香港OTC监管三步走：从“币店江湖”到全链条纳管

比推Publicado em 2025-08-08Última atualização em 2025-08-08

作者：BlockSec

原标题：香港OTC监管三次进化：从“币店江湖”到全面纳管

2025 年 5 月，香港警方捣毁一个价值1500 万美元（约 1.17 亿港元）的虚拟资产洗钱集团，涉案团伙主要通过位于尖沙咀的OTC 渠道分拆、转移资金。

在早些时候，轰动全港的JPEX 案中，商业罪案调查科（Commercial Crime Bureau, CCB）披露，不少涉案资金通过在港 OTC 店铺完成兑换与转移，成为诈骗链条的重要一环。

2025 年 6 月，香港政府发布了《Legislative Proposal to Regulate Dealing in Virtual Assets》（《规管虚拟资产买卖服务立法建议》）公众咨询文件，建议将包括 OTC 在内的所有虚拟资产买卖服务纳入统一发牌监管框架。 虽然该建议目前仍处于咨询阶段，尚未形成法规，但它为香港虚拟资产监管的下一步描绘了清晰的蓝图——从早期的 VATP 平台发牌，到币店纳管，再到全面覆盖 VA Dealing 服务。

用一句话概括：三年时间，香港监管从 OTC 「真空地带」走向全链条纳管。

第一阶段（2023）VATP 纳入监管，OTC 却成「漏网之鱼」

2022 年底，香港通过《打击洗钱及恐怖分子资金筹集（修订）条例》，自 2023 年 6 月起，对虚拟资产交易平台（VATP）实施发牌制度，由证监会（SFC）监管。

VA Dealing Consultation Paper, 1.3

「In December 2022, … a licensing regime for VA trading platforms (」VATPs「) … commenced operation in June 2023 … must be licensed by the SFC unless otherwise permitted by the law.」VADEALING_consultation_…

根据VA exchange 的定义：

  • 通过电子设施撮合买卖双方的虚拟资产交易；

  • 接触客户资产（持有、控制或安排托管）

因此，当时的制度只针对「电子平台 + 接触客户资产」的业务，实体币店、柜台、ATM 等 OTC 场景并未纳入，这导致出现了监管的真空。

第二阶段（2024）海关发牌，加密货币 OTC 也需持牌

2024 年 2 月至 4 月，财政司及库务局（FSTB）推出首轮《虚拟资产场外交易服务发牌制度》咨询，首次将实体 OTC 纳入监管。

主要内容：

  • 所有人在港经营虚拟资产现货交易（实体或线上）均须持牌；

  • 由香港海关（CCE）负责发牌；

  • 涵盖 USDT、BTC 等法币兑换与转账；

VA Dealing Consultation Paper, 1.6(a)-(b)：

「Scope and coverage: Any person … services of spot trade of any VAs … would have to be licensed by the Commissioner of Customs and Excise (」CCE「).Eligibility: A licensee would be required to be a locally incorporated company …」

第三阶段（2025）OTC 并入 VASP 大家庭，SFC 统一监管

2025 年 6 月，香港发布第二轮《规管虚拟资产买卖服务立法建议》（Legislative Proposal to Regulate Dealing in Virtual Assets），监管范围和深度双升级：

  • 范围扩展：涵盖大宗交易、经纪撮合、结算兑换、资产管理等复杂服务；

  • 监管机构调整：由 SFC 发牌，HKMA 监管银行 /SVF 业务；

  • 原则延续：同业务、同风险、同规则；

  • 豁免安排：仅在一级市场发行 / 赎回稳定币且已获 HKMA 许可的发行人可豁免。

VA Dealing Consultation Paper, 1.10：

「Under the proposed regime, any person … providing the VA service of dealing in any VAs in Hong Kong is required to be licensed by or registered with the SFC… including conversion, brokerage, block trading…」

变化原因：这一轮建议正是建立在首轮咨询收到的 70 多份书面意见基础上制定的，政府在文件中说明，意见集中反映了 OTC 高风险性、跨境洗钱漏洞、监管覆盖不足等问题，因此将原有的 OTC 监管建议扩展为更广泛的「VA Dealing」框架。

VA Dealing Consultation Paper, 1.8：

「Following the conclusion of the first round of consultation, we received over 70 written submissions from various stakeholders… We have refined our proposal to expand the scope to VA dealing services to better address AML/CFT risks.」

重要提示：该阶段的内容目前仍处于公众咨询阶段，并未正式立法，最终细节可能在立法过程中调整。

政策变化背后的驱动

香港 OTC 监管政策的三次演变，并非孤立发生，而是多重因素叠加推动的结果，背后至少有三个核心驱动：

驱动一：重大案件频发，暴露监管真空

2025 年 5 月的 1500 万美元洗钱案中，涉案团伙利用 OTC 拆分资金、绕过银行监控，短时间完成多笔跨境转账。在 JPEX 案中，商业罪案调查科（CCB）发现，不少投资者被骗资金通过本地 OTC 店完成现金或稳定币兑换，再快速流向境外钱包。

这些案件暴露了一个问题：即使平台监管收紧，线下 OTC 的匿名、即时结算特性依然能绕开监管，成为「最后一公里」的风险通道。

驱动二： 国际监管压力与 FATF 标准

FATF（金融行动特别工作组）自 2019 年更新第 15 号建议（Recommendation 15）起，明确要求各司法辖区将虚拟资产服务提供者（VASPs）全面纳入反洗钱 / 反恐怖融资（AML/CFT）框架。香港在首次引入 VATP 发牌时，虽然已满足部分 FATF 要求，但 OTC 业务的「漏网」状态被国际评估机构和合作方多次指出。为维持香港国际金融中心的信誉，监管机构必须弥补这一漏洞，确保「same business, same risk, same rules」落到实处。

香港要做国际虚拟资产中心，必须解决 AML/CFT 隐患。

驱动三： 本地公众意见推动政策升级

在 2024 年首轮 OTC 咨询中，政府共收到 70 多份书面公众意见，来自银行、合规机构、加密企业、执法部门等。多数意见集中反映：OTC 的匿名交易风险高；跨境资金流难以追踪；诈骗和洗钱案件中，OTC 扮演重要中介角色。

政府在 2025 年发布的《VA Dealing》立法建议中明确指出，正是基于这些反馈，才将原本仅覆盖 OTC 兑换的监管范围扩大到更完整的VA Dealing 全链条业务。

VA Dealing Consultation Paper, 1.8：

「Following the conclusion of the first round of consultation, we received over 70 written submissions from various stakeholders… We have refined our proposal to expand the scope to VA dealing services to better address AML/CFT risks.」

总结

OTC 曾是香港加密货币市场的「地下水路」，如今它正被纳入光天化日之下。从 2023 年的平台监管，到 2024 年的币店纳管，再到 2025 年提出的全链条「VA Dealing」框架，香港虚拟资产监管正走向系统化与国际化。而这一切的最新篇章，正处于公众咨询期，等待立法的最后定稿。

Twitter：https://twitter.com/BitpushNewsCN

比推 TG 交流群：https://t.me/BitPushCommunity

比推 TG 订阅： https://t.me/bitpush

原文链接:https://www.panewslab.com/zh/articles/f3b12d05-0fb3-40ca-9f46-3544828a6499
说明: 比推所有文章只代表作者观点，不构成投资建议
相关新闻
    没有相关文章

Leituras Relacionadas

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

Three Years Later: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's launch, I made 20 predictions about its future. Now, in mid-2026, I've used AI agents to fact-check each one against the latest data. Overall, most major directional forecasts were correct, with only one outright error (incorrectly stating GPT-4 had 100 trillion parameters). Key successes included predicting that RAG and retrieval architectures would become the standard for handling knowledge and hallucinations, that natural language interfaces (LUI) would create a massive new industry layer beyond the models themselves, and that China would develop viable large language models, significantly closing the performance gap with Western counterparts within about three years. Predictions about the absence of mass unemployment, the rise of a new "robot network" for agent communication, and ChatGPT not possessing consciousness also held true in their core arguments. However, the "devil was in the details." Errors frequently involved specific numbers, timelines, or overlooking distributional effects. I tended to overestimate the speed of adoption (e.g., for agent networks) while underestimating the ultimate scale of capabilities or costs (e.g., AI winning IMO gold without tools, or the extreme capital required for frontier models). Other misjudgments included: underestimating how AI would reinforce, not dissolve, information filter bubbles; incorrectly assuming AI-generated content would easily circumvent copyright (it has instead triggered record-breaking settlements); and misidentifying where value would be captured (it accrued overwhelmingly to the compute layer, like Nvidia, not just the application or model layers). Key lessons from reviewing these predictions are: 1) Directional and mechanistic insights are far more reliable than precise numbers or absolute statements. 2) There's a consistent bias to overestimate short-term speed but underestimate long-term magnitude. 3) Errors often lie in missing distributional impacts within a generally correct aggregate trend. 4) Predictions phrased with nuance and caveats aged the best. 5) Some fundamental debates (e.g., on machine consciousness or the ultimate value chain) remain unresolved even after three years. This exercise is less about scoring the past and more about establishing rules for clearer thinking about the next three years of AI.

marsbitHá 2h

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

marsbitHá 2h

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

Looking Back After Three Years: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's debut and before GPT-4's release, I made over twenty predictions about AI's future based on limited information and intuition. Now, in May 2026, I revisited those forecasts using an AI-driven analysis with 41 Opus 4.8 agents to cross-reference them with the latest data. The assessment used symbols: ✅ Correct, 🟢 Mostly Correct, 🟡 Partially Correct, ❌ Incorrect. Overall, the directional judgments held up well, with only one major factual error regarding GPT-4's rumored parameter size (incorrectly cited as 100T). However, nuances and degrees of accuracy revealed more. **What Was Largely Correct:** Predictions about mechanisms and directions proved accurate. The rise of RAG (Retrieval-Augmented Generation) as the standard architecture for combating AI hallucination was confirmed, as was the transformative potential of LUI (Language User Interface) in creating a new industry layer atop GUIs. The emergence of "robot networks" (agent-to-agent communication protocols) and China's rapid catch-up in developing capable large models (closing the performance gap with top models to ~2.7%) were also on point. The analysis affirmed that LLMs lack consciousness and that the Turing Test merely measures perceived intelligence. **What Was Off Target:** Errors often involved specific numbers, over-optimistic timelines, or misjudged distributions. The prediction that value would primarily accrue to the application layer was half-right but missed NVIDIA's dominance as the profitable infrastructure layer. Forecasts about AI circumventing copyright issues and fostering a "global common ground" by averaging human viewpoints were incorrect; instead, major copyright settlements occurred and AI personalization is increasing. Estimates for model training costs ("$5-10 billion cap") were significantly off, underestimating frontier costs and overestimating replication costs. The notion that LLMs could never do complex math without tools was disproven by later models winning IMO gold. **Key Patterns from the Review:** 1. **Direction over precision:** Judgments about mechanisms and trends were more reliable than specific numbers or definitive statements. 2. **Timing bias:** There was a tendency to overestimate short-term speed but underestimate long-term magnitude and transformation. 3. **The distribution blind spot:** Aggregate-level correctness often masked uneven impacts (e.g., on young professionals' employment). 4. **The value of qualifiers:** Predictions framed with caution (e.g., "reportedly," "for now," "prototype in 2-3 years") aged better. 5. **Some debates continue:** Issues like the nature of "emergent abilities" or machine consciousness remain unresolved. This three-year review highlights that while seeing the big picture is crucial, humility regarding specifics, timelines, and disparate impacts is essential for future forecasting.

链捕手Há 5h

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

链捕手Há 5h

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

The article issues a stark warning about a potential AI investment bubble. It notes that while the AI boom shares similarities with the TMT bubble of the late 1990s, its scale is vastly larger, currently driving 93% of U.S. GDP growth. Major hyperscale cloud providers like Microsoft, Alphabet, Amazon, Meta, and Oracle are planning to invest trillions in AI data centers over the coming years. However, calculations based on analyst projections for 2025-2030 reveal a concerning math problem: expected capital expenditure growth far outpaces projected revenue growth. Even under an extremely optimistic scenario of zero costs, the implied return on investment for most of these tech giants (except Amazon) is deeply negative. This suggests that the current trajectory could lead to one of history's largest shareholder value destruction events. The piece outlines two potential escapes: AI generating vastly more revenue than currently anticipated—a near-impossible task—or a significant cutback in the planned investment splurge. The latter scenario could trigger a domino effect, severely impacting the entire tech supply chain (from Nvidia to TSMC), potentially pushing the U.S. economy into recession, and causing a major stock market downturn. The author suggests upcoming high-profile IPOs by companies like OpenAI and Anthropic might represent a transfer of risk from early investors to public market participants. While the peak of the hype cycle might sustain investment through 2026, the fundamental financial dilemma remains unresolved, setting the stage for a potential market correction in 2027 or 2028, similar to the years following Alan Greenspan's "irrational exuberance" warning.

marsbitHá 6h

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

marsbitHá 6h

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

The article "From Token to Machine Labor: AI is Evolving from Tool to 'Worker'" argues that the business model for AI is shifting beyond simply selling computational resources (tokens, GPU hours) or model access. Instead, a new "machine labor market" is emerging, where the core economic transaction is the purchase of economically useful work directly performed by software. The central thesis is that AI pricing will evolve through four stages: 1) raw tokens, 2) standardized LLM capabilities (e.g., text generation), 3) industry-specific labor markets (e.g., legal review, radiology), and finally 4) a programmable results market where tasks like resolving a support ticket are bid on and priced based on outcome. In this future, buyers will care less about *which* model or GPU completes a task and more about whether the work meets specified standards for accuracy, latency, and cost. This transition reframes the impact of AI on human labor. Rather than simple replacement, it suggests a re-coordination where machines handle standardized, verifiable work, freeing humans for roles involving oversight, context management, responsibility, and final judgment. In some cases, this "last 1%" of human input becomes more valuable as it enables the other 99% to be automated. Furthermore, as AI reduces the cost of work, demand may expand, creating larger markets (e.g., 24/7 customer service) rather than just cheaper versions of existing ones. The article concludes that while infrastructure (GPUs, models, tokens) remains crucial upstream, the market is converging on a simpler, tradeable unit: machine labor that can be defined, measured, priced, and procured based on contractible specifications.

marsbitHá 6h

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

marsbitHá 6h

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

The price of Xiaomi's MiMo-V2.5 series API has been permanently reduced by up to 99%, specifically for the "Input (Cache Hit)" cost, which covers users re-reading historical context in long conversations. MiMo's head, Luo Fuli, published a detailed technical blog to clarify that this drastic price cut stems from genuine engineering breakthroughs, not a marketing stunt or a simple price war. The core of the achievement lies in six key engineering optimizations. First, the model architecture adopts a Hybrid Sliding Window Attention (SWA), reducing the memory footprint (KVCache) to 1/7th of a traditional model. Second, a dual-pool memory management system actually utilizes these savings, allowing a single GPU to handle over 5 times more concurrent users. Third, an upgraded prefix caching mechanism achieves a cache hit rate of 93-95% for repeated reads, meaning most such requests bypass GPU computation entirely. Fourth, a self-developed distributed cache (GCache) utilizes idle SSD space on existing GPU servers, eliminating additional storage costs. Fifth, an intelligent scheduling system (LLM-Router) efficiently routes requests to maximize cache reuse and performance. Sixth, Multi-Token Prediction (MTP) accelerates the model's text generation ("output") side. Together, these systemic optimizations dramatically lower the real computational cost per request, enabling the 99% price reduction for cached inputs while reportedly maintaining positive gross margins. Luo Fuli's disclosure aims to shift the narrative from "price war" to a demonstration of substantive AI engineering progress.

marsbitHá 8h

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

marsbitHá 8h

Trading

Spot
Futuros
活动图片

Categorias popularesright-arrow

Etiquetas Popularesright-arrow