Academic Research
Academic Research
Information Content of Aggregate Implied Volatility Spread, with Bing Han
Published: Management Science (2021)
Paper Award: Outstanding Paper Award from China International Forum on Finance and Policy (CIFFP, 2018)
Paper Presentation: Nankai University, Southwestern University of Finance and Economics, University of International Business and Economics, University of Toronto, University of Waterloo, FMA (2018), CICF (2018), AsiaFA (2018), CIFFP (2018)
Abstract: Aggregate implied volatility spread (IVS), defined as the cross-sectional average difference in the implied volatilities of at-the-money call and put equity options, is significantly and positively related to future stock market returns at daily, weekly, and monthly to semiannual horizons. This return predictive power is incremental to existing return predictors, and it is significant both in sample and out of sample. Furthermore, IVS can forecast macroeconomic news up to one year ahead. The return predictability concentrates around macro news announcement. Common informed trading in equity options offers an integrated explanation for the ability of IVS to predict both future stock market returns and real economic activity.
[Data] (Updated to 2019)
Stock Return Autocorrelations and Expected Option Returns, with Yoontae Jeon and Raymond Kan
Published: Management Science (2024)
Paper Award: Best Paper Award from the Asia-Pacific Association of Derivatives (APAD, 2019)
Paper Presentation: University of Toronto, AsiaFA (2019), APAD (2019), SFS Cavalcade Asia-Pacific (2019), EasternFA (2020), SoFiE (2023), AFFI (2023), NFA (2023), China Derivatives Youth Forum (2023)
Abstract: We show that the return autocorrelation of underlying stock is an important determinant of expected equity option returns. Using an extended Black-Scholes model incorporating the presence of stock return autocorrelation, we demonstrate that expected returns of both call and put options are increasing in return autocorrelation coefficient of the underlying stock. Consistent with this insight, we find strong empirical support in the cross-section of average returns of equity options. Our paper highlights the necessity to control for stock return autocorrelation when studying option return predictability.
Idiosyncratic Volatility and the ICAPM Covariance Risk, with Bing Han
Published: Journal of Financial and Quantitative Analysis (2025)
Paper Presentation: University of Toronto, Chinese University of Hong Kong, Cheung Kong Graduate School of Business, Dongbei University of Finance and Economics, Tianjin University, University of South Carolina, University of Sussex, Shanghai University of Finance and Economics, London School of Economics and Political Science, Chinese University of Hong Kong (Shenzhen), University of International Business and Economics, Beijing Institute of Technology, APAD (2020), NFA (2020), FMA (2020), Frontiers of Factor Investing (2021), ITAM (2021), AsiaFA (2021), CIRF (2021)
Abstract: We show theoretically and empirically that the cross-section of stock return idiosyncratic volatilities contains useful information about the ICAPM. We construct a proxy cross-sectional bivariate idiosyncratic volatility (CBIV) for the covariance risk between the market and the unobserved hedge portfolio under the ICAPM. Consistent with the ICAPM pricing relation, CBIV is a robust and significant predictor of the equity risk premium. We further show that the return predictability of the tail index in Kelly and Jiang (2014) can be explained by the ICAPM covariance risk.
[Data] (Updated to 2022)
Betting Against the Crowd: Option Trading and Market Risk Premium, with Jie Cao, Xintong Zhan, and Guofu Zhou
Revise and Resubmit: Journal of Financial and Quantitative Analysis
Paper Award: Best Paper Award from the 6th China Derivatives Youth Forum (2023)
Paper Presentation: Hunan University, Hunan Normal University, Jiangxi University of Finance and Economics, Renmin University of China, Tsinghua University, Xi'an Jiaotong University, CICF (2023), CIRF (2023), China Derivatives Youth Forum (2023), Hong Kong Fintech, AI and Big Data in Business (2023), CFE-CMStatistics (2023), AsiaFA (2024), HK PolyU Derivatives Symposium (2024), WFEClear (2025), NFA (2025)
Abstract: We comprehensively study how option trading influences the equity market risk premium. Surprisingly, we find that trading of individual call options predicts the market index more strongly than index options. This predictability is both statistically significant and economically substantial, persisting from weeks to months. Aggregate individual options trading largely reflects investor sentiment and is primarily driven by retail investors. It also forms the key component in an ensemble learning model, combined with index option trading and other related predictors, respectively. Among all predictors examined, option trading emerges as the most powerful predictor of the market risk premium.
[Data] (Updated to 2020)
Forecasting Option Returns with News, with Jie Cao, Bing Han, Ruijing Yang, and Xintong Zhan
Revise and Resubmit: Journal of Finance
Paper Award: Best Paper Award from Hong Kong Fintech, AI and Big Data in Business (2024)
Paper Presentation: Chinese Academy of Sciences, Nankai University, Shanghai University of Finance and Economics, University of International Business and Economics, CFE-CMStatistics (2021), CICF (2022), AsianFA (2022), CIRF (2022), SFS Cavalcade Asia-Pacific (2022), APAD (2023), CIRF (2023), FERM (2023), China Derivatives Youth Forum (2023), 4th Bay Area FinTech Research Forum (2024), MFA (2024), Virtual Derivatives Workshop (2024), Hong Kong Fintech, AI and Big Data in Business (2024), China FinTech Annual Conference (2024), ICFT (2024), FMA Derivatives and Volatility (2024), ICE Hong Kong (2024), AFA (2025), China Derivatives Youth Forum Keynote (2025)
Abstract: This paper examines the information content of news media for the cross-section of expected equity option returns. We derive text-based signals from news articles on publicly traded companies that strongly forecast their delta-hedged equity option returns. The option return predictability of our textual signals is robust to variations in text representations and machine learning approaches. We propose a text-based method to evaluate various underlying mechanisms. We find that media coverage of companies contains valuable information about future change in stock return volatilities. This appears to be the key source of option return predictability by news articles.
Do Insurers Listen to Earnings Conference Calls? Evidence from the Corporate Bond Market, with Jie Cao, Russell Wermers, Xintong Zhan, and Linyu Zhou
Paper Presentation: Central University of Finance and Economics, Renmin University of China, University of International Business and Economics, Southwestern University of Finance and Economics, Shandong University, Xi'an Jiaotong-Liverpool University, King's College London, Kyoto University, Southern Methodist University, Southern University of Science and Technology (Shenzhen), MIT Asia Conference in Accounting (2023), CIRF (2023), MRS (2023), AFA (2024), CICF (2024), NFA (2025)
Abstract: We provide novel evidence that insurance companies react to the linguistic tone of earnings conference calls, and that their trades in the wake of soft information revealed in calls are incrementally predictive of corporate bond downgrades. Specifically, negatively toned earnings calls trigger insurance company selling, in aggregate, of BBB-minus rated bonds (the lowest investment-grade rating). The "selling on bad news" by insurance companies strongly predicts future bond downgrades, after controlling for the predictive content of the call itself. Finally, we find that insurance companies employ linguistic tone information from public company calls to trade bonds issued by private industry peers.
Conditional Expected Returns on Individual Stocks with and without Intertemporal Hedging, with Fousseni Chabi-Yo and Da-Hea Kim
Paper Award: Best Paper Award from the 9th China Derivatives Youth Forum (2025)
Paper Presentation: University of Massachusetts Amherst, China Derivatives Youth Forum (2025)
Abstract: We derive tractable lower and upper bounds on the conditional expected excess return of an individual stock in a dynamic multi-period economy that allows for portfolio rebalancing. The bounds are expressed in terms of higher-order risk-neutral joint moments between market and stock returns and are implementable ex ante using index and single-name option prices. Our framework nests the benchmark without intertemporal hedging, enabling us to quantify the incremental role of hedging motives. Empirically, the bounds are tight and outperform leading one-period alternatives in out-of-sample predictions of future stock returns. The gap between the hedging and non-hedging bounds is economically meaningful, indicating that intertemporal hedging materially shapes expected returns on individual stocks.