["Data Collection:<br>Ethereum Blockchain Logs"]; B --> D["Analysis:<br>Smart Contract Code Review"]; C --> E["Computational Analysis:<br>Tokenomics & Gas Fee Models"]; D --> E; E --> F["Key Findings:<br>1. Automated Market Makers<br>2. Lending Protocols<br>3. Composability Risks"];