CoinDeskPolicyОпубліковано о 2024-04-08Востаннє оновлено о 2024-04-09

Анотація

Countries have varying definitions and categorizations for stablecoins that may pose a risk to financial stability, the report by the Financial Stability Institute said.

  • Countries around the world must ensure consistency in their approaches to regulating stablecoins, a new report by the Financial Stability Institute says.
  • Differing approaches could pose challenges to an integrated financial system, the FSI added in its report.

Countries need to make their regulatory frameworks for stablecoins consistent with one another, the Financial Stability Institute (FSI) warned in a report published Tuesday.

The FSI, jointly created by the Bank for International Settlements and the Basel Committee on Banking Supervision, is tasked with assisting regulators worldwide in strengthening their financial systems. The institute's report on policy implementation insights for stablecoins – which refers to cryptocurrencies whose value is pegged to other assets such as sovereign currencies – warns of the dangers of fragmentation in supervision across the world.

"Stablecoins may still be unregulated or lightly regulated in other jurisdictions," said the report, authored by FSI Deputy Chair Juan Carlos Crisanto and Senior Advisors Johannes Ehrentraud and Denise Garcia Ocampo.

Advertisement
Advertisement

The authors argued that while many regulatory approaches have similarities when it comes to key requirements, the differences are largely driven by the variety of stablecoin design features and perceived risks. The report warned that this fragmentation in approaches to supervision could pose challenges to an integrated financial system and threaten financial stability.

Nations around the world have been exploring how to regulate stablecoins for several years. The U.K., for instance, passed legislation to recognize stablecoins as a means of payment in 2023, while the European Union passed the landmark Markets in Crypto Assets regulation (MiCA) to supervise issuers and service providers handling stablecoins. Japan too has started regulating stablecoins, while the U.S. is considering a stablecoin bill.

The FSI report says jurisdictions have varying definitions and categorizations for stablecoins that may pose a risk to financial stability. There are also discrepancies in requirements for the disclosure of reserve assets kept by stablecoin issuers to maintain the crypto's value against its reference currency.

Advertisement
Advertisement

"A consistent regulatory framework, as well as its global implementation, is essential to address stablecoins’ risks, prevent regulatory arbitrage and ensure a level playing field in the digital asset ecosystem," the FSI report said.

Ensuring the interoperability of stablecoins with central bank digital currencies (CBDC) and other digital assets would also be key to promoting an integrated financial system, the report added.

Global organizations such as the International Monetary Fund (IMF) and Financial Stability Board (FSB) have issued or are working on universal norms for stablecoins.

Edited by Sandali Handagama.

Пов'язані матеріали

The Computing Power Dilemma in the Sino-US AI Rivalry

The Sino-US AI rivalry faces a fundamental bottleneck: the widening compute power gap. While Chinese AI chip companies have seen investment surges, their current focus remains largely on the less demanding inference market. The real challenge lies in the high-end training chip sector, crucial for developing cutting-edge large language models (LLMs), where Nvidia holds a near-monopoly. The compute disparity is stark. US tech giants like Meta, Google, and xAI command massive GPU clusters, enabling them to train trillion-parameter models rapidly. Estimates suggest US data center count and total compute capacity significantly outstrip China's. This "brute force" advantage allows for faster model iteration and exploration of larger parameter scales, with top US models reportedly leading their Chinese counterparts by 8 to 15 months. Chinese alternatives, such as Huawei's Ascend and others from companies like Moore Thread and Biren, are emerging. They show promise in inference and some training scenarios, closing the performance gap with mid-range Nvidia products. However, the core hurdle extends beyond raw chip performance to the entrenched software ecosystem, exemplified by Nvidia's CUDA platform. The path forward involves "walking on two legs": navigating import restrictions while heavily investing in the domestic chip industry. Though still in a catch-up phase, China's vast market, talent pool, and capital are fostering progress. The ultimate test is whether Chinese firms can build a competitive hardware-software ecosystem to power the next generation of AI.

marsbit7 хв тому

The Computing Power Dilemma in the Sino-US AI Rivalry

marsbit7 хв тому

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

KaiMing He's team introduces **MiniT2I**, a minimalist text-to-image (T2I) model that challenges the complexity of mainstream approaches. It eliminates components commonly considered essential: the VAE encoder-decoder, AdaLN conditioning mechanisms, auxiliary losses, private training data, and post-training alignment stages like RL/DPO. Instead, it uses a pure flow-matching objective trained directly on RGB pixels. The model employs a simplified **MM-JiT** Transformer architecture. It removes AdaLN blocks for conditioning and instead prepends two lightweight text adapter blocks to a standard pre-norm Transformer, allowing frozen T5 text features to adapt to the denoiser. Training follows a two-stage, LLM-like paradigm using only public datasets: pre-training on LLaVA-recaptioned CC12M for coverage, followed by fine-tuning on ~120k high-quality image-text pairs. With just 258M parameters (B/16), MiniT2I achieves competitive scores (0.87 on GenEval, 84.2 on DPG-Bench), outperforming larger pixel-space models. Scaling to 912M parameters (L/16) yields results comparable to SD3-Medium (~2B parameters) in style, composition, and imagination, though it lags in text rendering and named entities due to public data limitations. Key advantages include lower computational cost (~570 GFLOPs vs. ~1379 for latent models) and architectural simplicity. Acknowledged limitations include patch boundary artifacts in pixel space, side effects of high CFG scales, resolution ceilings for sequences longer than 1024 tokens, and the aforementioned data bottlenecks. The work demonstrates that high-performance T2I generation is possible with a radically simplified, publicly reproducible baseline.

marsbit12 хв тому

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

marsbit12 хв тому

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

The insurance industry, long a stable "ballast" in the economy, may face a significant challenge from the rise of prediction markets, which are beginning to function as a new form of risk hedging and insurance. Platforms like Kalshi and Polymarket are demonstrating their utility in areas traditionally dominated by insurers. Examples include Kalshi's partnership with sports insurance broker Game Point Capital to offer more cost-effective hedging for NBA team performance bonuses, and Polymarket's collaboration with real estate platform Parcl, allowing users to hedge against housing price fluctuations in major US cities. A New York bar also used Kalshi to hedge a marketing promotion tied to an NBA game outcome, highlighting prediction markets' potential for small business risk management. These markets offer advantages over traditional insurance and sports betting in transparency, liquidity, and flexibility. They allow information monetization across a wider range of events, act as neutral platforms rather than direct counterparties, and provide clearer pricing. A historical precedent is the "Mattress Mack" marketing campaigns, which used sports betting for large-scale customer refunds, but prediction markets offer a more systematic and accessible model. Experts like SIG CEO Jeff Yass see their potential for efficient, parameter-based risk sharing, such as for weather-related property damage. However, challenges remain, including liquidity issues, unclear regulatory boundaries, and potential manipulation of event outcomes. Despite these hurdles, prediction markets represent a growing competitive force for both traditional gambling platforms and segments of the insurance industry.

marsbit13 хв тому

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

marsbit13 хв тому

Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the 'Barbarians at the Gate'?

"Insurance Industry Faces New Rival: Are Prediction Markets the 'Barbarians at the Gate'?" Prediction markets, exemplified by platforms like Kalshi and Polymarket, are emerging as potential disruptors to the traditional insurance industry by offering alternative risk-hedging mechanisms. These markets allow users to bet on specific event outcomes, effectively creating a form of customizable, on-demand insurance. Key examples highlight this shift. In sports, Kalshi partnered with insurance broker Game Point Capital to provide NBA teams with more affordable options to hedge performance bonuses compared to traditional insurers. In real estate, Polymarket's collaboration with Parcl lets users speculate on city-specific housing price indices, allowing homeowners to hedge against price drops or buyers against price increases. Furthermore, businesses like a New York bar have used Kalshi to hedge marketing promotions (e.g., offering free drinks if a team wins), framing the transaction explicitly as placing a "hedge." The article argues prediction markets offer advantages over traditional insurance and even sports betting in transparency, liquidity, and flexibility. They provide a wider range of event coverage, act as neutral platforms rather than counterparties, and offer clearer pricing. The piece cites historical precedents like large "refund promotion" hedges by businesses using sportsbooks but notes prediction markets modernize the concept. However, challenges remain for widespread adoption as an insurance alternative, including limited liquidity in some markets, unclear regulatory status, and potential vulnerabilities in event resolution mechanisms. Despite these hurdles, prediction markets are positioning themselves as new tools for risk management, directly challenging certain segments of the conventional insurance landscape.

Odaily星球日报19 хв тому

Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the 'Barbarians at the Gate'?

Odaily星球日报19 хв тому

Торгівля

Спот
Ф'ючерси
活动图片