---
title: Early Exercise and Put Risk Premia
url: https://www.ml-quant.com/papers/repec/inm-ormnsc-v-71-y-2025-i-2-p-1824-1845/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:inm:ormnsc:v:71:y:2025:i:2:p:1824-1845
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1287%2Fmnsc.2023.00440%3Bh%3Drepec%3Ainm%3Aormnsc%3Av%3A71%3Ay%3A2025%3Ai%3A2%3Ap%3A1824-1845
featured: 2025-10-27
citations: unknown
topic: Asset Pricing & Factors
---


# Early Exercise and Put Risk Premia

Accounting for optimal early exercise, American puts show less negative raw returns but more negative delta‑hedged returns than European puts, changing which option anomalies look profitable.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1287%2Fmnsc.2023.00440%3Bh%3Drepec%3Ainm%3Aormnsc%3Av%3A71%3Ay%3A2025%3Ai%3A2%3Ap%3A1824-1845
- Identifier: RePEc:inm:ormnsc:v:71:y:2025:i:2:p:1824-1845
- Released: 2025-10-27
- First featured: Quant Letter No. 117 (2025-10-27): https://www.ml-quant.com/issues/2025-10-27/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Asset Pricing & Factors

## Related

- [NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks](https://www.ml-quant.com/papers/arxiv/2409.03024/): A Synthetic Mobility Dataset: The paper presents NUMOSIM, a synthetic mobility dataset for testing anomaly detection techniques, simulating realistic mobility scenarios and anomalies to improve geospatial mobility analysis.
- [A Capital Asset Pricing Model with Idiosyncratic Tail Risk: Comovement of Momentum and Low Risk Anomalies](https://www.ml-quant.com/papers/ssrn/4680248/): Momentum and Low Risk Anomalies: The new model expands the traditional Capital Asset Pricing Model (CAPM) by factoring in idiosyncratic tail risk, explaining momentum in stock returns and low risk anomalies.
- [Anomaly Persistence](https://www.ml-quant.com/papers/ssrn/5276723/): The article introduces a method for testing asset pricing anomalies, showing that multiple paths on the same dataset lead to high outcome correlations, significantly affecting inference.
- [Expected Returns and Stock Performance](https://www.ml-quant.com/papers/ssrn/5244033/): A few stocks significantly influence the performance of cross-sectional asset pricing anomalies, indicating that a large part of the returns may be due to mispricing.
- [FearBased Pricing](https://www.ml-quant.com/papers/ssrn/5127501/): The article introduces a new fear-based model for returns, arguing that it could have predicted most anomalies and factors in the past 50 years.
- [Do Chinese Retail and Institutional Investors Trade on Anomalies?](https://www.ml-quant.com/papers/ssrn/5112567/): The study shows that retail investors in China trade against anomaly prescriptions, while institutions trade in line with anomalies, influenced by lottery stock preference and return extrapolation.
