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% Author: Simon-Pierre Boucher
% Contact: contact@spboucher.ai
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% Literature Review
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\section{Literature Review}\label{sec:lit}

\subsection{Information Content of Option Prices}

The notion that options markets convey informational advantages relative to the equity market dates back to \citet{black1975fact} and \citet{manaster1982option}. \citet{easley1998option} develop a sequential-trade model in which option volume signals the presence of informed traders; \citet{pan2006information} extend this framework and find that the informational role is concentrated in out-of-the-money puts. \citet{johnson2012option} show that the option-to-stock volume ratio predicts returns more strongly when short-sale constraints bind, and \citet{ge2016informed} use order-level data to confirm that non-market-maker option trades carry predictive power for future stock prices. \citet{hu2014does} documents that the information content of option trades has increased over time.

\subsection{Option-Implied Moments and Return Predictability}

\citet{xing2010does} demonstrate that the steepness of the volatility smirk predicts individual stock returns. \citet{cremers2010deviations} show that deviations from put-call parity contain return-predictive information. \citet{an2014joint} analyze the joint cross section of stocks and options and find incremental predictive power in implied volatility and skewness. \citet{bali2019option} construct comprehensive implied-moment measures and document significant cross-sectional predictability at the weekly horizon. \citet{stilger2017puzzle} examine the implied volatility spread (call IV minus put IV) and document a strong positive relationship with future returns. \citet{cao2005informational} provide evidence that the options market leads the equity market in price discovery.

\subsection{Implied Volatility and Realized Volatility Forecasting}

\citet{christensen1998relation} establish that implied volatility is a biased but efficient forecast of realized volatility, a result \citet{blair2001forecasting} confirm using the VIX. The HAR-RV model of \citet{corsi2009simple} has become the benchmark for realized volatility forecasting, and \citet{busch2011role} augment it with implied volatility. \citet{bekaert2014asymmetric} decompose the VIX into expected volatility and variance-risk-premium components, while \citet{bollerslev2009expected} show that the variance risk premium predicts equity returns. \citet{carr2009variance} provide the theoretical foundation for model-free implied variance. \citet{patton2015good} propose the semivariance-based asymmetric HAR-RV, and \citet{hansen2012realized} develop the Realized GARCH model.

\subsection{Implied Correlation and Systemic Risk}

\citet{driessen2009price} formalize the correlation risk premium and show that it is positive. \citet{buss2012more} demonstrate that implied correlation spikes during market stress. \citet{kelly2014tail} develop tail-risk measures from out-of-the-money puts, and \citet{acharya2017measuring} propose systemic-risk measures related to the correlation approach adopted here.

\subsection{Market Microstructure of Options and Greeks}

\citet{ni2009does} document that high open interest at specific strikes affects underlying prices, and \citet{avellaneda2003weighted} study expiration-day pinning. \citet{barbon2022option} show that aggregate dealer gamma positions predict returns and volatility.

\subsection{Machine Learning in Volatility Forecasting}

\citet{bucci2020realized} compare neural networks with traditional volatility models. \citet{christensen2023machine} find that machine-learning methods offer modest improvements that often fail out-of-sample, whereas \citet{gu2020empirical} show that tree-based methods perform well for cross-sectional return prediction.