Why Bitcoin And Ethereum Saw A Spike In Correlation With Asian Equities

newsbtcОпубліковано о 2022-08-24Востаннє оновлено о 2022-08-24

Анотація

The International Monetary Fund (IMF) published a study on the spike in positive correlation with Bitcoin (BTC), Ethereum (ETH), and Asian equities. The financial organization claims digital assets began an accelerated integration with the region during the pandemic as more people traded them looking to generate yield.
From 2020 to its all-time high in 2021, the crypto total market cap increased by over 20-fold which led Bitcoin and Ethereum into price discovery. As seen in the chart below, the total trading volume for cryptocurrencies rose very close to $900 billion from below $100 billion at its peaked in 2021.
The regions with the highest trading volume are the Americas and Europe. The Middle East and Central Asia, EM Asia, and AE Asia are below other regions. However, the IMF claims adoption of cryptocurrencies in Asia could pose a systematic risk for the financial world.

Source: IMF If the price of Bitcoin and the crypto market reclaim their previous levels, and re-entered price discovery, the financial institution believes that there could be negative consequences. If digital assets were to rise and crash as they did over the past year, “contagion could spread through individual or institutional investors”.
As cryptocurrencies trend lower these investors would allegedly “rebalance their portfolios, possibly causing financial market volatility or even default on traditional liabilities”, the IMF said. In that sense, the financial institution shared the chart below to show the contrast between the price of Bitcoin and Asian stock indexes.

Source: IMF From 2020 until 2022, this correlation seems to be trending upward with Thailand and Vietnam showing the highest positive correlation. This has translated into similar price action for Bitcoin and traditional equities in these countries.

In India, the correlation between the price of Bitcoin and local equities has increased by 10-fold with a 3-fold spike in volatility correlations. The financial institution believes that if the price of Bitcoin decreases or increases, there could be “spillovers of risk sentiment”.
Can Bitcoin Lead The Asian Markets Into A Shock?
The financial institution suggests that these “spillovers” are already happening in Asia. Therefore, authorities in the region have been working on implementing a regulatory framework to allegedly mitigate risk.
The financial institution failed to mention that Bitcoin has been showing a positive correlation with the performance of major equities indexes across the world, the phenomenon is not limited to Asia. As seen below, the price of BTC has been moving in tandem with the Nasdaq 100 since the start of 2022.

BTC’s price and Nasdaq 100 moving in tandem. Source: TraderFromTheNorth on Tradingview The positive correlation has been attributed to current macroeconomic conditions. These indexes often move-in tandem with macroeconomic events, such as the one the market has experienced since 2020.
Therefore, the positive correlation between Bitcoin and Asia equities could also be attributed to the cryptocurrency reaching high adoption levels rather than a tale sign of potential financial risk.

Пов'язані матеріали

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.

marsbit39 хв тому

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

marsbit39 хв тому

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

Intel CEO Chen Liwu outlines a bold vision for the company's transformation in a podcast interview, targeting a "10x return in 5-10 years." He emphasizes a strategic shift beyond traditional process node scaling, focusing on advanced packaging (like EMIB), new materials (glass substrates, GaN, SiC, InP, synthetic diamond), and system-level integration. Key to this is Intel's foundry business, where he prioritizes building trust through superior yield, defect density, and cycle times. Chen sees strong CPU demand resurgence driven by agent AI and inference workloads. He details the Terafab collaboration with Elon Musk to address semiconductor infrastructure gaps and stresses the strategic importance of U.S.-based advanced manufacturing. Acknowledging Intel is still in the "crawl" phase of his crawl-walk-run framework, Chen believes the company's full potential—extending from its PC base into edge computing and physical AI—will become apparent by 2030-2032.

marsbit45 хв тому

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

marsbit45 хв тому

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.

marsbit46 хв тому

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

marsbit46 хв тому

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.

marsbit2 год тому

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

marsbit2 год тому

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.

marsbit3 год тому

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

marsbit3 год тому

Торгівля

Спот

Ф'ючерси