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% Author: Simon-Pierre Boucher
% Contact: contact@spboucher.ai
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\section{Introduction}\label{sec:intro}

Options markets aggregate the expectations of heterogeneous participants regarding future price movements, volatility, and tail risks. Since the seminal work of \citet{black1973pricing}, financial economists have recognized that derivative prices embed forward-looking information that is not fully reflected in the underlying asset's spot price. The implied volatility surface---spanning the dimensions of moneyness and time-to-expiration---constitutes a rich, high-dimensional representation of market expectations about the full distribution of future returns. Whether, and how, this information can be exploited for return prediction and volatility forecasting remains a central question in empirical finance.

This paper addresses that question with a dataset of unprecedented scale: 3.83~billion individual option contract records spanning 11,077 underlying tickers over 4,025 trading days (January 2010--December 2025), merged with 11.5~billion intraday OHLCV observations at the 1-minute and 5-minute frequencies across six asset classes. This infrastructure supports five interconnected research questions:

\begin{enumerate}[label=\textbf{RQ\arabic*.}]
    \item Do option-implied moments (variance, skewness, kurtosis) extracted from the strike--expiry surface predict next-day and next-week cross-sectional stock returns? Does this predictability extend to sector ETFs and equity indices?
    \item Can the implied volatility term structure and volatility smile jointly forecast realized volatility more accurately than standard time-series models (GARCH, HAR-RV)?
    \item Does the divergence between option-implied and realized correlations predict future market stress events?
    \item How does the information content of aggregated options Greeks decay as a function of days-to-expiry? Does open-interest concentration at specific strikes create predictable intraday price magnets?
    \item Can machine-learning models trained on the full daily options surface outperform the VIX in forecasting the 5-minute realized variance of the S\&P~500?
\end{enumerate}

The study advances the existing literature along four dimensions. First, whereas most prior work examines individual stocks or a single index, I provide evidence spanning 69 underlyings across three asset groups (individual stocks, sector ETFs, and broad market indices), documenting a systematic cross-asset gradient in predictability. Second, I assess the \textit{economic significance} of option-implied predictability through portfolio sorts, constructing long-short quintile portfolios and evaluating risk-adjusted performance under realistic transaction costs. Third, I provide the most comprehensive set of robustness checks in this literature: Newey-West HAC standard errors at multiple lag lengths \citep{newey1987simple}, double-clustered standard errors \citep{cameron2011robust,petersen2009estimating}, Fama-MacBeth regressions \citep{fama1973risk}, subperiod and leave-one-year-out analysis, VIX-regime conditioning, quantile regressions, winsorization sensitivity, nonparametric rank-based information coefficients, decile sorts, ticker-by-ticker $R^2$ distributions, and a within-ticker permutation placebo test. Fourth, I employ Granger causality tests and bivariate VAR models to establish the direction of information flow between option-implied and realized measures, complemented by impulse response functions and forecast error variance decomposition.

The 16-year sample encompasses the post-GFC recovery (2010--2012), the extended bull market (2013--2016), the low-volatility era of 2017, the Volmageddon of February 2018, the COVID-19 crash of March 2020, the meme-stock episode of January 2021, the 2022 rate-hiking cycle, the SVB crisis of March 2023, and the August 2024 VIX spike---a sequence of natural experiments for regime-dependent analysis.

The remainder of the paper is organized as follows. Section~\ref{sec:lit} reviews the literature. Section~\ref{sec:data} describes the data and variable construction. Section~\ref{sec:method} presents the methodology. Section~\ref{sec:results} reports the results. Section~\ref{sec:robust} presents robustness tests. Section~\ref{sec:discuss} discusses the findings, and Section~\ref{sec:conclude} concludes.
