Leverage Staking with Liquid Staking Derivatives (LSDs): Opportunities and Risks

• URL: http://arxiv.org/abs/2401.08610v3
• Date: Thu, 23 May 2024 13:50:59 GMT
• Title: Leverage Staking with Liquid Staking Derivatives (LSDs): Opportunities and Risks
• Authors: Xihan Xiong, Zhipeng Wang, Xi Chen, William Knottenbelt, Michael Huth,
• Abstract summary: In the Proof of Stake (PoS) ecosystem, users can stake ETH on Lido to receive stETH, a Liquid Staking Derivative (LSD) that represents staked ETH and accrues staking rewards. The composability of Lido, Aave, and Curve enables an emerging strategy known as leverage staking. This paper explores the opportunities and risks of leverage staking.
• Score: 5.150039023969438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the Proof of Stake (PoS) Ethereum ecosystem, users can stake ETH on Lido to receive stETH, a Liquid Staking Derivative (LSD) that represents staked ETH and accrues staking rewards. LSDs improve the liquidity of staked assets by facilitating their use in secondary markets, such as for collateralized borrowing on Aave or asset exchanges on Curve. The composability of Lido, Aave, and Curve enables an emerging strategy known as leverage staking, where users supply stETH as collateral on Aave to borrow ETH and then acquire more stETH. This can be done directly by initially staking ETH on Lido or indirectly by swapping ETH for stETH on Curve. While this iterative process enhances financial returns, it also introduces potential risks. This paper explores the opportunities and risks of leverage staking. We establish a formal framework for leverage staking with stETH and identify 442 such positions on Ethereum over 963 days. These positions represent a total volume of 537,123 ETH (877m USD). Our data reveal that the majority (81.7%) of leverage staking positions achieved an Annual Percentage Rate (APR) higher than that of conventional staking on Lido. Despite the high returns, we also recognize the risks of leverage staking. From the Terra crash incident, we understand that token devaluation can greatly impact the market. Therefore, we conduct stress tests under extreme conditions, particularly during stETH devaluations, to thoroughly evaluate the associated risks. Our simulations indicate that leverage staking can exacerbate the risk of cascading liquidations by introducing additional selling pressures from liquidation and deleveraging activities. Moreover, this strategy poses broader systemic risks as it undermines the stability of ordinary positions by intensifying their liquidations.

Related papers

• 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)
• Exploring the Market Dynamics of Liquid Staking Derivatives (LSDs) [3.386981473609616]
  Liquid staking Derivatives (LSDs) have effectively addressed the illiquidity issue associated with solo staking. This paper analyzes the LSD market dynamics from the perspectives of both liquidity takers (LTs) and liquidity providers (LPs)
  arXiv  Detail & Related papers  (2024-02-27T18:34:15Z)
• Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning [19.916721360624997]
  Decentralized exchanges (DEXs) are a cornerstone of decentralized finance (DeFi) This paper introduces a deep reinforcement learning (DRL) solution designed to adaptively adjust price ranges. Our approach also neutralizes price-change risks by hedging the liquidity position through a rebalancing portfolio.
  arXiv  Detail & Related papers  (2023-09-18T20:10:28Z)
• 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)
• Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization [63.93275508300137]
  We introduce a novel risk-aware Counterfactual Learning To Rank method with theoretical guarantees for safe deployment. Our experimental results demonstrate the efficacy of our proposed method, which is effective at avoiding initial periods of bad performance when little data is available.
  arXiv  Detail & Related papers  (2023-04-26T15:54:23Z)
• Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training [44.7991257631318]
  We propose a novel Split Variational Adrial Training (SVAT) method for risk-aware stock recommendation. By lowering the volatility of the stock recommendation model, SVAT effectively reduces investment risks and outperforms state-of-the-art baselines by more than 30% in terms of risk-adjusted profits.
  arXiv  Detail & Related papers  (2023-04-20T12:10:12Z)
• 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)
• A Game of NFTs: Characterizing NFT Wash Trading in the Ethereum Blockchain [53.8917088220974]
  The Non-Fungible Token (NFT) market experienced explosive growth in 2021, with a monthly trade volume reaching $6 billion in January 2022. Concerns have emerged about possible wash trading, a form of market manipulation in which one party repeatedly trades an NFT to inflate its volume artificially. We find that wash trading affects 5.66% of all NFT collections, with a total artificial volume of $3,406,110,774.
  arXiv  Detail & Related papers  (2022-12-02T15:03:35Z)
• Efficient Risk-Averse Reinforcement Learning [79.61412643761034]
  In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the returns. We prove that under certain conditions this inevitably leads to a local-optimum barrier, and propose a soft risk mechanism to bypass it. We demonstrate improved risk aversion in maze navigation, autonomous driving, and resource allocation benchmarks.
  arXiv  Detail & Related papers  (2022-05-10T19:40:52Z)
• Reinforcement learning for options on target volatility funds [0.0]
  We deal with the funding costs rising from hedging the risky securities underlying a target volatility strategy (TVS) We derive an analytical solution of the problem in the Black and Scholes (BS) scenario. Then we use Reinforcement Learning (RL) techniques to determine the fund composition leading to the most conservative price under the local volatility (LV) model.
  arXiv  Detail & Related papers  (2021-12-03T10:55:11Z)

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.