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.
Summary- Researchers studied how to predict if cryptocurrency prices will go up or down using a special kind of market.
- They found two different ways to make predictions: one based on how the government controls money and another based on how prices for things we buy change.
- By looking at how the government changes interest rates, they could accurately guess what might happen to Bitcoin's price.
- Another signal called KXRECSSNBER helped predict the prices of other cryptocurrencies like Ethereum, Solana, Cardano, and Chainlink during economic downturns.
- The study also showed that by tracking changes in consumer prices, they could predict how Ethereum and Solana's prices might change.
Definitions
- Cryptocurrency: Digital money that uses cryptography for security.
- Volatility: How much the price of something goes up and down quickly.
- Monetary policy: Actions taken by a country's central bank to control the supply of money and interest rates.
- Inflation: When prices for goods and services increase over time.
- Altcoins: Alternative cryptocurrencies to Bitcoin.
Cryptocurrency volatility has been a topic of great interest and concern in recent years. With the rise of digital currencies, there is a growing need to understand and predict their volatility in order to make informed investment decisions. In their study titled "Do Prediction Markets Forecast Cryptocurrency Volatility? Evidence from Kalshi Macro Contracts," authors Hardhik Mohanty and Bhaskar Krishnamachari explore the use of prediction markets to forecast cryptocurrency volatility.
The study focuses on the daily probability changes in Kalshi macro prediction markets, which are used to predict future events such as economic indicators or political outcomes. The authors identify two distinct channels through which these prediction markets can accurately forecast cryptocurrency volatility: the monetary policy channel and the inflation channel.
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. This means that there is strong evidence that this signal is not due to chance and is indeed an accurate predictor of Bitcoin's volatility.
However, this channel also exhibits regime dependence linked to the 2024-2025 rate-cutting cycle, indicating that it may not be as reliable during certain periods. 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. This means that the predictions made by this channel are more accurate than those made by traditional methods, such as using historical data or market indicators.
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. This further strengthens the reliability of using prediction markets to forecast cryptocurrency volatility.
To ensure the accuracy and uniqueness of these signals, the authors also use orthogonalization techniques and baseline comparisons against conventional financial instruments such as Fed Funds futures, Treasury yields, and the Deribit implied volatility index. The results 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.
In conclusion, Mohanty and Krishnamachari's research paper sheds light on the potential of prediction markets in forecasting cryptocurrency volatility. By identifying two distinct channels - monetary policy channel and inflation channel - they demonstrate how these markets can accurately predict volatility for various cryptocurrencies. This has significant implications for investors looking to make informed decisions in this rapidly evolving digital currency landscape.
Daily probability changes in Kalshi macro prediction markets forecast cryptocurrency realized volatility through two distinct channels. The monetary policy channel, measured by Fed rate repricing on KXFED contracts, predicts Bitcoin volatility in sample with t = 3.63 and p < 0.001 but exhibits regime dependence tied to the 2024-2025 rate-cutting cycle. The recession risk signal from KXRECSSNBER proves more stable out of sample, delivering an MSFE ratio of 0.979 with Clark-West p = 0.020. The inflation channel, measured by CPI repricing on KXCPI contracts, predicts altcoin volatility for Ethereum, Solana, Cardano, and Chainlink with t-statistics ranging from -2.1 to -3.4 and out-of-sample gains for Ethereum at MSFE = 0.959 with p = 0.010 and Solana at p = 0.048. Both the Bitcoin--Fed-dovish and Chainlink--CPI specifications survive Benjamini-Hochberg correction at q = 0.05. Orthogonalization and baseline comparisons against Fed Funds futures, Treasury yields, and the Deribit implied volatility index confirm that these signals carry information not embedded in conventional financial instruments. The sample covers ten Kalshi event series and six cryptocurrency assets over January 2023 to March 2026.
