Three Essays on Implied Information of Options Markets

Abstract

The options market, as one of the most important derivatives markets, plays a crucial role in hedging risk, reducing investment costs, and providing diversified investment strategies. The inherent low cost and leverage characteristics of the options market attract various types of investors, including speculators. Furthermore, the non-linear features of option prices offer implied information under a risk-neutral measure that other assets cannot provide. The implied information, extracted from option prices, is timely and forward-looking, making it useful for economic forecasting and guiding investment decisions.

This paper investigates the implied information in the options market from a unique industry-level perspective. Using data from U.S. industry Exchange Traded Funds (ETF) options and index options, the paper constructs three industry-level option-implied indicators: implied volatility, variance risk premium, and implied correlation. The paper further explores the role of these industry-level implied indicators in predicting realized volatility and future returns. Additionally, it analyzes the information transmission mechanism between industries and between industries and the market through spillover effects. The main conclusions of the paper are as follows:

First, by introducing industry implied volatility into the realized volatility forecasting model, the study finds that: (1) Adding the industry's own implied volatility significantly improves the predictive ability of the industry realized volatility model. (2) Cross-industry implied volatility enhances the predictability of both industry and market realized volatility, with a more significant role in long-term predictions. (3) The predictive power of industry implied volatility for realized volatility persists out-of-sample. (4) Principal component analysis (PCA) effectively integrates the implied volatility information of various industries, and the realized volatility model containing the principal components of implied volatility performs well both in-sample and out-of-sample.

Second, by studying the spillover effects between implied volatility and realized volatility at the industry and market levels, the study finds that: (1) There are significant spillover effects between the two types of volatility in various industries and the market, indicating clear information transmission across industries and between industries and the market. (2) Grouping volatilities by type reveals that the net spillover effect direction is generally from implied volatility to realized volatility, suggesting that implied volatility can provide incremental information for predicting realized volatility. (3) Grouping volatility by industry reveals that volatility risk transmitters are concentrated in cyclical industries, while risk receivers are primarily found in defensive industries. (4) Pairwise net spillover effects analysis shows that all industries' realized volatilities are “net receivers” of volatility risk, with the risk mainly originating from the implied volatility of four cyclical industries (materials, finance, industrials, and consumer discretionary), the implied volatility of the defensive energy industry, and the market's implied and realized volatilities. (5) Although market implied volatility is always a “net transmitter” of volatility risk, market realized volatility does not always play the role of a “net transmitter”; it absorbs risk from the implied volatility of some industries. (6) Dynamic net spillover effect charts indicate that overall, volatility risk is transmitted from implied volatility to realized volatility. (7) Net spillover effects are time-varying; some industries, originally risk receivers, may switch roles to become risk transmitters after economic events, and vice versa.

Third, by decomposing the market-wide variance risk premium into industry variance risk premiums and cross-industry implied correlation, the study examines the predictive role of these two industry-level option-implied variables on returns, finding that: (1) Industry variance risk premiums positively predict industry returns, and combining industry average variance risk premium with cross-industry implied correlation enhances the predictability of industry returns. (2) In predicting market returns, the model combining industry average variance risk premium and cross-industry implied correlation performs best both in-sample and out-of-sample. (3) Risk-averse investors can translate the predictive ability of industry variance risk premiums and cross-industry implied correlations on returns into economic gains through asset allocation strategies. (4) Cross-industry implied correlation outperforms cross-stock implied correlation in predicting market returns. (5) The information contained in industry average variance risk premium surpasses that of market variance risk premiums, providing unique incremental information for predicting real economic activities (REAs). (6) There is significant information transmission from industry variance risk premium to market variance risk premium.

Through these three empirical studies mentioned above, this paper provides new empirical evidence highlighting the importance of industry-level implied information from the options market. Investors can use this industry-level implied information for prediction, asset allocation, and managing corresponding investment risks. Market regulators can track these industry-level implied information indicators to monitor market risks, identify potential systemic risks in a timely manner, and take appropriate policy measures to stabilize financial markets. Policymakers can use this industry-level implied information to better understand the risk transmission mechanism between industries and between industries and the market, thereby more effectively adjusting and formulating corresponding macroeconomic policies.

Date of Award	23 Dec 2025
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Tao LI (Supervisor) & Xingguo LUO (External Supervisor)