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

**Authors:** Hardhik Mohanty, Bhaskar Krishnamachari  
**arXiv:** [2604.01431](https://arxiv.org/abs/2604.01431)  
**Date:** 2026-04-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2604.01431v1/

## Key points

- Study by Hardhik Mohanty and Bhaskar Krishnamachari on using Kalshi macro prediction markets to forecast cryptocurrency volatility
- Two distinct channels identified for forecasting: monetary policy channel and inflation channel
- Monetary policy channel accurately predicts Bitcoin volatility based on Fed rate repricing with t-value of 3.63 and p-value < 0.001, but exhibits regime dependence related to rate-cutting cycle
- Recession risk signal from KXRECSSNBER provides stable out-of-sample predictions for altcoins like Ethereum, Solana, Cardano, and Chainlink
- Inflation channel successfully predicts altcoin volatility for Ethereum and Solana based on CPI repricing with t-statistics ranging from -2.1 to -3.4
- Bitcoin--Fed-dovish and Chainlink--CPI specifications survive Benjamini-Hochberg correction at q = 0.05, showing robustness in predicting cryptocurrency volatility through these channels
- Orthogonalization techniques used to confirm unique information carried by signals not captured by traditional market indicators
- Study covers ten Kalshi event series and six cryptocurrency assets from January 2023 to March 2026, offering valuable insights into utilizing prediction markets for accurate cryptocurrency volatility forecasts

## Summary

In their study titled "Do Prediction Markets Forecast Cryptocurrency Volatility? Evidence from Kalshi Macro Contracts," authors Hardhik Mohanty and Bhaskar Krishnamachari explore the daily probability changes in Kalshi macro prediction markets to forecast cryptocurrency realized volatility. The research identifies two distinct channels through which this forecasting occurs. The first channel, known as the monetary policy channel, is measured by Fed rate repricing on KXFED contracts. This channel accurately predicts Bitcoin volatility within the sample with a t-value of 3.63 and p-value less than 0.001. However, it also exhibits regime dependence linked to the 2024-2025 rate-cutting cycle. On the other hand, the recession risk signal from KXRECSSNBER proves to be more stable out of sample, delivering an MSFE ratio of 0.979 with a Clark-West p-value of 0.020. The second channel identified in the study is the inflation channel, measured by CPI repricing on KXCPI contracts. This channel successfully predicts altcoin volatility for Ethereum, Solana, Cardano, and Chainlink with t-statistics ranging from -2.1 to -3.4. Out-of-sample gains are observed for Ethereum at MSFE = 0.959 with a p-value of 0.010 and for Solana at a p-value of 0.048. Both the Bitcoin--Fed-dovish and Chainlink--CPI specifications survive Benjamini-Hochberg correction at q = 0.05, indicating their robustness in predicting cryptocurrency volatility through these channels. Furthermore, orthogonalization techniques and baseline comparisons against conventional financial instruments such as Fed Funds futures, Treasury yields, and the Deribit implied volatility index confirm that these signals carry unique information not captured by traditional market indicators. The study covers ten Kalshi event series and six cryptocurrency assets over the period from January 2023 to March 2026, providing valuable insights into how prediction markets can be utilized to forecast cryptocurrency volatility with accuracy and reliability.