Identifying Stock Option Mispricing at a Large Cross Section

ABSTRACT This paper introduces an innovative two‐step approach for identifying implied volatility (IV) mispricing across a large cross‐section, moving beyond the traditional volatility forecasting framework. The two‐step process disentangles the contributions of historical volatility and other firm‐specific characteristics, isolating the residual as the IV mispricing. Different from traditional IV misvaluation proxies, which primarily focus on 1‐month at‐the‐money (ATM) options, our method demonstrates broader applicability. It accommodates options with wider maturities and extends to both ATM and out‐of‐the‐money (OTM) call and put options. Applying a long‐short delta‐hedged options trading strategy, using the IV mispricing, achieves a high information ratio (IR). When incorporating short‐ and long‐term historical volatility trends as conditions, while returns remain relatively unchanged, portfolio volatility is significantly reduced, further enhancing the IR to 4.093. This approach provides a robust predictive signal for option returns and remains resilient to transaction costs, consistently outperforming alternative signals, as validated through double‐sorting analysis.

Authors

Institutions

Publication Details

Journal
Journal of Futures Markets
Published
2025-06-02
DOI
https://doi.org/10.1002/fut.22606
Citations
1
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
2.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identifying Stock Option Mispricing at a Large Cross Section

Yaofei Xu, Dalu Zhang, S.G. Wang, Zhiyong Li
1 citations
Journal of Futures Markets
Stochastic processes and financial applications
2.21
article

Identifying Stock Option Mispricing at a Large Cross Section

Yaofei Xu, Dalu Zhang, S.G. Wang, Zhiyong Li
article en
1 citations

Abstract

ABSTRACT This paper introduces an innovative two‐step approach for identifying implied volatility (IV) mispricing across a large cross‐section, moving beyond the traditional volatility forecasting framework. The two‐step process disentangles the contributions of historical volatility and other firm‐specific characteristics, isolating the residual as the IV mispricing. Different from traditional IV misvaluation proxies, which primarily focus on 1‐month at‐the‐money (ATM) options, our method demonstrates broader applicability. It accommodates options with wider maturities and extends to both ATM and out‐of‐the‐money (OTM) call and put options. Applying a long‐short delta‐hedged options trading strategy, using the IV mispricing, achieves a high information ratio (IR). When incorporating short‐ and long‐term historical volatility trends as conditions, while returns remain relatively unchanged, portfolio volatility is significantly reduced, further enhancing the IR to 4.093. This approach provides a robust predictive signal for option returns and remains resilient to transaction costs, consistently outperforming alternative signals, as validated through double‐sorting analysis.

Journal of Futures MarketsVol. 45(9)
University of Leicester (GB), Xi’an Jiaotong-Liverpool University (CN)
Openalex Percentile: Top 14%
Stochastic processes and financial applications
2.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Identifying Stock Option Mispricing at a Large Cross Section — Yaofei Xu, Dalu Zhang, et al. · Journal of Futures Markets (2025) | TGRS Research Map | TGRS