Crash Risk Matters: An Option‐Implied Approach to the Expected Market Return

ABSTRACT This study introduces a lower bound that integrates the market's simple return risk‐neutral variance () and a combination of log‐return moments () to predict market log returns. A distinctive feature of the model is that the ratio of to captures crash risk, and the lower bound of log returns depends on the joint effect of crash risk and risk‐neutral variance. Our in‐sample analysis shows that crash risk exhibits significant predictive power for market returns, and its marginal effect differs markedly between crisis and noncrisis periods. In out‐of‐sample testing, we argue that crash risk outperforms several benchmarks in return prediction, while the joint effects of crash risk and variance risk achieves higher accuracy in forecasting crash events. We further demonstrate that our model delivers superior performance when the risk aversion is high, particularly during periods of crises.

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Publication Details

Journal
Journal of Futures Markets
Published
2025-12-08
DOI
https://doi.org/10.1002/fut.70070
Primary Topic
Financial Markets and Investment Strategies
Type
article
Field-Weighted Citation Impact
0.00

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article

Crash Risk Matters: An Option‐Implied Approach to the Expected Market Return

Xinyi Song, Qiang Chen
Journal of Futures Markets
Financial Markets and Investment Strategies
article

Crash Risk Matters: An Option‐Implied Approach to the Expected Market Return

Xinyi Song, Qiang Chen
article en

Abstract

ABSTRACT This study introduces a lower bound that integrates the market's simple return risk‐neutral variance () and a combination of log‐return moments () to predict market log returns. A distinctive feature of the model is that the ratio of to captures crash risk, and the lower bound of log returns depends on the joint effect of crash risk and risk‐neutral variance. Our in‐sample analysis shows that crash risk exhibits significant predictive power for market returns, and its marginal effect differs markedly between crisis and noncrisis periods. In out‐of‐sample testing, we argue that crash risk outperforms several benchmarks in return prediction, while the joint effects of crash risk and variance risk achieves higher accuracy in forecasting crash events. We further demonstrate that our model delivers superior performance when the risk aversion is high, particularly during periods of crises.

Journal of Futures MarketsVol. 46(3)
Shanghai University of Finance and Economics (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 52%
Financial Markets and Investment Strategies
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