spb/wp8_uqo

1% ============================================================================2% Section 1: Introduction3% ============================================================================4\section{Introduction}\label{sec:introduction}56A prediction-market contract that pays one dollar if the Federal Open Market Committee (FOMC) leaves its target range unchanged at a given meeting, and nothing otherwise, is the textbook example of a state-contingent claim. When a platform lists one such contract for every possible decision of that meeting, and when its settlement rules guarantee that exactly one of them pays, the set of contracts spans the outcome space of the decision in the sense of \citet{arrow1964role} and \citet{debreu1959theory}. Blockchain-based prediction markets have made this construction routine: Polymarket, the largest venue by volume, groups mutually exclusive contracts into ``negative-risk'' events whose collateral mechanics enforce that the complete set of claims is worth exactly one dollar at resolution. The resulting prices are therefore observable analogues of Arrow--Debreu state prices for a small number of macroeconomic and political state spaces, traded continuously, with a public order book and public settlement.78This paper asks what can and cannot be learned from such prices. The question has two parts. The first is a question about pricing. In an Arrow--Debreu economy the price of the complete set of state claims equals the price of a riskless bond, and each state price is the product of a discount factor and a risk-neutral probability. Because the spanning property is built into the contract design, the empirically testable content of the framework is not completeness itself but the pricing restrictions it imposes: the law of one price for the complete set, the no-arbitrage bounds implied by the mechanics of minting, converting and redeeming claims, and the interpretation of normalised prices as a risk-neutral distribution rather than as physical probabilities \citep{manski2006interpreting,wolfers2006interpreting}. The second is a question about information. If normalised state prices form a risk-neutral distribution over FOMC decisions, their first moment is a market-implied expected policy change comparable, in spirit, to the federal funds futures-based measures used since \citet{kuttner2001monetary}. One can then ask whether this quantity moves on scheduled macroeconomic releases, whether weekend trading---a feature absent from the futures market---carries information, and whether its daily changes lead or lag the Treasury, equity and volatility markets.910We address both parts with daily data from two Polymarket negative-risk groups: the five-outcome market on the June 2026 FOMC decision (11 December 2025 to 22 May 2026, 146 daily closes, about \$38 million of cumulative volume) and the seventeen-contract market on the winner of the 2024 U.S.\ presidential election (4 January to 5 November 2024, 304 daily closes, about \$3.7 billion of volume). The Fed group is the primary laboratory because its state space is small, exhaustive and economically interpretable; the election group serves to check whether the pricing regularities hold in a much larger and more heavily traded state space.1112Three sets of results emerge. First, the law of one price holds for the complete set of claims up to a small and economically interpretable overround. The sum of the five Fed state prices averages 1.017 (standard deviation 0.026) and the sum of the seventeen election prices averages 1.015 (standard deviation 0.031). Both overrounds are statistically different from zero, but they are of the order of the quoted spreads, decline steadily as resolution approaches---from about 5 percent in the first month of the Fed market to 0.4 percent in the last---and revert within two days. At the order-book snapshot taken with the data, the sum of the best bids across the five Fed contracts is 0.999 and the sum of the best asks is 1.004, so that no round trip through the complete set was profitable at the quotes. We also show that the overround has the wrong sign to be explained by the time value of money: a complete set bought and held to resolution should trade \emph{below} one by the discount factor, which averaged 0.989 over the Fed sample, so that the executable bounds are asymmetric, and the data sit at or slightly above the upper bound rather than at the lower one. Second, the risk-neutral expected policy change implied by the Fed group moves significantly more on scheduled release days than on other days. Its absolute daily change is 1.15 basis points on the thirteen release days in the sample against 0.51 basis points on the other 132 days (variance ratio 4.9, Brown--Forsythe $p=0.008$), and the ``No change'' state price moves 5.5 against 1.4 percentage points (variance ratio 14.2). The effect is concentrated on FOMC days and employment-report days; the four CPI releases in the sample produced no detectable excess movement. Weekend days, when the futures market is closed, carry about one third of the variance of weekdays. Third, once Polymarket closes are aligned to the U.S.\ trading calendar and the lag order of the vector autoregression is chosen by information criteria rather than by searching over lags, there is no evidence that daily changes in the Polymarket-implied policy expectation lead or lag daily changes in the ten-year Treasury yield, the S\&P~500, the VIX or Bitcoin. The series are contemporaneously correlated with the Treasury yield (correlation 0.26), consistent with both responding to the same macroeconomic news within the day, but neither predicts the other.1314The paper makes three contributions. The first is conceptual. The prediction-market literature has long debated whether prices are probabilities \citep{manski2006interpreting,wolfers2006interpreting,snowberg2013prediction,ottaviani2015price}; we show that for negative-risk groups the question can be separated into a pricing restriction that is testable (the complete set is worth one dollar within executable bounds) and an interpretation that is not (normalised prices are risk-neutral, not physical, probabilities), and we derive the bounds from the actual settlement mechanics rather than from a generic ``transaction cost'' argument. The second is empirical. To our knowledge this is the first study to document the term structure of the overround in a blockchain prediction market, its relation to quoted depth, and the announcement-day behaviour of a market-implied FOMC expectation extracted from binary contracts. The third is methodological and cautionary. Polymarket's daily price history is stamped at 00:00 UTC of the following day, so that a naive merge with U.S.\ closing prices misaligns the two calendars by one day in one direction only; and a lag-search procedure that selects the lag with the smallest $p$-value produces spurious ``Granger causality'' in short samples. An earlier version of this paper reported both artefacts as findings; we document them in Appendix~\ref{app:reconciliation} because they are likely to affect other work that merges blockchain and conventional market data.1516The results also delimit what prediction markets can contribute to the measurement of policy expectations. The Polymarket June 2026 market traded through four FOMC meetings, five CPI releases and five employment reports, and its implied expected policy change moved from $-15.5$ basis points to zero over the sample. The two largest daily moves---a fall of 11.5 percentage points in the ``No change'' price on 28 January 2026 and a rise of 23 percentage points on 18 March 2026---both occurred on FOMC days. These are the responses one would expect from an instrument that prices the policy path; they are not evidence of informational superiority over the futures market, and the Granger tests give none. The appropriate reading is that Polymarket offers a continuously traded, publicly settled complement to federal funds futures with a transparent and small transaction-cost wedge, not a substitute with a lead.1718The remainder of the paper proceeds as follows. Section~\ref{sec:literature} describes the institutional setting and the related literature. Section~\ref{sec:theoretical} develops the framework and states the hypotheses. Section~\ref{sec:data} describes the data and the alignment of calendars. Section~\ref{sec:methodology} presents the methods. Section~\ref{sec:results} reports the results for the Fed market. Section~\ref{sec:robustness} extends the pricing analysis to the election market and reports further checks. Section~\ref{sec:conclusion} discusses limitations and concludes. Appendix~\ref{app:volatility} reports the volatility models, Appendix~\ref{app:variables} defines the variables, and Appendix~\ref{app:reconciliation} reconciles the present results with the earlier version.19