Опционы на биткоин-ETF должны выпустить в США в 2024 году

investing.ruОпубліковано о 2024-08-12Востаннє оновлено о 2024-08-12

Happycoin.club - Специалисты издания Bloomberg прогнозируют, что опционы на спотовые биткоин-ETF выпустят в США до конца 2024 года.

8 августа биржа Cboe подала Комиссии по ценным бумагам и биржам (SEC) США исправленную заявку на создание опционов на BTC-ETF, в которой указаны методы решения проблем, связанных с рыночными манипуляциями. Например, компания предлагает ввести лимит на предельно допустимую сумму средств, вложенных в один контракт. По мнению аналитика Bloomberg Джеймса Сейффарта, внесение поправок в документ свидетельствует о том, что чиновники дали работникам Cboe некие рекомендации, и лёд, наконец, тронулся.

Как полагают сотрудники Bloomberg, опционы на биткоин-ETF выпустят перед президентскими выборами в США, намеченными на 5 ноября. Спустя некоторое время SEC должна дать добро на запуск торговли опционами на Ethereum-ETF, разработанными биржей Nasdaq.

Согласно результатам исследования общественного мнения, проведённого редакцией The Journal of Financial Planning, свыше 10% финансовых советников рекомендовали своим клиентам, вложившим в ETF около $4,5 трлн, инвестировать в опционы для защиты от больших убытков в случае резкого падения курса активов. Поэтому благодаря опционам повысится спрос на биткоин- и Ethereum-ETF, что приведёт к росту уровня капитализации этих деривативов и положительно повлияет на стоимость соответствующих криптовалют.

Читайте оригинальную статью на сайте Happycoin.club

Пов'язані матеріали

Embodied Intelligence 'Gaokao' is Insanely Hard, Humans Score 100, Best Model Only 12.8

Embodied AI Faces a Daunting "Everest": New Benchmark Reveals Huge Gap Between Models and Humans A comprehensive new benchmark for robotic manipulation, RoboDojo, has been released, painting a stark picture of the current state of embodied AI. It serves as a unified evaluation platform covering both simulation and real-world robot tasks. The benchmark assesses five core capabilities: Generalization (adapting to new scenes/objects), Memory, Precision manipulation, Long-Horizon multi-step tasks, and Open semantic understanding. It includes 42 simulation tasks and 18 standardized real-world tasks across three dual-arm robot platforms. The results are sobering. In simulation, the best-performing generalist robot policy achieved an average success rate of only 8.80%. Performance in the real world was slightly higher but still low, with the top model succeeding 12.8% of the time on average. In stark contrast, human experts scored 76.03% in simulation and 100% in real-world tests. The benchmark highlights significant, uneven gaps in current models' abilities. While some excel in specific areas like visual recognition or simple actions, they struggle with reliability, especially in long-horizon tasks where errors accumulate and in open-ended semantic instructions. The low scores, particularly in real-world deployment with physical uncertainties like camera noise and contact dynamics, underscore that today's models are far from being robust, general-purpose operational robots. RoboDojo is more than just a ranking; it's an infrastructure designed for fair, reproducible comparison. Its companion system, XPolicyLab, standardizes the interface for different models to be evaluated. Maintained by an academic consortium without commercial ties, it aims to provide a community-wide "altitude meter" to track genuine progress toward reliable and generalizable robot manipulation.

marsbit1 год тому

Embodied Intelligence 'Gaokao' is Insanely Hard, Humans Score 100, Best Model Only 12.8

marsbit1 год тому

Zuckerberg's 'Mango' Image Generation Model Trails Only GPT Image 2, It Learned to Revise Prompts on Its Own

Meta's MSL has launched Muse Image, an advanced image generation model nicknamed "Mango," which ranks second globally in text-to-image benchmarks, closely trailing OpenAI's GPT Image 2. Its key innovation is agent-like behavior: it searches for factual information, writes code for charts, and, most notably, has developed self-correction abilities through reinforcement learning, allowing it to revise its own outputs without explicit programming. This shift emphasizes reasoning over immediate generation. Integrated with Meta's ecosystem, Mango connects with the Muse Spark language model for complex tasks and features a unique "@" function that can incorporate public Instagram photos into generated images—raising privacy concerns as it's enabled by default. The model is directly accessible in Meta AI, Instagram, and WhatsApp, leveraging Meta's vast user base for distribution rather than competing solely on image quality. Accompanying Mango is the preview of Muse Video, a video generation model with integrated audio, currently ranked third in its category. All Mango-generated images include an invisible, persistent watermark (Content Seal) for AI identification, alongside a public detection tool. While Mango advances "thinking" image models, its use of social data poses new ethical questions about consent and digital boundaries.

marsbit2 год тому

Zuckerberg's 'Mango' Image Generation Model Trails Only GPT Image 2, It Learned to Revise Prompts on Its Own

marsbit2 год тому

Торгівля

Спот
活动图片