Key Insights:
- Kalshi rate forecasts matched professional survey accuracy over roughly 150-day horizon.
- Prediction markets reacted quickly to inflation data and Federal Reserve communications.
- Kalshi provides full probability distributions unavailable in traditional macro forecasting tools.
The Federal Reserve Board’s Finance and Economics Discussion Series published a staff working paper that examined macroeconomic prediction markets on Kalshi, a CFTC-regulated platform. The paper said Kalshi markets can provide “a high-frequency, continuously updated, distributionally rich benchmark” that may help track macro expectations in real time.
The authors, Anthony M. Diercks, Jared Dean Katz, and Jonathan H. Wright, described the work as preliminary. The paper stated it “does not indicate concurrence” by the Board of Governors or other research staff.
Fed paper reviews Kalshi as an expectations tool
The paper reviewed Kalshi contracts tied to inflation, unemployment, GDP growth, payroll releases, recession odds, and Federal Reserve rate decisions. It compared Kalshi-based signals with survey forecasts and with tools that traders often use to read policy expectations.
The authors said surveys can lag fast-moving conditions. They also said many surveys provide point estimates with limited information about uncertainty. The paper described Kalshi markets as a source of probability distributions that update through the day.
Rate forecasts match survey performance in one test
For the federal funds target rate, the paper compared Kalshi’s pricing to the Federal Reserve Bank of New York’s Survey of Market Expectations. It reported that, over an average horizon near 150 days, Kalshi’s average absolute error was close to the error from professional forecasters.
The paper also compared Kalshi with fed funds futures and other rate measures. It said futures can be harder to translate into meeting-specific distributions, while Kalshi contracts can be tied to outcomes at specific meetings.
Moves after data and policy communication
The authors tracked how Kalshi probabilities changed around major releases and official communication. They described fast shifts after inflation data, labor market updates, and policy messaging.
The paper also looked beyond the expected rate path. It examined changes in uncertainty and asymmetry in the distribution. It reported that variance often declined after major data, while the mean response could differ depending on whether the surprise was positive or negative.
Contract design and market access
The paper described Kalshi contracts as event contracts that pay $1 if a defined outcome occurs. It said this structure allows a set of contracts to be used to build a full risk-neutral probability distribution for a variable.
Kalshi began operating in 2021 and is regulated as a “Designated Contract Market,” the paper said. The authors wrote that retail users can access some Kalshi contracts through brokerage platforms, and that market makers support trading in key markets.
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