Do Prediction Markets Forecast Cryptocurrency Volatility? Evidence from Kalshi Macro Contracts

Battle Count: 166

Highly relevant for crypto volatility forecasting and risk management. The paper demonstrates that Kalshi prediction market signals carry information not embedded in conventional instruments (Fed Funds futures, Treasury yields, Deribit DVOL). Practical applications include volatility-managed portfolios (7% position weight adjustment at high-signal episodes) and options pricing (16% predicted vol reduction for ETH straddles post-CPI). However, the regime dependence of the monetary policy channel and modest OOS gains (2-4% MSFE improvement) limit immediate trading applicability. The CPI channel for altcoins is more stable and actionable.

Moderate complexity. The core methodology uses standard econometric techniques (HAR regression, Newey-West SEs, Clark-West test) that are well-documented. Data collection via Kalshi API and CoinGecko is straightforward. Key challenges include: volume-weighted aggregation of multiple contracts per series, handling irregular contract availability, proper lag alignment between Kalshi 4PM ET close and crypto midnight UTC close, and implementing the expanding-window OOS evaluation correctly. No machine learning or complex optimization required.

The paper uses publicly available data (Kalshi API, CoinGecko) and standard econometric methods (HAR, Newey-West, Clark-West). However, no GitHub repository or code is mentioned. The Kalshi API is public but contract availability changes over time. The sample period (Jan 2023 - Mar 2026) and specific contract series would need to be replicated. The methodology is well-documented with clear equations and model specifications.