Hong Kong to Issue First Stablecoin Licences in Early 2025

TheNewsCryptoОпубліковано о 2026-01-21Востаннє оновлено о 2026-01-21

Анотація

Hong Kong is set to issue its first batch of stablecoin licences in the first quarter of 2025, as part of its strategy to become a regional hub for digital assets. Financial Secretary Paul Chan announced the plan at the World Economic Forum in Davos, emphasizing the city’s responsible and sustainable approach to crypto regulation. The licensing regime, introduced last year, imposes strict requirements on stablecoin issuers, including rules on reserve backing, governance, and risk management. This initiative is part of a broader effort to build a comprehensive digital asset ecosystem, which also includes licensed trading platforms and tokenised financial products. Hong Kong has already licensed 11 virtual asset trading platforms, including OSL and HashKey, and is advancing in tokenisation through initiatives like Project Ensemble. Meanwhile, regulators are developing additional proposals for licensing crypto-related services, though concerns have been raised about potential compliance costs affecting traditional asset managers.

Hong Kong is getting ready to issue its first batch of stablecoin issuer licences in Q1 of 2025, accelerating its efforts to place itself as a regional hub for digital assets at a time of increasing global competition.

The Financial Secretary of Hong Kong, Paul Chan, spoke at the World Economic Forum in Davos about how the approach of the city towards crypto regulation is responsible and sustainable, as revealed by the South China Morning Post.

The financial secretary committed that the starting round of stablecoin licences is anticipated to be allowed in the near future. Chan established stablecoins as a segment of a wider push to make a complete digital asset ecosystem in Hong Kong, bridging regulated stablecoin issuance, licensed trading platforms and tokenised financial products.

He characterised digital finance as a strategic growth pillar as the city looks to maintain its status as a global financial centre. The stablecoin licensing regime passed last year describes strict requirements for fiat-referenced stablecoin issuers.

These comprise rules on reserve backing, redemption rights, governance, and risk management, showing the focus of regulators on financial stability and consumer protection after volatility in global crypto markets.

The Other Works By the Country

The stablecoin plans of the company stand beside a so far active framework for crypto trading platforms. As per the rules imposed by the Securities and Futures Commission, 11 virtual asset trading platforms have got licences to date.

Approved operators comprise OSL, HashKey and Bullish, as per the public disclosures of the regulator. After trading in stablecoins, Hong Kong is also delving deeper into tokenisation.

Last year, in November, the Hong Kong Monetary Authority rolled out a pilot under Project Ensemble to test actual-value transactions leveraging tokenised deposits and digital assets, comprising major banks and asset managers.

Meanwhile, regulators are working on additional proposals that would roll out new licensing regimes for crypto asset dealing, advisory, and management services. At the start of this week, the Hong Kong Securities and Futures Professionals Association alerted that tighter virtual asset management regulations could put off traditional asset managers by increasing compliance costs and decelerating institutional participation.

Highlighted Crypto News Today:

Binance has Announced Listing Ripple’s Stablecoin, RLUSD

TagsHong KongLicenseStablecoin

Пов'язані питання

QWhen is Hong Kong planning to issue its first batch of stablecoin licences?

AIn Q1 of 2025.

QWho is the Financial Secretary of Hong Kong and where did he discuss the city's crypto regulation approach?

APaul Chan, and he spoke about it at the World Economic Forum in Davos.

QWhat are some of the key requirements for stablecoin issuers under Hong Kong's new licensing regime?

AThe requirements include rules on reserve backing, redemption rights, governance, and risk management.

QName two licensed virtual asset trading platforms in Hong Kong mentioned in the article.

AOSL and HashKey.

QWhat is the name of the project launched by the Hong Kong Monetary Authority to test tokenised transactions?

AProject Ensemble.

Пов'язані матеріали

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit3 хв тому

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit3 хв тому

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit4 хв тому

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit4 хв тому

Торгівля

Спот
活动图片