Empirical and Theoretical Analysis of Liquid Staking Protocols

• URL: http://arxiv.org/abs/2401.16353v1
• Date: Mon, 29 Jan 2024 17:56:45 GMT
• Title: Empirical and Theoretical Analysis of Liquid Staking Protocols
• Authors: Krzysztof Gogol, Benjamin Kraner, Malte Schlosser, Tao Yan, Claudio Tessone, Burkhard Stiller,
• Abstract summary: Liquid staking protocols allow for earning staking rewards without the disadvantage of locking the capital at the validators. They are seen by some as a threat to the Proof-of-Stake blockchain security. This paper is the first work that classifies liquid staking implementations.
• Score: 3.2963988742807295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Liquid staking has become the largest category of decentralized finance protocols in terms of total value locked. However, few studies exist on its implementation designs or underlying risks. The liquid staking protocols allow for earning staking rewards without the disadvantage of locking the capital at the validators. Yet, they are seen by some as a threat to the Proof-of-Stake blockchain security. This paper is the first work that classifies liquid staking implementations. It analyzes the historical performance of major liquid staking tokens in comparison to the traditional staking for the largest Proof-of-Stake blockchains. Furthermore, the research investigates the impact of centralization, maximum extractable value and the migration of Ethereum from Proof-of-Work to Proof-of-Stake on the tokens' performance. Examining the tracking error of the liquid stacking providers to the staking rewards shows that they are persistent and cannot be explained by macro-variables of the currency, such as the variance or return.

Related papers

• Demystifying Private Transactions and Their Impact in PoW and PoS Ethereum [43.548299433042835]
  Private transactions, a specialized transaction type employed to evade public Peer-to-Peer (P2P) network broadcasting, remain largely unexplored. We analyze large-scale datasets comprising 14,810,392 private transactions within a 15.5-month Proof-of-Work (PoW) dataset and 30,062,232 private transactions within a 15.5-month Proof-of-Stake (PoS) dataset.
  arXiv  Detail & Related papers  (2025-03-30T16:45:18Z)
• Phantom Events: Demystifying the Issues of Log Forgery in Blockchain [31.570414211726888]
  We present the first in-depth security analysis of transaction log forgery in EVM-based blockchains. We propose a tool designed to detect event forgery vulnerabilities in smart contracts. We have successfully identified real-world instances for all five types of attacks across multiple decentralized applications.
  arXiv  Detail & Related papers  (2025-02-19T08:07:26Z)
• Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain [1.2180334969164464]
  This paper proposes to use graphical representation learning technology to find transaction patterns and distinguish malicious transaction contracts. Our research opens up more possibilities for trust and security in the ecosystem.
  arXiv  Detail & Related papers  (2024-12-16T21:56:01Z)
• A Game-Theoretic Approach to the Study of Blockchain's Robustness [0.0]
  This thesis investigates the robustness of blockchain protocols, specifically focusing on Proof-of-Stake. We define robustness in terms of two critical properties: Safety, which ensures that the blockchain will not have permanent conflicting blocks, and Liveness, which guarantees the continuous addition of new reliable blocks. Our research addresses the gap between traditional distributed systems approaches, which classify agents as either honest or Byzantine (i.e. malicious or faulty), and game-theoretic models that consider rational agents driven by incentives.
  arXiv  Detail & Related papers  (2024-11-28T14:29:14Z)
• Mitigating Challenges in Ethereum's Proof-of-Stake Consensus: Evaluating the Impact of EigenLayer and Lido [4.606106768645647]
  The transition from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism introduces a transformative approach to blockchain validation. This paper explores two innovative solutions: EigenLayer and Lido. We conclude with an evaluation of the combined potential of EigenLayer and Lido to foster a more resilient and inclusive ecosystem.
  arXiv  Detail & Related papers  (2024-10-30T19:58:46Z)
• Maximal Extractable Value in Decentralized Finance: Taxonomy, Detection, and Mitigation [0.534772724436823]
  Maximal Extractable Value (MEV) can be extracted from financial transactions on the blockchain. MEV causes financial losses and consensus instability, disrupting the security, efficiency, and decentralization goals of the DeFi ecosystem. This survey provides valuable insights for researchers, developers, stakeholders, and policymakers.
  arXiv  Detail & Related papers  (2024-10-22T12:11:41Z)
• Maximal Extractable Value Mitigation Approaches in Ethereum and Layer-2 Chains: A Comprehensive Survey [1.2453219864236247]
  MEV arises when miners or validators manipulate transaction ordering to extract additional value. This not only affects user experience by introducing unpredictability and potential financial losses but also threatens the underlying principles of decentralization and trust. This paper presents a comprehensive survey of MEV mitigation techniques as applied to both protocolss L1 and various L2 solutions.
  arXiv  Detail & Related papers  (2024-07-28T19:51:22Z)
• SoK: Liquid Staking Tokens (LSTs) [0.0]
  Liquid Staking Tokens (LSTs) function as tokenized representations of staked native assets while also accruing staking rewards. We establish a general framework describing the design choices and protocols underlying liquid staking. We compare the top LST implementations, examining their node operator selection, validator operations, and staking rewards distribution models. We empirically analyze LSTs' performance and find that the design choices and market events affect peg stability.
  arXiv  Detail & Related papers  (2024-03-31T11:08:12Z)
• Large Language Models for Blockchain Security: A Systematic Literature Review [31.972465064055168]
  Large Language Models (LLMs) have emerged as powerful tools across various domains within cyber security. This study aims to comprehensively analyze and understand existing research, and elucidate how LLMs contribute to enhancing the security of blockchain systems.
  arXiv  Detail & Related papers  (2024-03-21T10:39:44Z)
• Vulnerability Scanners for Ethereum Smart Contracts: A Large-Scale Study [44.25093111430751]
  In 2023 alone, such vulnerabilities led to substantial financial losses exceeding a billion of US dollars. Various tools have been developed to detect and mitigate vulnerabilities in smart contracts. This study investigates the gap between the effectiveness of existing security scanners and the vulnerabilities that still persist in practice.
  arXiv  Detail & Related papers  (2023-12-27T11:26:26Z)
• Demystifying DeFi MEV Activities in Flashbots Bundle [36.64508078443365]
  Decentralized Finance, mushrooming in permissionless blockchains, has attracted a recent surge in popularity. Due to the transparency of permissionless blockchains, opportunistic traders can compete to earn revenue by extracting Miner Extractable Value (MEV) The Flashbots bundle mechanism further aggravates the MEV competition because it empowers opportunistic traders with the capability of designing more sophisticated MEV extraction.
  arXiv  Detail & Related papers  (2023-12-02T09:46:39Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Onchain Sports Betting using UBET Automated Market Maker [45.410818354926406]
  Decentralized sports betting requires automated market makers (AMMs) for efficient liquidity provision. Existing AMMs like Uniswap lack alignment with fair odds, creating risks for liquidity providers. The paper introduces UBET AMM (UAMM), utilizing smart contracts and algorithms to price sports odds fairly.
  arXiv  Detail & Related papers  (2023-08-18T02:19:30Z)
• Uniswap Liquidity Provision: An Online Learning Approach [49.145538162253594]
  Decentralized Exchanges (DEXs) are new types of marketplaces leveraging technology. One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds. This introduces the problem of finding an optimal strategy for choosing price intervals. We formalize this problem as an online learning problem with non-stochastic rewards.
  arXiv  Detail & Related papers  (2023-02-01T17:21:40Z)
• ACon$^2$: Adaptive Conformal Consensus for Provable Blockchain Oracles [31.439376852065713]
  Power of smart contracts is enabled by interacting with off-chain data, which in turn opens the possibility to undermine the block state consistency. We propose an adaptive conformal consensus (ACon$2$) algorithm, which derives consensus from multiple oracle contracts. In particular, the proposed algorithm returns a consensus set, which quantifies the uncertainty of data and achieves a desired correctness guarantee.
  arXiv  Detail & Related papers  (2022-11-17T04:37:24Z)
• Decentralized Basic Income: Creating Wealth with On-Chain Staking and Fixed-Rate Protocols [0.0]
  We evaluate the mechanisms behind the decentralized finance protocols for generating stable, passive income. Currently, such savings interest rates can be as high as 20% annually, payable in traditional currency values such as US dollars. We will discuss potential pitfalls, assess how these protocols may behave in market cycles, as well as suggest areas for further research and development.
  arXiv  Detail & Related papers  (2021-07-29T20:21:55Z)
• CubeFlow: Money Laundering Detection with Coupled Tensors [39.26866956921283]
  Money laundering (ML) is the behavior to conceal the source of money achieved by illegitimate activities. Most existing methods detect dense blocks in a graph or a tensor, which do not consider the fact that money are frequently transferred through middle accounts. CubeFlow proposed in this paper is a scalable, flow-based approach to spot fraud from a mass of transactions.
  arXiv  Detail & Related papers  (2021-03-23T09:24:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.