A Classification Framework for Stablecoin Designs

Moin, Amani; Sirer, Emin Gün; Sekniqi, Kevin

doi:10.48550/arxiv.1910.10098

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Furthermore, Pernice et al [43] present a comprehensive taxonomy of cryptocurrency stabilization through combining their findings and studying classical monetary policy. Moin et al [41] systematically explore existing stablecoins via decomposing their design into various component elements and introduce their strengths, drawbacks, and future directions. Lastly, Kolodner et al [8] [11] and intermediate representation [46] of smart contracts.…”

Section: Related Workmentioning

confidence: 99%

Towards A First Step to Understand Flash Loan and Its Applications in DeFi Ecosystem

Wang

Lin

et al. 2021

Proceedings of the Ninth International Workshop on Security in Blockchain and Cloud Computing

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Flash Loan, as an emerging service in the decentralized finance ecosystem, allows traders to request a non-collateral loan as long as the debt is repaid within the transaction. While providing convenience, it brings considerable challenges that Flash Loan allows speculative traders to leverage vulnerability of deployed protocols with vast capital and few risks and responsibilities. Most recently, attackers have gained over $15𝑀 profits from Eminence Finance [53] via exploiting Flash Loans to repeatedly swap tokens (i.e., EMN and DAI).To be aware of foxy actions, we should understand what is the behavior running with the Flash Loan by traders. In this work, we propose ThunderStorm, a 3-phase transaction-based analysis framework, to systematically study Flash Loan on the Ethereum. Specifically, ThunderStorm first identifies Flash Loan transactions by applying observed transaction patterns, and then understands the semantics of the transactions based on primitive behaviors, and finally recovers the intentions of transactions according to advanced behaviors. To perform the evaluation, we apply ThunderStorm to existing transactions and investigate 11 well-known platforms. As the result,22, 244 transactions are determined to launch Flash Loan(s), and those Flash Loan transactions are further classified into 7 categories. Lastly, the measurement of financial behaviors based on Flash Loans is present to help further understand and explore the speculative usage of Flash Loan. The evaluation results demonstrate the capability of the proposed system.

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Section: Related Workmentioning

confidence: 99%

Towards A First Step to Understand Flash Loan and Its Applications in DeFi Ecosystem

Wang

Lin

et al. 2021

Proceedings of the Ninth International Workshop on Security in Blockchain and Cloud Computing

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“…Among many possible tasks, contracts are used to create and sell digital assets on the blockchain. The assets can be categorized into two categories: 1 Tokens whose prices can fluctuate; ERC20 or ERC721 [60], 2 Stablecoins whose prices are pegged to an asset such as USD [44] (these are also ERC20 tokens). Token networks are particularly valuable because each token naturally represents a network layer with the same nodes (addresses of investors) appearing in the networks (layers) of multiple tokens.…”

Section: Ethereum Token Networkmentioning

confidence: 99%

Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks

Ofori-Boateng¹,

Segovia-Domínguez²,

Kantarcıoğlu³

et al. 2021

Preprint

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Motivated by the recent surge of criminal activities with cross-cryptocurrency trades, we introduce a new topological perspective to structural anomaly detection in dynamic multilayer networks. We postulate that anomalies in the underlying blockchain transaction graph that are composed of multiple layers are likely to be also manifested in anomalous patterns of the network shape properties. As such, we invoke the machinery of clique persistent homology on graphs to systematically and efficiently track evolution of the network shape and, as a result, to detect changes in the underlying network topology and geometry. We develop a new persistence summary for multilayer networks, called stacked persistence diagram, and prove its stability under input data perturbations. We validate our new topological anomaly detection framework in application to dynamic multilayer networks from the Ethereum Blockchain and the Ripple Credit Network, and show that our stacked PD approach substantially outperforms the state-of-art techniques, yielding up to 40% gains in precision.

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“…Stablecoins are cryptoassets which possess a price stabilization mechanism to maintain some target peg. Here we briefly outline two of the most widely used stabilization mechanisms [20]:…”

Section: Stablecoinsmentioning

confidence: 99%

DeFi Protocols for Loanable Funds: Interest Rates, Liquidity and Market Efficiency

Gudgeon,

Werner,

Perez

et al. 2020

Preprint

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We coin the term Protocols for Loanable Funds (PLFs) to refer to protocols which establish distributed ledger-based markets for loanable funds. PLFs are emerging as one of the main applications within Decentralized Finance (DeFi), and use smart contract code to facilitate the intermediation of loanable funds. In doing so, these protocols allow agents to borrow and save programmatically. Within these protocols, interest rate mechanisms seek to equilibrate the supply and demand for funds. In this paper, we review the methodologies used to set interest rates on three prominent DeFi PLFs, namely Compound, Aave and dYdX. We provide an empirical examination of how these interest rate rules have behaved since their inception in response to differing degrees of liquidity. We then investigate the market efficiency and inter-connectedness between multiple protocols, examining first whether Uncovered Interest Parity holds within a particular protocol and second whether the interest rates for a particular token market show dependence across protocols, developing a Vector Error Correction Model for the dynamics.

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A Classification Framework for Stablecoin Designs

Cited by 3 publications

References 3 publications

Towards A First Step to Understand Flash Loan and Its Applications in DeFi Ecosystem

Towards A First Step to Understand Flash Loan and Its Applications in DeFi Ecosystem

Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks

DeFi Protocols for Loanable Funds: Interest Rates, Liquidity and Market Efficiency

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