CFTC pilot opens path for crypto as collateral in derivative markets

cointelegraphPublished on 2025-12-09Last updated on 2025-12-09

Abstract

The U.S. Commodity Futures Trading Commission (CFTC) has introduced updated guidance and a pilot program allowing futures commission merchants (FCMs) to accept Bitcoin, Ether, and USDC as collateral in derivatives trading. The initiative aims to integrate cryptocurrencies into regulated markets, enhance customer protection, reduce settlement friction through instant on-chain transfers, and improve risk management. The program includes strict reporting requirements for FCMs and withdraws outdated advisories. Industry leaders from firms like Circle, StarkWare, and Coinbase have praised the move as a significant step toward broader adoption of digital assets and more efficient, automated settlement processes in derivatives markets.

The US Commodity Futures Trading Commission (CFTC) has issued updated guidance for tokenized collateral in derivatives markets, paving the way for a pilot program to test how cryptocurrencies can be used as collateral in derivatives markets.

Collateral in derivatives markets serves as a security deposit, acting as a guarantee to ensure that a trader can cover any potential losses.

The digital asset pilot, announced by CFTC acting chairman Caroline Pham on Monday, will allow futures commission merchants (FCM) — a company that facilitates futures trades for clients — to accept Bitcoin (BTC), Ether (ETH) and Circle’s stablecoin USDC (USDC) for margin collateral.

The CFTC pilot is another step toward integrating crypto into regulated markets, and Circle CEO Heath Tarbert said it will also protect customers, reduce settlement frictions because tokenized collateral moves instantly onchain, and assist with risk reduction.

Pham said in a statement that the pilot program also “establishes clear guardrails to protect customer assets and provides enhanced CFTC monitoring and reporting.”

As part of the pilot, participating FCMs will be subject to strict reporting criteria, which require weekly reports on total customer holdings and any significant issues that may affect the use of crypto as collateral.

Source: Caroline Pham

Updated CFTC guidance for tokenized assets

The CFTC’s Market Participants Division, Division of Market Oversight, and Division of Clearing and Risk also issued updated guidance on the use of tokenized assets as collateral in the trading of futures and swaps.

The guidance covers tokenized real-world assets, including US Treasury’s money market funds, and topics such as eligible tokenized assets, legal enforceability, segregation, and control arrangements.

Pham said in an X post on Monday that the “guidance provides regulatory clarity and opens the door for more digital assets to be added as collateral by exchanges and brokers, in addition to US Treasurys and money market funds.”

The Market Participants Division also issued a “no-action position” on specific requirements regarding the use of payment stablecoins as customer margin collateral and the holding of certain proprietary payment stablecoins in segregated customer accounts.

A CFTC Staff Advisory that restricted FCMs’ ability to accept crypto as customer collateral, Staff Advisory 20-34, was also withdrawn because it is “outdated and no longer relevant,” in part due to the GENIUS Act.

Crypto execs back CFTC move

Several crypto executives applauded the move by the CFTC.

Katherine Kirkpatrick Bos, the general counsel at blockchain company StarkWare, said the use of “tokenized collateral in the derivatives markets is MASSIVE.”

Related: US regulators dismiss SEC-CFTC merger rumors, move to dispel crypto ‘FUD’

“Atomic settlement, transparency, automation, capital efficiency, savings. Feels abrupt but who recalls the tokenization summit in 2/24, a glimmer of hope in the darkness,” she said.

Coinbase chief legal officer Paul Grewal also supported the action, calling Staff Advisory 20-34 a “concrete ceiling on innovation.”

“It relied on outdated info, went well beyond the bounds of regulation and frustrated the goals of the PWG.”

Source: Paul Grewal

Meanwhile, Salman Banaei, the general counsel at layer-1 blockchain, the Plume Network, said it was a “major move” by the CFTC, and another push toward wider adoption.

“This is a step toward the use of onchain infra to automate settlement for the biggest asset class in the world: OTC derivatives, swaps,” he added.

Magazine: XRP’s ‘now or never’ moment, Kalshi taps Solana: Hodler’s Digest, Nov. 30 – Dec. 6

Related Reads

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Ethereum Q1 2026 Report: Fees Down, Users & Transactions Hit New Highs Token Terminal's Q1 2026 report on Ethereum presents a pivotal development: the network achieved record highs in monthly active users (13.2M, +85.9% YoY), total transactions (200.4M, +81.5% YoY), and throughput (25.78 TPS), while transaction fees on the mainnet plummeted by 47.9% quarter-over-quarter. This shift is attributed to the network's strategic move into a "low fees for scale" phase, exemplified by the Fusaka upgrade which increased data capacity and lowered block space costs, releasing pent-up demand (a manifestation of Jevons's Paradox). The report highlights a core narrative shift for Ethereum: from a DeFi-centric blockchain to a global financial settlement layer. It maintains a dominant position in tokenized assets, holding majority market shares among top chains in stablecoins (61.8%), tokenized funds (73.0%), and tokenized commodities (84.0%). Growth in tokenized funds (+73.1% YoY) and commodities (+325.9% YoY) was particularly strong, driven by institutions like BlackRock and JPMorgan entering the space. Contrasting these usage gains, several USD-denominated value metrics declined in Q1: fully diluted market cap fell 30.3% QoQ, total value locked (TVL) dropped 11.0%, and ecosystem transaction volume decreased 24.0%. The report interprets this as Ethereum prioritizing long-term network expansion and cementing its role as the default settlement layer for finance over short-term fee capture. The commentary from Etherealize argues that, much like the early internet, Ethereum's open, permissionless model is poised to win over closed alternatives as institutional tokenization accelerates.

marsbit22m ago

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

marsbit22m ago

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbit29m ago

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbit29m ago

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbit2h ago

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbit2h ago

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbit2h ago

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbit2h ago

Trading

Spot
Futures
活动图片