# Hedging Related Articles

Prediction Markets Under Bias

The article "Prediction Markets Under Bias" by Jeff Park of Bitwise argues against the common media portrayal of prediction markets as mere gambling or a social ill. It distinguishes between gambling and investment based on whether a participant's strategy has a positive expected value (+EV), not the market's structure. The author contends that prediction markets, like poker, are skill-based and can be a legitimate form of investment, offering individuals autonomy, truth discovery, and decentralized value. The piece critiques the common conflation of prediction markets with casino gambling, highlighting their role in risk hedging and capital efficiency, similar to insurance and securitization. A key differentiator is their precision and finite expiration, which makes prices directly anchor to factual outcomes, rewarding deep research and information advantage rather than punishing the uninformed. The author concludes that media opposition often stems from a structural bias, as these markets challenge traditional information gatekeepers and promote a more democratic, transparent system for pricing truth. The real debate is not about information having a price, but about who controls and profits from it.

marsbit04/20 12:08

Netflix Founder Goes to His Most Feared Place

Netflix co-founder Reed Hastings is stepping down as chairman after nearly 30 years with the company, despite Netflix reporting its strongest-ever quarterly results with a 16% revenue increase and 83% surge in net profit. Hastings, who studied AI in the 1980s, has joined the board and donated $50 million to study AI’s impact on society. He now views AI as the biggest threat to Netflix, fearing that AI-generated content could eventually make free platforms like YouTube more appealing than paid services. While Netflix is using AI to improve production efficiency, Hastings is hedging his bets by engaging directly with AI governance, concerned that the rapid pace of AI development could disrupt the entire content industry.

marsbit04/20 00:08

a16z New Article: Prediction Markets Entering the Fast-Forward Phase

Prediction markets are evolving from niche tools focused on elections and sports into a broader financial infrastructure for pricing real-world uncertainty. Key shifts include: application expansion beyond sports into entertainment, macro, and CPI markets; the creation of direct price benchmarks for events (e.g., tariffs, Fed decisions), enabling precise hedging without correlated asset risks; and growing institutional adoption, though still early-stage. While sports drive volume, long-tail markets show faster growth. Current institutional use is primarily for data, but progression toward system integration and active trading is expected. Regulatory advancements, like margin trading, are critical for scaling. The market is transitioning toward an essential, institutional-grade tool, similar to the evolution of options markets.

marsbit04/19 12:00

Institutional Adoption of Prediction Markets Stuck at the Third Stage

Prediction markets are transitioning from niche platforms focused on elections and sports to mainstream financial tools, as highlighted at Kalshi Research's inaugural conference. While sports still dominate trading volume (around 80%), non-sports categories like macroeconomics, politics, and entertainment are growing faster, signaling a shift from entertainment-based trading to information and risk management tools. Institutions, including Wall Street firms, are increasingly using prediction markets for data reference (Stage 1 adoption), with some progressing to system integration (Stage 2). However, full-scale trading (Stage 3) is limited due to the lack of margin trading, requiring full collateral for positions—a barrier for leverage-dependent entities. Kalshi is working with regulators to introduce margin mechanisms. Key insights from participants like Goldman Sachs and CNBC emphasize the value of real-time pricing for events (e.g., Fed decisions, tariffs), providing benchmarks previously unavailable. The path to maturity mirrors historical financial instruments like options, with expectations that prediction markets will become institutional staples within five years. Political leaders, including Trump and Schumer, now cite Kalshi odds, underscoring its growing influence. The platform rewards domain expertise over traditional finance backgrounds, attracting diverse participants from fields like music and poker. Ultimately, prediction markets are evolving into critical infrastructure for pricing uncertainty.

marsbit04/17 02:27

Institutional Adoption of Prediction Markets Stuck at the Third Stage

Prediction markets are transitioning from niche platforms focused on elections and sports to mainstream tools for information and risk management. While events like the Super Bowl still dominate trading volume, non-sports categories—such as macroeconomics, politics, and entertainment—are growing faster and attracting institutional interest. Key drivers include the need for real-time benchmarks on uncertain events (e.g., election outcomes or policy changes), which traditional markets lack. Institutions like Goldman Sachs and CNBC are already using prediction market data for decision-making, though most remain in the early stages of adoption. The path to full institutional integration involves three phases: data integration, system compliance, and active trading. The current barrier to large-scale trading is the lack of margin trading, requiring full collateral for positions—a limitation platforms like Kalshi are working to address. Experts compare prediction markets to the early days of options trading, anticipating they will become a standard, if mundane, financial infrastructure. Political leaders and institutions are increasingly referencing these markets, signaling their growing credibility. The future lies in their evolution from speculative arenas to essential tools for hedging and pricing uncertainty.

Odaily星球日报04/17 02:22

Data Research: How Big Is the Liquidity Gap Between Hyperliquid and CME Crude Oil?

This analysis compares the liquidity and market structure of Hyperliquid's xyz:CL perpetual contract with CME's CLJ6 WTI crude oil futures. Key findings reveal Hyperliquid's absolute liquidity metrics are significantly lower than CME's, with its depth being less than 1% of CME's across various price points. The median trade size on Hyperliquid ($543) is 166 times smaller than on CME ($90,450), reflecting its crypto-native retail user base. While Hyperliquid exhibits high execution slippage for large orders (20x higher for a $1M order), its weekend trading activity shows a notable trend. Over three observed weekends, Hyperliquid's volume grew from $31M to over $1B, and its price discovery capability improved, closely aligning with CME's Monday opening prices by the third weekend. The increasing average trade size on weekends suggests growing use by traders seeking hedging exposure outside CME hours, not just speculators. The primary obstacle for Hyperliquid's expansion among institutional investors remains its high transaction costs for large orders. Without improving its capacity for institutional-sized trades, it risks remaining a supplementary weekend venue rather than a primary marketplace.

marsbit04/06 02:51

Data Research: How Big Is the Liquidity Gap Between Hyperliquid and CME Crude Oil?

This analysis compares the liquidity and market structure of Hyperliquid's xyz:CL perpetual crude oil contract with CME's CLJ6 futures contract over a three-week period from late February to mid-March 2026. Key findings reveal a significant liquidity gap: Hyperliquid's average depth is less than 1% of CME's, with a 125x difference at the ±2 bps level. The median trade size on Hyperliquid ($543) is 166x smaller than on CME ($90,450), reflecting its crypto-native retail user base. For a $1M order, estimated slippage on Hyperliquid (15.4 bps) is approximately 20x higher than on CME (0.79 bps), indicating it currently lacks the capacity for institutional-sized orders. However, a notable trend emerged during weekends when CME is closed. Hyperliquid's weekend trading volume grew significantly over the three observed weekends, from $31M to over $1B, and the average trade size increased, suggesting use by traders seeking exposure or hedging ahead of Monday's open. While an initial "discovery boundary" mechanism limited price discovery on the first weekend, subsequent weekends showed Hyperliquid's price increasingly converged with CME's Monday opening price, demonstrating its evolving price discovery capabilities. The report concludes that while Hyperliquid's absolute liquidity metrics are not comparable to CME, its growing weekend activity shows promise. However, high transaction costs for large orders remain a major barrier to attracting institutional participants.

Odaily星球日报04/06 02:50

Altcoins or metals? Here’s how investors are rotating amid rising risk

Amid rising macroeconomic volatility and declining risk appetite, investors are rotating capital away from Bitcoin, which faces selling pressure, and into select altcoins. Technical indicators show Bitcoin dominance encountering resistance at 60%, while the Altcoin Season Index has risen, suggesting accelerating altcoin rotation. However, the Bitcoin Risk Index mirrors 2022 patterns, indicating that altcoins remain highly vulnerable to BTC’s instability. If negative altcoin impulse exceeds 25%, broader corrective pressure may spread. Concurrently, investors are hedging risk by moving into hard assets like gold and silver, which have gained significantly. This deliberate shift into metals may continue to limit altcoin upside until Bitcoin stabilizes and overall risk conditions improve.

ambcrypto03/28 15:02

BitMart Research Institute Weekly Highlights: A Comprehensive Market Analysis Amidst the Stalemate in the Middle East and Stagflation Expectations

marsbit03/24 11:29

Mapping Volatility Through Time: Implied Volatility Heatmaps

Glassnode introduces two new implied volatility (IV) heatmap metrics—IV Moneyness Heatmap and IV Delta Heatmap—that visualize how options markets price risk across strikes and maturities over time, rather than at a single point. These tools help identify volatility regimes, such as periods of calm, stress, and transitions between them. The heatmaps display IV using color gradients, with red indicating high volatility and blue indicating low volatility. Key patterns include elevated put-side IV during drawdowns (signaling hedging demand), tail-risk repricing in deep out-of-the-money options, and regime shifts that inform trading strategies like hedging in low-vol environments or selling premium in high-vol periods. Available in Glassnode Studio, these metrics provide a dynamic view of market expectations and risk sentiment.

insights.glassnode03/23 08:38