Stock illiquidity and option returns

Kanne, Stefan; Korn, Olaf; Uhrig‐Homburg, Marliese

doi:10.1016/j.finmar.2022.100765

Cited by 3 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Christoffersen, Fournier, and Jacobs (2018a) show that equity options display a strong factor structure, which is highly correlated to volatility, skew and term structure of S&P500 index options. Christoffersen, Goyenko, Jacobs, and Karoui (2018b) include illiquidity premia in option valuation models and Kanne, Korn, and Uhrig-Homburg (2020) find that these premia are negative (positive) if there is net buying (selling) pressure. Ramachandran and Tayal (2021) examine the impact of short-sale constraints on the pricing of options.…”

Section: Related Literaturementioning

confidence: 99%

Option Return Predictability with Machine Learning and Big Data

Bali

Beckmeyer

Mörke

et al. 2023

The Review of Financial Studies

View full text Add to dashboard Cite

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing.

show abstract