% ============================================================================= % Author: Simon-Pierre Boucher % Contact: contact@spboucher.ai % ============================================================================= % UQO Working Paper No. 7 % The Options-Implied Information Content for Cross-Asset Return % and Volatility Prediction: Evidence from 3.8 Billion Option Contracts % % Build: latexmk (see Makefile) — pdflatex + bibtex, references in % references.bib, one file per section under sections/. % ============================================================================= \documentclass[12pt,letterpaper]{article} \input{preamble} % ============================================================================ % METADATA % ============================================================================ \newcommand{\WPnumber}{7} \newcommand{\WPtitle}{The Options-Implied Information Content for Cross-Asset Return and Volatility Prediction: Evidence from 3.8~Billion Option Contracts} \newcommand{\WPsubtitle}{} \newcommand{\WPdate}{May 2026} \newcommand{\WPversion}{1.1} \newcommand{\WPabstract}{% This paper investigates the information content embedded in equity option markets for predicting returns and realized volatility across multiple asset classes and market regimes. Using 3.83~billion option contracts on 11,077 underlyings (2010--2025) merged with 11.5~billion intraday OHLCV observations across stocks, ETFs, indices, futures, FX, and cryptocurrencies, I address five research questions. Option-implied moments---particularly implied kurtosis, skewness, and put-call ratios---significantly predict 5-day stock returns; quintile long-short portfolios sorted on implied kurtosis deliver annualized Sharpe ratios of~2.33 ($t = 19.84$). Augmenting the HAR-RV model with implied volatility surface features improves 1-day realized variance forecasting by 23.3\% in~$R^2$ (0.359 to 0.442), an improvement robust across all subperiods including COVID-19 ($+19.4\%$). The ratio of implied to realized correlation among S\&P~500 constituents predicts market stress at 5, 10, and 20-day horizons ($t$-statistics: 3.91--8.30). Granger causality confirms that ATM implied volatility leads realized volatility in 100\% of individual tickers ($F=62.4$), and variance decomposition shows IV shocks explain 73.8\% of RV forecast error variance at the 20-day horizon. All findings survive Newey-West HAC (up to 22 lags), double-clustered standard errors, subperiod and leave-one-year-out analysis, VIX-regime conditioning, quantile regressions, winsorization sensitivity, rank-based information coefficients, decile sorts, and a within-ticker permutation placebo.% } \newcommand{\WPkeywords}{Option-implied information, realized volatility, HAR-RV, implied correlation, volatility surface, portfolio sorts, Granger causality, high-frequency data} \newcommand{\WPjel}{G12, G13, G14, G17, C53, C58} % --- Author --- \newcommand{\WPauthor}{Simon-Pierre Boucher} \newcommand{\WPaffiliation}{% D\'epartement des sciences administratives\\ Universit\'e du Qu\'ebec en Outaouais% } \newcommand{\WPemail}{simon-pierre.boucher@uqo.ca} \newcommand{\WPaddress}{% Gatineau -- Pavillon Alexandre-Tach\'e\\ 283, boulevard Alexandre-Tach\'e\\ Gatineau, Qu\'ebec, Canada J9A 1L8% } % ============================================================================ % DOCUMENT % ============================================================================ \begin{document} % --- Title Page --- \input{sections/titlepage} % --- Table of Contents --- \setcounter{page}{1} \tableofcontents \newpage % --- Main Body --- \input{sections/introduction} \input{sections/literature} \input{sections/data} \input{sections/methodology} \input{sections/results} \input{sections/robustness} \input{sections/discussion} \input{sections/conclusion} % --- References --- \newpage \bibliography{references} % --- Appendix --- \newpage \appendix \begin{appendices} \input{appendix/appendix} \end{appendices} \end{document}