Option Return Predictability

Cao, Jie; Han, Bing; Tong, Qing; Zhan, Xintong

doi:10.1093/rfs/hhab067

Cited by 68 publications

(21 citation statements)

References 69 publications

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“…Cross-Section of Option Returns The cross-section of option returns remains a puzzle (see, for example, Bali and Murray (2013), Cao and Han (2013), Christoffersen et al (2018), andZhan et al (2022)). Existing literature has proposed different underlying stock characteristics (Bali and Murray (2013), Han (2013), andZhan et al (2022)) or liquidity measures (Christoffersen et al (2018)) as determinants of option returns.…”

Section: Predictability Of Return Higher Momentsmentioning

confidence: 99%

Price Discovery for Derivatives

Keller¹,

Tseng²

2023

Preprint

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A theory of price discovery across derivative markets with respect to higher-order information is obtained, via a model where an informed agent trades a complete set of state-contingent claims under general information asymmetry. In an equivalent options formulation, the informed agent has private information regarding arbitrary aspects of the payoff distribution of an underlying asset and trades a complete menu of options, with no assumption on the possible payoff distributions or the nature of private information. We characterize, in closed form, the informed demand, price impact, and information efficiency of prices. Our results contain a theory of insider trading on higher-order moments of the underlying payoff as a special case. The informed demand formula prescribes options strategies for trading on any given moment and extends those already used in practice for, e.g. volatility trading. The volatility smile is explained by an "insider smile" of implied volatilities.

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Section: Predictability Of Return Higher Momentsmentioning

confidence: 99%

Price Discovery for Derivatives

Keller¹,

Tseng²

2023

Preprint

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“…Ramachandran and Tayal (2021) report a monotonic relation between various measures of short-sales constraints and delta-hedged put returns on overpriced stocks. Zhan, Han, Cao, and Tong (2022) show that many stock characteristics (such as profit margin, firm profitability, cash holding, and shares issuance) have a strong predictive power for delta-hedged option returns.…”

Section: Related Literaturementioning

confidence: 99%

“…We consider three types of option positions: delta-hedged calls, delta-hedged puts, and straddles. Following Cao and Han (2013) and Zhan, Han, Cao, and Tong (2022), we define returns to buying a delta-hedged call as:…”

Section: Sample and Variable Constructionmentioning

confidence: 99%

“…Although the average moneyness (i.e., ratio of option strike to stock price) is in fact close to 1.0, the sample contains options which moneyness varies between 0.8 and 1.2. While our choice of this relatively large moneyness interval is in line with Zhan, Han, Cao, and Tong (2022), it is not the only approach in the literature. For example, Goyal and Saretto (2009) use a much tighter interval of between 0.975 and 1.025.…”

Section: Moneyness Intervalsmentioning

confidence: 99%

“…In our empirical analysis, we construct a library of predictor variables for option returns over the sample period of 1996 to 2020. Our characteristics come from previous studies such as An, Ang, Bali, and Cakici (2014), Boyer and Vorkink (2014), Bali and Murray (2013), Cao and Han (2013), Christoffersen, Goyenko, Jacobs, and Karoui (2018), Goyal and Saretto (2009), Hu and Jacobs (2020), Vasquez (2017), and Zhan, Han, Cao, and Tong (2022), and can be classified into four categories: (i) Contract characteristics such as maturity, open interest, option Greeks, etc. ; (ii) Descriptors of risk-neutral return distribution such as model free implied volatility, skewness, and kurtosis at the stock level; (iii) Measures of physical return distribution such as volatility, skewness, kurtosis, and autocorrelation at the stock level; and (iv) Firm level characteristics such as size, price, book-to-market.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Are Equity Option Returns Abnormal? IPCA Says No

Goyal¹,

Saretto²

2022

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Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach

Kumar

Agarwalla

Varma

et al. 2023

Journal of Futures Markets

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While there is a large literature on modeling volatility smile in options markets, most such studies are eventually focused on the forecasting performance of the model parameters and not on the applicability of the models in a trading environment. Drawing on the analogy of volatility smile like a term structure in the context of interest rates in fixed‐income markets, we evaluate the performance of the Dynamic Nelson–Siegel (DNS) approach to modeling the dynamics of volatility smile in a trading environment against competing alternatives. Using model‐based mispricing as our sorting criterion, and deploying a trading strategy of going long the options in the upper deciles and going short the options in the lower deciles, we show that dynamic models consistently outperform their static counterparts, with the worst dynamic model outperforming the best static model in terms of the percentage of mean returns from the trading portfolios and the Sharpe ratio. Specifically, we find that the DNS model consistently outperforms all other competing specifications on most of our selected criteria.

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Option Return Predictability

Cited by 68 publications

References 69 publications

Price Discovery for Derivatives

Price Discovery for Derivatives

Are Equity Option Returns Abnormal? IPCA Says No

Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach

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