Aave原生美元稳定币GHO为何一直低于1美元?

Odaily星球日报Published on 2023-11-01Last updated on 2023-11-01

Abstract

除流动性管理不足外,目前持有GHO没有任何实际应用价值。

原文作者:@TokenBrice

原文编译:Peisen,BlockBeats

编者按:Aave 协议中的本地稳定币 GHO 自其问世以来一直低于 1 美元,目前约为 0.96 美元,流动性工程师 @TokenBrice在社交媒体平台发文对此现象做出解释,指出其流动性管理不足之处。但他同时表示,除了提供流动性,现在持有 GHO 没有任何实际应用价值。

BlockBeats 于 10 月 16 日报道,Aave 社区已通过「进一步提高稳定币 GHO 借贷利率至 3% 」的 ARFC 提案,目的是增强 GHO 锚定和提高收入。17 日,Aave 原生稳定币 GHO 发行量已突破 2500 万枚,截止目前达到 25, 152, 308 枚。

基于此,@TokenBrice将作为流动性委员会一员提出并实行一系列治理 DAO 的应对措施,并承诺在一个月内使 GHO 价格在此期间至少达到 0.985 美元。BlockBeats 将原文编译如下:

自诞生以来,GHO 一直在基准价以下交易(目前约为 0.96)。你可能想知道为什么,以及这种情况是否会改变。简单的回答是:GHO 的价格波动源于需求和供应的失衡,而这个问题即将得到解决。想了解详细的回答吗?请继续阅读,一场精彩的探索之旅即将展开。

首先,我们需要了解 GHO:它是 Aave 生态系统中的一种本地稳定币,可以使用 Aave 上任何作为抵押品支持的代币进行铸造。借款人需要支付由治理决定的利率,而 stkAAVE 持有者可以享受该利率的折扣。目前的利率范围为 2.13% 至 3.05% ,预计不久将会上升。

GHO 利率的问题

GHO 的当前状况与 Maker/DAI 早期相似:其利率受治理决定(无 PSM/GSM),这意味着 GHO 在不断变化的市场条件下无法自然调整。然而,自 GHO 诞生以来,市场条件已发生了巨大变化。关于这种设计,我已多次分享过我的观点,无需再次重复,可阅读我的博客获取详细信息。

如果你想知道当前利率和利率模型为什么有问题,答案很简单:它仍然远远低于其他替代方案,例如在 Maker 上借款 DAI 的利率,甚至是在 Aave 上借款其他稳定币的利率(如$DAI 或$USDC 的>5% )。这导致了一个套利循环,stkAAVE 持有者可以以约 2.13% 的年化收益率借款 GHO,将其兑换成 DAI,并享受 DAI 储蓄利率(目前为 5% )。

正如我上文提到的,治理机构非常清楚这个问题,并正在积极解决。因此,从这个角度来看,前景是积极的,这只是时间问题。现在让我们讨论另一个解释 GHO 当前状况的主要话题,它与利息息息相关。

GHO 的实用性

确保稳定币长期稳定的一个有效方法是赋予其实际应用价值。的确,对于大量借入或购买稳定币并将其保持为此类资产的持有者来说,这至关重要。例如,@LiquityProtocol 的$LUSD 稳定性池使 LUSD 持有者能够从清算过程中获得 ETH 收益,并从排放过程中获得 LQTY 收益。稳定池是一个特殊情况,因为它还充当协议上清算的主要储备。

其他 CDP 协议已经实施了不同的解决方案,但在为稳定币提供实用性方面实现了相同的目的,例如:在 MakerDAO 中的 Dai 储蓄利率:用户可以押注 DAI 以赚取更多的 DAI。

尽管这些解决方案各有特点,但从宏观角度来看,它们达到了相同的目的:确保有一群稳定币持有者乐于将它们保持为此类资产,即不将其兑换为另一个稳定或其他资产。

这正是 GHO 目前所缺少的:除了提供流动性,现在持有 GHO 没有任何实际应用价值。因此,大多数享受低于市场借款成本的借款者都乐于将他们的 GHO 卖给另一个稳定币,以便用这些货币赚取收益,例如 DAI。

现在,在这方面,治理已经意识到并正在考虑其选择,主要选择包括:

1. 在 Aave 上添加 wGHO 作为抵押品

2. 为 GHO 持有者提供押注选项:GHO 储蓄利率。

现在,舞台已经布置好了,我们可以谈谈我最喜欢的话题:流动性策略!

迄今为止 GHO 流动性管理的不足之处

流动性管理是一场艰巨的博弈:在支出、效率、政治和市场目标之间保持稳定的平衡。在未锚定稳定币(如 GHO)的情况下,弄清楚哪些是必要的和哪些是有害的变得更加具有挑战性。

实际上,尽管你可能每天都会看到我在抱怨流动性效率问题,但在制定重新锚定稳定币的策略时,这却是你最不需要关注的因素。这是因为重新锚定需要采取一些效率较低的措施,如支付超出范围的流动性作为价格支持。

尽管 GHO 流动性委员会由经验丰富的专业人士组成,但他们却为追求效率而陷入了一个陷阱,这对锚定来说是有害的。为了让你更好地理解,我们来举一些例子。

支持 GHO 的 Stableswap 池

对于稳定币而言,Stableswap 通常被认为是一个非常优秀的流动性集中选择:它将流动性集中在 1: 1 的价格附近,当所有涉及的稳定币实际上都处于锚定状态时,提供了巨大的流动性深度。

然而,如果某种资产要脱钩,情况就会变得更糟糕,因为 Stableswap 实际上可能会加剧问题。为什么会这样?我们可以观察一下 $GHO 主要 Stableswap 池的流动性分布情况。

Aave原生美元稳定币GHO为何一直低于1美元?

由于 GHO 的锚定价格偏低,池中 GHO 的浓度远高于目标的 50% 。假设 TVL 为 1000 万,以方便计算,如果所有的稳定币都处于完美锚定状态,那么池中将有约 500 万 的 GHO 和各占 133 万的其他三种币。

然而,实际上我们有约 750 万 的 GHO 和 250 万 的其他币。从买卖压力的角度来看,这意味着有 250 万 GHO 的盈余将在 GHO 价格上涨时被重新平衡到 GHO。本质上是一个分布在 GHO 价格 0.95 到 1.00 之间的 250 万 GHO 卖单墙。

对于锚定价格过低或过高的资产,stableswap 是有害的:当稳定币锚定价格过高时,我们观察到与 LUSD Curve 池几个月前的情况类似但相反的效应:池中配对资产变得更重。随着 LUSD 价格下降,它作为一个购买储备能够有阻止其有效下降的力量。

支持 0.95-1.003 GHO 的 Uniswap V3 池

现在,这个问题变得更加微妙,但概念仍然相似。当 GHO 的价格约为 0.96 时,Bunni 在 Uniswap v3 上启动了一个 GHO/USDC 池,其价格范围设定为 0.95-1.003 。与往常一样,要计算买卖压力,你必须计算各自代币的平衡。由于 Uniswap v3 的流动性分布是线性的,你需要找出我们在范围内的当前位置(就价格而言),以便在此基础上计算平衡。这其实很简单!

以下是价格区间的划分:所有低于 0.95 的 USDC -0.95 - 0.96 (我们当前处于 0.95-0.96 区间)-0.97-0.980.99 -1.00 ,最后到所有高于 1.00 区间的 GHO。

如你所见,在池启动时,价格处于其范围的左侧,这意味着超过 80% 的流动性集中在 GHO 上。这表明,如果池已经充分供应,那么从 0.96 到 1.00 的价格区间内会有一个相当大的卖单墙。幸运的是,池的价格超出了预设范围,Bunni 的流动性提供计划被迅速关闭,从而终止了所有 LP 激励措施,避免了大量的代币存入。

是时候改变流动性委员会的运作方式了

两周前成立的流动性委员会(LC)已经推出了许多措施,目前采用的是协同结构。为了使 LC 的行动更加精简、高效且减少错误,我们昨天决定改变决策过程。

为确保委员会在明确领导下运作,具有清晰愿景和为实现该愿景而制定的关键行动,我们投票决定让笔者本人主导精简领导层。

在当前困难时期,我们需要采取果断措施。鉴于 GHO 价格已降至 0.96 美元,我们有责任确保 LC 发挥最大效能。因此,我建议 LC 在我领导下运作一个月,并将提案提交投票。

在 4 票支持、 2 票未表态的情况下(LC 共有 7 名成员,但我弃权投票),流动性委员会通过了提案。

Aave原生美元稳定币GHO为何一直低于1美元?

因此,我将在截止到 11 月 30 日的一个月内,临时担任流动性委员会的领导者,旨在使 GHO 价格在此期间至少达到 0.985 。如果我们未能实现这一目标,我将承担全部责任,并向委员会提交辞呈。

为什么选择我?

现在,你可能会好奇:为什么是我?原因其实很简单:流动性委员会需要一种独特的专业知识,在整个 DeFi 生态系统中,掌握这种知识的人可能屈指可数。这种知识要求兼具稳定币和流动性专家的能力。更具体地说,它需要在非固定锚定价格的稳定币上管理定向流动性的经验,我喜欢把它称为「非锚定的锚定资产」。正如你所料,这样的人才并不多。幸运的是,我曾经管理过一个向上脱锚(LUSD,从创立到大约 6 月)和轻微向下脱锚(LUSD, 6 月至今)的稳定币的流动性。

我很高兴将这些经验运用到带领流动性委员会重新锚定 GHO 上。为了实现这一目标,我们将充分利用所有相关工具,重点关注最适合完成这项任务的工具 @mavprotocol。

Maverick:一种插接引擎?

确实,Maverick 的静态池让你能够在特定范围内实现均匀的流动性分布,这是其最有趣的应用之一,遗憾的是,Uniswap 无法实现这一点,因为它的分布是线性的。这意味着你可以创建静态池,并在给定价格范围内进行交易挂单,这正是我们将使用 GHO 来实现的!这已经开始在 GHO/USDC BP #57 0.956 至 0.966 之间展开,并被称为「GHO 锚定支持池 I」。

Aave原生美元稳定币GHO为何一直低于1美元?

请注意名称中的「I」字母:这意味着未来还会有更多类似的池。随着价格向左移动(即 GHO 价格上涨),激励将逐渐向更高的价格底线转移。在治理引导下,通过提高 GHO 的实用性和刺激还款等其他措施,GHO 将以稳定但坚定的步伐朝着锚定价格推进。

DeFi 是惊人的:加入吧,匿名者!

DeFi 独特之处在于,所有人都可以观看、验证和参与其中的现场活动。无论您对 GHO 的看法如何,强烈建议您在未来几周内密切关注这里的发展,因为正在进行的实验具有前所未有的规模。

更令人兴奋的是,您不仅可以作为观察者,还可以积极地为这些努力做出贡献。以下是一些可行的方式:

1. 如果您有闲置的 USDC,可以关注即将到来的类似于 BP#57 的 Maverick 矿池,并为其提供资金。在此过程中,您还将获得相当可观的回报。

2. 如果您已经在 Balancer GHO 池中提供流动性或用您的 veBAL/vlAURA 票据进行支持,那么请暂时停止这些操作。虽然 Aave/GHO 喜欢 Balancer,但如前所述,目前 stableswap 对 GHO 不利。尽量减少这个池子的规模将有助于减轻恢复锚定价格所需的压力。我们也关注/$crvUSD 池,尽管该池相对较小(30 万美元),所以问题并不紧迫。

3. 如果您相信流动性委员会在新的统一领导下有能力恢复 GHO 的锚定价格,那么可以考虑做多 GHO。目前 GHO 的价格为 0.96% ,如果我们真的能在年底前恢复锚定价格,那么您将在 2 个月内获得每 1 美元 4 美分的利润,这相当于 24% 的年化收益率。对于仅仅购买并持有一种稳定币来说,这已经是相当不错的回报了。

我感激流动性委员会其他成员的支持,并会竭尽所能实现这一目标。我所要求的报酬并不是为了这些努力,而仅仅是对我的 gas 费用(可能是几个 ETH)的补偿。

在达到目标后的一个月内,如果 Aave DAO 和社区对我的努力表示赞赏,我唯一的要求是将任何追溯性认可引导至而不是我个人:蚂蚁渴望获得资助,因为这将有助于他们扩大自己的行动,并像我昨天代表他们进行的这次小规模政变一样,实施更大胆的举措。

原文来源

Related Reads

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbit47m ago

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbit47m ago

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbit52m ago

Token Inefficient, Economy Tokenless

marsbit52m ago

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

In 2026, a historic shift occurred in AI as major cloud providers' inference spending surpassed training spending for the first time, signaling a move from "building large models" to "using large models." This shifts the core challenge from computing power to the "memory wall"—the bottleneck of data movement (model weights, activations, KV Cache) between external DRAM and processors, where energy and latency from data transfer far exceed computation itself. Companies like Nvidia face GPU idle time due to bandwidth limits. In contrast, Cerebras Systems adopts a radical "wafer-scale" approach with its Wafer-Scale Engine (WSE). Instead of cutting a silicon wafer into many chips, Cerebras uses almost the entire wafer as one massive chip (WSE-3). This design provides 44GB of on-chip SRAM, delivering memory bandwidth thousands of times higher than traditional HBM (e.g., 21 PB/s vs. Nvidia B200). For LLM inference, weights are streamed layer-by-layer from external MemoryX storage to the chip, avoiding HBM bottlenecks. This results in token generation speeds 1.5–5 times faster than Nvidia's B200 in some models and significant advantages in first-token latency and long-context tasks. Additionally, Cerebras's architecture offers much lower interconnect power consumption (0.15 pJ/bit vs. GPU's ~10 pJ/bit). However, Cerebras faces challenges: SRAM scaling has slowed with advanced nodes, limiting future capacity gains; the chip requires specialized liquid cooling and custom software stacks; and its external I/O bandwidth (150 GB/s) is low compared to NVLink, hindering multi-system scaling for very large models. Competition is intensifying. Major players are pursuing three paths: 1) Developing proprietary inference ASICs (e.g., Google TPU, Microsoft Maia), 2) Leveraging advanced packaging (e.g., TSMC's SoW) to democratize wafer-scale-like integration, potentially eroding Cerebras's process advantage within a few years, and 3) Exploring optical interconnects for ultimate bandwidth. Commercially, Cerebras is transitioning from a hardware vendor to a service provider, facing the immense challenge of building high-power, specialized data centers to meet large contracts (e.g., 250MW/year from 2026–2028). In conclusion, the AI inference era presents a fundamental architectural trade-off. Cerebras opts for extreme physical optimization for low-latency, single-task performance, while Nvidia prioritizes versatility and massive cluster throughput. The path forward remains uncertain, with technology and business models still evolving in the race toward advanced AI.

marsbit57m ago

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

marsbit57m ago

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

**Title: Has Bitcoin's Rebound Ended, Entering the Late Bear Market Phase?** **Summary:** Bitcoin's price has declined by 13% this week, signaling a potential return to late-stage bear market conditions. The price fell to around $67k, positioned between the Realized Price and Realized Cap Weighted Average. For the first time since early 2022, the Short-Term Holder cost basis has dropped below this key average, confirming a hallmark of late-cycle bear markets. Profitability metrics have collapsed sharply. The 7-day average of the Realized Profit/Loss ratio plummeted from a local high of 3.16 to 0.29, mirroring the February panic sell-off. Critically, the 90-day average never breached the threshold of 2, indicating the recent rally to $82k was a bear market bounce, not a structural shift. Realized losses surged to $1.35 billion daily, with $770 million coming from Long-Term Holders selling at a loss. This accelerating redistribution of supply from weak to strong hands is a necessary but ongoing process for a market bottom. The rally stalled almost precisely at the aggregate cost basis (~$83k) of US spot Bitcoin ETF investors, turning that level into strong resistance and leaving the average ETF holder underwater again. Spot market flows have turned decisively negative, showing sellers are dominating order books despite the price drop. While a significant futures long liquidation event cleared over $400 million in leverage, providing a potential reset, sustained spot demand is yet to materialize. Options markets continue to price in higher future volatility (Implied Volatility) than recent price action (Realized Volatility) has shown, with a persistent skew towards put options, indicating ongoing demand for downside protection. In conclusion, multiple metrics point to a fragile market structure. Resistance at the ETF cost basis, accelerating realized losses, dominant spot selling, and cautious options pricing all suggest the bear market trend persists. A sustainable recovery likely requires a resurgence of spot demand, ETF holders returning to profit, and a clear reduction in selling pressure.

marsbit58m ago

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

marsbit58m ago

TechFlow Intelligence Agency: Anthropic Calls for Global Pause in AI Development While Preparing for Trillion-Dollar IPO; SpaceX IPO Roadshow Heats Up, But S&P 500 Rejects Fast-Track Inclusion

In today's TechFlow Intelligence Briefing, several major tech stories highlight a growing theme of trust and credibility gaps across AI, crypto, and finance. AI company Anthropic has publicly called for a global pause in AI development, citing risks from Claude's "recursive self-improvement." Ironically, this coincides with reports the company is preparing for a massive IPO targeting a near $1 trillion valuation. This perceived hypocrisy, coupled with widespread user complaints about Claude's declining performance, is sparking debate over whether the safety warning is genuine or a competitive tactic. Meanwhile, in a substantive security move, Anthropic open-sourced a framework for AI-powered vulnerability discovery. In the crypto market, Bitcoin's price drop below $61,000 triggered over $1.16 billion in liquidations, flipping the market into a state where more BTC is held at a loss than at a profit, a historical bearish signal. On the corporate front, SpaceX's highly anticipated IPO is generating immense Wall Street excitement, with Goldman Sachs projecting 100x revenue growth by 2030. However, the S&P 500 has refused to fast-track the company's inclusion post-IPO, potentially limiting immediate institutional demand. Separately, ByteDance's AI app Doubao lost over 6 million monthly active users after introducing a subscription model, highlighting the challenges of AI monetization. Other notable developments include Nvidia certifying HBM4 memory from Samsung, SK Hynix, and Micron; Cloudflare's acquisition of front-end tooling company VoidZero; and its CEO warning that bot traffic now exceeds human traffic online. The underlying narrative connects these events: a trust crisis. From AI firms' contradictory actions and crypto volatility to the clash between SpaceX's hyped narrative and institutional rules, a pattern is emerging where stated intentions and actual practices are increasingly misaligned.

marsbit1h ago

TechFlow Intelligence Agency: Anthropic Calls for Global Pause in AI Development While Preparing for Trillion-Dollar IPO; SpaceX IPO Roadshow Heats Up, But S&P 500 Rejects Fast-Track Inclusion

marsbit1h ago

Trading

Spot
Futures
活动图片