SignalPlus波动率专栏(20240909)：又是宏观周

Odaily星球日报Publicado em 2024-09-09Última atualização em 2024-09-09

Resumo

上周五低于预期的非农就业数据以及前值下修再次引发了人们对经济衰退的担忧，导致美股创下 2023 年 3 月以来最差的单周表现，消极的风险情绪也拖累了数字货币的表现，再度印证了来自季节性统计数据的诅咒...

SignalPlus波动率专栏(20240909)：又是宏观周

SignalPlus波动率专栏(20240909)：又是宏观周

上周五低于预期的非农就业数据以及前值下修再次引发了人们对经济衰退的担忧，导致美股创下 2023 年 3 月以来最差的单周表现，消极的风险情绪也拖累了数字货币的表现，再度印证了来自季节性统计数据的诅咒。与此同时，美债出现牛陡走势， 2/10 s 收益率终于结束了倒挂形态。

SignalPlus波动率专栏(20240909)：又是宏观周

Source: SignalPlus, Economic Calendar

本周加密社区的关注点依然围绕宏观事件，在平静的周末结束后，期权做市商已经悄然拉高了前端的 IV，尽管 Term Structure 上 BTC 和 ETH 十分相似，但细节上略有不同。首先注意到 BTC 前端的峰值在 11 SEP 而 ETH 则是在 12 SEP，两个到期日 Price-In 了非常高的 Vol Premium。

12 SEP 的不确定主要来自 CPI 数据，尽管通胀数据的影响力已经退居就业数据之后，但本周再次成为了金融交易员心中的头等大事，并会许会成为决定美联储是否会在 9 月下旬的 FOMC 上大幅降息 50 个基点的决定性因素。

对购买 11 SEP 的交易员来说，他们主要看中的还是北京时间上午 9 点举行的总统竞选演讲。这场对抗是特朗普和哈里斯第一次，或许也是唯一一次正面交锋的机会，距离正式的选举日已经不到两个月，这迫使他们抓住每个机会来说服选民。有关数字货币的话题势必也会在双方的对话中出现，市场赋予了 BTC 更高的相关性，这也在期限结构上得到了体现。

SignalPlus波动率专栏(20240909)：又是宏观周

SignalPlus波动率专栏(20240909)：又是宏观周

Source: SignalPlus

期限结构的中段形成了以 25 OCT 为最低点的谷底，不确定性被分配到了 FOMC 会议之前和总统大选的日子上。两币种在 Forward IV 的结构上略有差别，ETH 12 SEP-13 SEP 的这段低点有机会被调平修正，月末 ETH 平坦的斜率以及高出 BTC 10% 的 Vol Premium 或许也能吸引到跨币种的波动率交易。

从波动率微笑的曲率上看，远端的 Fly 在经历了短暂的上升后得到重新回调，接近一周前的水平。

SignalPlus波动率专栏(20240909)：又是宏观周

Source: Deribit （截至 9 SEP 16: 00 UTC+ 8）

SignalPlus波动率专栏(20240909)：又是宏观周

Source: SignalPlus

SignalPlus波动率专栏(20240909)：又是宏观周

您可在 t.signalplus.com 使用 SignalPlus 交易风向标功能，获取更多实时加密资讯。如果想即时收到我们的更新，欢迎关注我们的推特账号@SignalPlusCN，或者加入我们的微信群（添加小助手微信：SignalPlus 123）、Telegram 群以及 Discord 社群，和更多朋友一起交流互动。

SignalPlus Official Website：https://www.signalplus.com

Leituras Relacionadas

Google CEO Admits Lagging Behind in Coding

Google CEO Sundar Pichai acknowledged in a recent interview that Google's Gemini AI models are currently "lagging behind" in coding capabilities, particularly for complex, long-horizon tasks requiring advanced developer expertise. He noted the field is advancing at an "unprecedented" pace, where 30-60 days now brings changes equivalent to five years in the past. Pichai expressed that achieving Artificial General Intelligence (AGI) now seems closer than previously imagined due to rapid progress. While highlighting strengths in text, multimodal, and reasoning tasks, Pichai admitted competitors like Anthropic and OpenAI have focused more intently on coding. He emphasized Google's commitment to catching up, citing internal tools like Antigravity 2.0 and the newly released Gemini 3.5 Flash, which aims to address previous shortcomings. Regarding Google Search's AI-driven overhaul, Pichai stated changes will be gradual to align with user needs, not disrupt the core search experience or its advertising model. He addressed public AI anxiety as understandable, given the technology's potential to reshape jobs and society, but remained optimistic about AI augmenting human capabilities and creating new opportunities. Pichai stressed the need for broad societal dialogue and responsible development as AI approaches more advanced, potentially recursive self-improvement stages. He affirmed Google's long-term commitment to leading in AI while navigating its profound implications responsibly.

marsbitHá 1h

Google CEO Admits Lagging Behind in Coding

marsbitHá 1h

The Paradox of Automation: The Stronger the AI, the Busier Humans Become

The Paradox of Automation: The more powerful AI becomes, the more work humans have to do. This article, based on observations from AI-heavy company Every, argues that while AI agents automate tasks like coding, writing, and customer service, they don't eliminate human jobs. Instead, they transform work and create *more* demand for human expertise. AI commoditizes "yesterday's human capabilities" by cheaply generating code, text, and images from past data. This leads to an abundance of similar, generic outputs. Consequently, what becomes scarce and valuable is human judgment in the present moment: knowing *what* is worth doing, *why*, and *how* to do it well. The article identifies two collaboration models: "Agent employees" for delegated tasks and "human-AI collaboration" within tools like Claude Code for complex work. In both cases, humans are essential to set direction, judge quality, and maintain systems. As AI makes execution cheap, human roles shift from executors to designers, reviewers, and meaning-makers. The author addresses "benchmark anxiety" by explaining that AI excels within specific, human-defined problem "frames." As AI masters one frame (e.g., code rewriting), new, more complex frames emerge (e.g., deciding *when* to rewrite). This creates an ongoing cycle where AI chases the frames, but humans remain the "framers." Even with advanced AGI, this dynamic may persist as long as AI lacks true human-like agency and self-directed purpose. The core paradox holds: automation amplifies the need for the very human judgment it seems to replace.

marsbitHá 2h

The Paradox of Automation: The Stronger the AI, the Busier Humans Become

marsbitHá 2h

a16z: 7 Charts to Understand How Tokenization is Changing the Nature of Assets

"a16z: 7 Charts on How Tokenization is Changing the Nature of Assets" Tokenized Assets (or Real-World Assets - RWA) are transforming asset forms, liquidity, and financial system construction. The market recently surpassed $30 billion, stabilizing around $34 billion (excluding stablecoins), representing a tenfold increase in less than two years, driven by clearer regulations, mature institutional infrastructure, and increased financial institution adoption. The primary driver of recent growth is tokenized U.S. Treasury bonds. These offer investors efficient, flexible digital access to yield-bearing assets and improve institutional operations like settlement and collateral management. Other asset classes show varied growth: asset-backed credit leads, followed by niche financial assets (e.g., reinsurance, mining notes), while venture capital took longer to scale. Market segmentation shows high concentration. In commodities, tokenized gold dominates (~$5 billion), as its standardized, storable nature fits tokenization well. Bonds are the largest category ($15.2B), but only ~5% are used in DeFi protocols. Conversely, smaller niches like reinsurance tokens see high (~84%) on-chain utilization, highlighting a core industry divide: most current tokenized assets are merely digitized records for easier holding/transfer, lacking the "composability" (free combination/interaction) that is key to blockchain-native finance. The ecosystem is distributed across multiple blockchains, with Ethereum hosting over half the value ($15.7B), followed by BNB Chain, Solana, and others. Future market size predictions vary widely (e.g., $2-$30 trillion by 2030+), but all indicate massive potential from the current small base. Tokenized assets currently represent minuscule fractions of their global counterparts (e.g., 0.01% of global bonds). The current phase focuses on digitizing straightforward assets. The next challenge is to bring more complex financial components on-chain and deeply integrate tokenized assets into composable, internet-native financial infrastructure.

链捕手Há 3h

a16z: 7 Charts to Understand How Tokenization is Changing the Nature of Assets

链捕手Há 3h

a16z: How Tokenization is Transforming the Nature of Assets in 7 Charts

"Tokenized Assets: How Tokenization Changes the Nature of Assets" by a16z Crypto The market for tokenized assets, excluding stablecoins, has grown from under $3 billion two years ago to over $340 billion today. US Treasury bonds are the primary growth driver, allowing investors to hold yield-bearing assets digitally and enabling more efficient settlement. Other key sectors include private credit (growing fastest), commodities (dominated by gold), and niche financial assets. However, the market remains concentrated in tokenized US Treasuries and gold. A critical insight is that most tokenized assets currently lack "composability." While the total market is large, only a small fraction is actively used within DeFi protocols. For instance, only about 5% of tokenized bonds and a low percentage of tokenized gold are utilized on-chain. In contrast, assets like reinsurance and private credit tokens show much higher on-chain usage rates (84% and 33%, respectively). This highlights a divide: many tokenized assets are merely digital records on a blockchain without enabling new, programmable financial applications. The Pantera Capital Token Native Index indicates over 70% of tokenized assets have minimal on-chain native functionality. Ethereum remains the dominant blockchain for tokenized assets (over $150B), but the ecosystem is diversifying across chains like BNB Chain, Solana, and Stellar, based on factors like cost and compliance. Major institutions forecast massive future growth, with predictions for the tokenized asset market ranging from $2 trillion to over $30 trillion by the early 2030s. However, compared to the global financial system (e.g., ~$140T bonds, multi-trillion dollar gold market), tokenized assets currently represent a tiny fraction (0.01% or less). The conclusion is that while tokenization has begun by digitizing and streamlining settlement for simpler assets, the next phase involves bringing more complex financial instruments on-chain and deeply integrating them into composable, internet-native financial infrastructure.

Odaily星球日报Há 3h

a16z: How Tokenization is Transforming the Nature of Assets in 7 Charts

Odaily星球日报Há 3h

The Revived Codex, Carrying OpenAI's Hopes for IPO

This article analyzes the intense recent development of OpenAI's Codex, positioning it as a crucial component for OpenAI's impending IPO. Over the past two months, Codex has seen a rapid series of major updates focused on integrating into real enterprise workflows. Key new features include enhanced context capture (Appshots, file previews, built-in browser), long-running task execution ("Goal Mode"), remote operation (phone control, lock-screen access), and enterprise management tools (plugin sharing, access tokens, automated risk review). These updates aim to make Codex a comprehensive AI workbench that can "see the scene, push tasks, and manage risks." The author argues that while ChatGPT proves OpenAI's massive user base and API provides foundational revenue, Codex represents OpenAI's clearest path to demonstrating tangible, high-value commercial viability. It targets developers and engineering teams—a segment already accustomed to paying for efficiency gains in costly software development cycles. This is critical because, despite higher overall revenue, OpenAI's adjusted operating margins remain deeply negative, highlighting the challenge of outrunning immense compute costs. The pressure is amplified by competitor Anthropic's success with Claude Code, which has shown that a focused approach on high-value enterprise and developer workflows can lead to a path toward profitability. Codex's aggressive evolution is thus seen as OpenAI's strategic move to capture a similar enterprise-ready, revenue-generating narrative essential for its market debut. In essence, "ChatGPT proved OpenAI has users. Codex needs to prove OpenAI is a business that can make money."

marsbitHá 4h

The Revived Codex, Carrying OpenAI's Hopes for IPO

marsbitHá 4h

Trading

Spot
Futuros
活动图片

Categorias popularesright-arrow

Etiquetas Popularesright-arrow