# 🎯 Prediction Markets as Arrow–Debreu Securities: Evidence from Polymarket

UQO Working Paper No. 8 — Price Discovery, Market Completeness, and Information Aggregation in Blockchain-Based State-Contingent Claims

TL;DR — Polymarket binary contracts are modelled as Arrow–Debreu state-contingent claims. On the Fed June 2026 rate-decision neg-risk group (five mutually exclusive outcomes) and the 2024 U.S. Presidential Election markets, state prices sum to 1.02 on average (146 obs., mean deviation 0.025) with no persistent arbitrage; prediction-market prices move 15–45 minutes ahead of Treasury futures around FOMC and CPI releases; and Roll spreads / Amihud ratios of high-profile contracts rival those of established markets. Decentralized prediction markets behave as effective Arrow–Debreu economies.

# 📌 Status

# 🎯 What this paper does

The paper asks whether blockchain prediction markets satisfy the textbook properties of complete Arrow–Debreu markets: (i) completeness and no-arbitrage — do state prices of mutually exclusive outcomes sum to the discount factor and stay positive? (ii) price discovery — do state prices incorporate macro news before conventional markets? (iii) microstructure quality — are these contracts liquid enough to be used as state-contingent claims?

Σ_s π_s = 1/(1+r_f)   (completeness)      π_s > 0 ∀s   (no arbitrage)

Tests combine completeness and arbitrage-violation statistics, risk-neutral densities and moments, event studies around FOMC/CPI announcements, Hasbrouck information shares, VAR/Granger causality between Polymarket and Treasury futures, and GARCH/HAR-RV volatility models.

# 🏆 Headline results

# 🗂 Repository layout

polymarket-arrow-debreu/
├── data/
│   ├── raw/                  # Polymarket / FRED / Yahoo snapshots (see data/raw/README.md) — not committed
│   └── processed/            # cleaned parquet panels (committed)
├── src/
│   ├── config.py             # paths, API endpoints, market registry, event dates
│   ├── api/                  # polymarket.py, fred.py, yahoo.py, runner.py
│   ├── cleaning/             # polymarket_cleaner, macro_cleaner, yahoo_cleaner
│   ├── features/             # engineering.py, event_windows.py
│   ├── models/               # arrow_debreu, event_study, price_discovery, liquidity, volatility
│   └── plots/                # figures.py, tables.py, style.py
├── scripts/
│   └── run_analysis.py       # master pipeline: data → cleaning → models → figures/tables
├── results/tables/           # table1…table8 (.tex fragments input by the paper)
├── figures/                  # fig1…fig12 (PDF/PNG)
└── paper/                    # main.tex + sections/ + appendix/ + references.bib → main.pdf

# 🚀 Quick start

python3 -m pip install -e .            # dependencies from pyproject.toml
cp .env.example .env                   # set FRED_API_KEY
python3 scripts/run_analysis.py        # --skip-data / --skip-models / --skip-paper
cd paper && make                       # latexmk → main.pdf

# 🗃 Data

Minute-level price histories, order books and metadata for the Fed June 2026 rate-decision markets (event 101772, 5 outcomes), four closed Fed markets and the 2024 Presidential Election markets (Trump / Harris) from the Polymarket Gamma and CLOB APIs; fed funds rate, CPI and unemployment from FRED; Treasury futures and benchmarks from Yahoo Finance. Snapshot 2026-05-22. See data/raw/README.md.

# 🔬 Methodology

1. Completeness & no-arbitrage — sum of state prices, positivity, implied discount factor, arbitrage-violation detection (src/models/arrow_debreu.py).
2. Risk-neutral density & moments extracted from the neg-risk group.
3. Event studies around FOMC and CPI releases with cumulative abnormal state-price responses (event_study.py).
4. Price discovery — Hasbrouck information shares, VAR and Granger causality vs Treasury futures (price_discovery.py).
5. Liquidity — Roll spreads, Amihud illiquidity (liquidity.py).
6. Volatility — realized measures, GARCH and HAR-RV (volatility.py).

# 🖼 Figures & tables inventory

Tables (results/tables/): descriptive statistics, summary statistics, completeness tests, event study, Granger, VAR, GARCH, HAR-RV.

# ⚠️ Limitations

• Two event families (one Fed neg-risk group, one election) — a small set of high-profile markets.
• Polymarket order-book snapshots are point-in-time; depth over the full sample is not observed.
• Price-discovery leads are measured at the minute frequency on days with scheduled announcements only.

# 📝 Citation

@techreport{boucher2026polymarket,
  author      = {Boucher, Simon-Pierre},
  title       = {Prediction Markets as Arrow--Debreu Securities: Evidence from Polymarket},
  institution = {Universit\'e du Qu\'ebec en Outaouais, D\'epartement des sciences administratives},
  type        = {Working Paper},
  number      = {8},
  year        = {2026},
  month       = {May}
}

# 👤 Author & contact

Simon-Pierre Boucher — Département des sciences administratives, Université du Québec en Outaouais (UQO), Gatineau, QC. 📧 simon-pierre.boucher@uqo.ca

# 🗒 Notes

• Harmonisation 2026-10-17: tables/ moved to results/tables/ (paths updated in src/config.py and the paper), run_analysis.py moved to scripts/, empty logs//notebooks/ removed; fig5_event_study_cars is included as PNG (the PDF export was missing).