Kalshi (Predictive Market) and the Rise of Macro Markets - An Article by the Federal Reserve Yesterday I saw the CFTC talking about the prediction market, but I didn't pay much attention to it. After all, the CFTC is the superior unit of the prediction market. However, when I was reading the minutes of the Federal Reserve meeting in the early morning, I suddenly saw the Fed's comments on Kalshi and the prediction market, which made me very interested. Firstly, the Federal Reserve believes that predicting the market is becoming a new macro expectation measurement tool, compressing participants' judgments into prices using real funds, and being able to achieve high-frequency, real-time, and continuous updates, which is very scarce in the traditional macro expectation framework. More importantly, the Federal Reserve emphasizes that the value of macro forecasting markets like Kalshi lies not in the addition of a point forecast, but in its ability to provide distribution forecasts. Users not only know whether the market is betting on CPI at 3.1% or 3.2%, but also can see the probability weights for each range of 3.0-3.1, 3.1-3.2, and 3.2-3.3, and see how tail risk is priced. For policy makers, distribution is more important than points because the essence of policy is to manage tails and uncertainty. PS: This statement from the Federal Reserve is very important, even representing that the Fed's policy makers will look at the distribution in the forecast market to determine tail risk. Secondly, the Federal Reserve believes that one of Kalshi's killer advantages is the ability to turn "how expectations are rewritten by news" into observable intraday data. The biggest problem with surveys is the low frequency, often only seeing the "results after the last meeting". However, Kalshi allows users to directly see a statement from an official, changes in the market's probability of interest rate cuts at the next meeting, how an employment report re prices the market, and even how expectations fluctuate repeatedly and eventually converge on the same day. This is very useful for understanding the transmission chain of "communication expectations asset prices". Thirdly, the Federal Reserve believes that Kalshi's forecasting accuracy is not poor, and even comparable to traditional tools in some dimensions, and even better in some indicators. Especially for the prediction of the Federal Reserve's interest rate path, Kalshi's error performance is very close to professional forecasts, with little difference in the error of core CPI, unemployment rate, and Bloomberg's consistent expectations. For overall inflation forecasts, Kalshi's performance is actually better. To put it simply, the Federal Reserve believes that predicting the market is not about sentiment trading, but rather about approaching a reliable source of macro expectation data in terms of availability. Fourthly, the Federal Reserve emphasizes that Kalshi enables researchers and policy makers to systematically study for the first time how macroeconomic data affects the shape of policy interest rate distribution. For example, after inflation data is released, uncertainty (distribution variance) usually decreases, but the impact of inflation's "positive surprises" and "negative surprises" on the mean interest rate is not symmetrical. Inflation exceeding expectations often pushes the mean interest rate even harder, while "dovish positives" with inflation below expectations are not pulled back so symmetrically. To put it simply, the market is more sensitive to the pricing of 'bad inflation' and more stingy in rewarding 'good inflation'. But at the same time, the Federal Reserve also reminds that predicting market prices is a risk neutral probability, not a purely true probability. Traders have risk appetite and risk premium, and the participant structure is biased towards retail investors, which may lead to systemic bias. The liquidity of the tail contract is weak, and the probability of extreme outcomes may result in outdated quotes. So predictive tools such as Kalshi are more suitable as windows for real-time emotion and risk pricing, rather than the only truth. Fifth, the Federal Reserve's positioning of Kalshi is to upgrade macro expectations from low-frequency point predictions to high-frequency distribution predictions. By separating what the market believes from what it fears, macro narratives will be closer to real financial behavior, rather than staying at the level of rhetoric and emotions. So overall, the significance of predicting the market is not to tell us if something will happen, but to tell us how much probability the market is willing to pay for it to happen in the face of real money. For policy makers, the real determinant of policy difficulty is often the small tail probability. @bitget VIP， Lower rates and more generous benefits