# 📈 The Options-Implied Information Content for Cross-Asset Return and Volatility Prediction

UQO Working Paper No. 7 — Evidence from 3.8 Billion Option Contracts

TL;DR — A daily panel of option-implied features and 5-minute realized variances for 69 liquid US underlyings (49 large stocks, 17 ETFs, 3 indices; 264,383 ticker-days, 2010–2025) is built from 3.83 billion option records. Option-implied features predict weekly returns in a statistically detectable but economically marginal way (R² 0.3 % for stocks, 0.6 % ETFs, 1.0 % indices; only the 25δ skew survives two-way clustering; one strategy of fourteen is profitable after 10 bps, with t = 2.2). The IV surface raises the HAR-RV R² from 0.415 to 0.457 (1-day) and 0.421 to 0.515 (5-day) on a common sample, in every subperiod; out of sample the gain holds under MSE but not under QLIKE. The implied/realized correlation ratio predicts stress at 5–20 days (t 2.3–4.1); ATM IV Granger-causes RV in 100 % of tickers but explains 20–29 % of its 20-day FEVD. Price magnets rejected; tree-based ML does not beat HAR-RV.

⚠️ Version 1.2 (2026-10-17) corrects a target-alignment error in v1.1: the 5-day "forward" targets were trailing windows (r_{t−3}+…+r_{t+1}). The v1.1 headline results (weekly R² 5–19 %, kurtosis Sharpe 2.33, R²_OOS 0.99) are withdrawn. See docs/CHANGELOG-REVISION.md.

# 📌 Status

# 🎯 What this paper does

Five research questions on the information content of equity option markets, answered on a panel of 69 liquid underlyings:

# 🏆 Headline results

# 🗂 Repository layout

options-implied-cross-asset/
├── data/
│   ├── raw/                  # external DuckDB stores (not shipped — see data/raw/README.md)
│   └── processed/            # 5 derived parquets (unchanged; forward targets re-derived at load time)
├── src/wp7/                  # analysis library (config, data_io, econometrics, forecasting, portfolio)
├── scripts/                  # numbered entry points 01–14 (table below)
├── results/                  # CSV result tables (every paper table derives from these)
│   └── _pre-revision-20261017/   # v1.1 CSVs kept for audit
├── figures/                  # supplementary figures (the paper itself is tables-only)
├── docs/                     # REVIEW-REPORT.md, CHANGELOG-REVISION.md, AUDIT.md, CHANGES.md, verify/
├── Makefile                  # make pipeline | figures | paper | all
└── paper/                    # main.tex + preamble.tex + sections/ + tables/ (generated) + appendix/ + references.bib → main.pdf

# 🚀 Quick start

python3 -m pip install -r requirements.txt   # scripts bootstrap src/ themselves
make pipeline        # scripts 02–13 (raw-dependent steps skip themselves gracefully)
python3 scripts/14_make_paper_tables.py      # regenerate paper/tables/*.tex from results/
make figures         # supplementary figures from the result CSVs
cd paper && make     # latexmk → main.pdf   (latexmk -pdf main_anon.tex for the anonymous version)

Environment variables: WP7_RAW_DATA_DIR (raw DuckDB stores, default data/raw/), WP7_RESULTS_DIR (default results/), WP7_LEGACY_TARGETS=1 (reproduce the mis-aligned v1.1 targets, audit only), WP7_LEGACY_VIX=1 (use the 3,794-day VIX fallback instead of vix_daily.parquet), FDQ_BIN (path of the FinData OS fdq helper for the batch-N scripts).

# Revision 2026-11-04 (Stage D review): legacy mode, v2 outputs, new scripts

Every analysis script above keeps a --legacy switch that regenerates the committed CSVs of HEAD 3e1fc1c byte-for-byte (make test runs them all into _verify/results/, compares with docs/verify/compare_results.py and runs tests/test_inference.py); the default mode writes the revision outputs to new files (results/*_v2.csv or new names) and never overwrites a legacy CSV. The four raw-only CSVs (rq4_greeks_decay, rq5_model_comparison, rq5_feature_importance, descriptive_options_quality) are frozen outputs of commit c54a8e2 and carry code_version / status = frozen-unverified columns; the withdrawn 5-/22-day and degenerate OLS rows were purged from rq5_model_comparison.csv (full copy in results/_pre-revision-20261017/).

Environment used for the revision: Python 3.14.4 (macOS 27.0, Apple silicon, 18 cores), numpy 2.4.4, pandas 3.0.2, scipy 1.17.1, statsmodels 0.14.6, arch 8.0.0, scikit-learn 1.6.1; OMP_NUM_THREADS=OPENBLAS_NUM_THREADS=MKL_NUM_THREADS=VECLIB_MAXIMUM_THREADS=4, seeds fixed at 42 (bootstraps) and 0–9 (tree learners).

# 🗃 Data

Options chains with Greeks (3.83 B contract-days in the 2026-08 stores, 11,077 underlyings, 2010–2025) and 1-minute/5-minute OHLCV bars (1,761 M bars in the stores used; the "≈11,500" of version 1.2 was a typo — the Panel B rows sum to 1,761 M) licensed from FirstRate Data and served by the FinData OS lake (M3U96a; catalog of 2026-11-04: options.eod 4,232,091,733 rows, market_bars.stock.5m.raw 2,060,507,511, etf.5m.split 330,637,732, index.5m.raw 41,162,094). The five derived parquets in data/processed/ are versioned in git (analysis data, registry: data_gitignored = false) and are sufficient for every table except the frozen RQ4-A and RQ5 outputs; the original DuckDB stores were lost on 2026-08-05 (docs/AUDIT.md §6.1). Licence: private use, no redistribution of raw bars or option records. Of the lake's records, 275,448,124 enter the 69-name feature panel (results/data_records_used.csv, package N-H1).

Regenerable, not committed (gitignored): data/processed/vix_daily.parquet (scripts/19), correlation_divergence_v2.parquet (scripts/04), the batch-N parquets (scripts/26–29, 06 --lake), results/*.parquet, external_data/ff/ (Ken French daily FF5 and momentum factors, downloaded 2026-11-04T02:44Z, sha256 e58577223032aa45071a46686f7068900c371b516c9eb04e28e974c34264f84f and e56d5bc845544e2993e77353b12c611cfce3149ef754f9b59fc67904ddadd369). The analysis universe is a fixed, ex-post list of liquid names (survivorship bias acknowledged in the paper).

# 🔬 Methodology

1. Implied features (ATM IV 30/90 d, term slope, 25δ skew, smile-based skewness and kurtosis proxies, put–call ratios, net gamma) per ticker-day (src/wp7/data_io.py, scripts/01).
2. Forward targets: strictly forward sums Σ_{j=1..5} x_{t+j} (data_io.fix_forward_targets).
3. Return predictability: pooled OLS (HC1, NW, two-way clustering), Fama–MacBeth with NW, quintile/decile sorts with NW t-stats, calendar-time overlapping-cohort strategies with measured turnover and net-of-cost Sharpe ratios (econometrics.py, portfolio.py).
4. RV forecasting: HAR-RV (RV_t, RV^(w), RV^(m)) vs IV models on a common sample; rolling OOS (500/250), MSE, QLIKE, R²_OOS, DM with NW variance (forecasting.py).
5. Correlation divergence: equal-weight implied correlation (CBOE/Skintzi–Refenes analogue), 22-day realized correlation, LPM with NW(h).
6. Greeks decay & price magnets: by DTE bucket; max-OI attraction test.
7. Granger / VAR: per-ticker F-tests; bivariate VAR(5) with Cholesky-orthogonalized IRF/FEVD under both orderings.
8. Robustness: subperiods, VIX regimes, controls, quantiles, ticker R², winsorization, NW lags, Spearman ICs, deciles, leave-one-year-out, placebo.

# 🖼 Figures & tables inventory

The paper is tables-only: 27 table fragments generated by scripts/14_make_paper_tables.py into paper/tables/ (plus the frozen raw-data inventory table typed in sections/data.tex). Supplementary figures from scripts/12_make_figures.py are in figures/.

# ⚠️ Limitations

• Fixed ex-post universe of 69 liquid names (survivorship bias); results do not speak to small caps, where the literature locates most option-implied return predictability.
• "Implied skewness/kurtosis" are smile-based proxies, not model-free risk-neutral moments; results are sensitive to tail trimming.
• Long-short strategies are equal-weighted, not factor-adjusted; costs are proportional assumptions.
• RQ4-A (Greeks by DTE) and RQ5 (ML) are frozen outputs of the original run (raw stores unavailable); only their 1-day components are reported.
• RQ3 stress indicator is defined from the VIX, which is also a regressor; realized correlation available from Feb 2016 only.

# 📝 Citation

@techreport{boucher2026options,
  author      = {Boucher, Simon-Pierre},
  title       = {The Options-Implied Information Content for Cross-Asset Return and Volatility Prediction: Evidence from 3.8 Billion Option Contracts},
  institution = {Universit\'e du Qu\'ebec en Outaouais, D\'epartement des sciences administratives},
  type        = {Working Paper},
  number      = {7},
  year        = {2026},
  month       = {October},
  note        = {Version 1.2}
}

# 👤 Author & contact

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

# 🗒 Notes

• Revision 2026-10-17 (v1.2): full editorial-board review — docs/REVIEW-REPORT.md (diagnosis, methodology verdicts, literature, maturity) and docs/CHANGELOG-REVISION.md (every corrected number, scripts re-run, items not re-executable). Pre-revision state: git tag pre-revision-20261017, docs/main_pre-revision-20261017.pdf, results/_pre-revision-20261017/. Bibliography 45 → 97 entries, all verified on Crossref (docs/verify/crossref/).
• Reproduction audit of v1.0 (2026-08-05): docs/AUDIT.md, docs/CHANGES.md, evidence in docs/verify/ (v1.1 logs and CSVs).
• The May 2026 flat version is archived in Recherche-UQO/_archives/versions-mai-2026/options-implied-cross-asset--_old/; the original project is in …/options-implied-cross-asset--hf-wp7-20260519/.
• Harmonisation 2026-10-17: canonical title page, bibliography style aer, pinned requirements.txt.