Vol. 5 No. 1 (2025): January−June 2025
Articles

Option-Implied Information for Forward-Looking Market Risk Assessment: Evidence from Commodity Derivatives Markets

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  • Zhen Feng,
  • Chunhe Ni,
  • Shuwen Zhou
Zhen Feng
University of Rochester, Business Analytics, NY, USA
Chunhe Ni
Computer Science, University of Texas at Dallas,Richardson, TX, USA
Shuwen Zhou
Computer Science, The University of New South Wales, Sydney, Australia

Published 2025-06-21

Keywords

  • Option-implied volatility,
  • Risk-neutral density,
  • Commodity derivatives,
  • Forward-looking risk metrics

How to Cite

Feng, Z., Ni, C., & Zhou, S. (2025). Option-Implied Information for Forward-Looking Market Risk Assessment: Evidence from Commodity Derivatives Markets. Spectrum of Research, 5(1). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/31

Copyright (c) 2025 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This paper investigates the predictive power of option-implied information for forward-looking market risk assessment in commodity derivatives markets. While traditional risk metrics rely on historical data, option-implied measures incorporate market expectations about future price movements and potential extreme events. We analyze high-frequency options data from four major commodity markets—crude oil, natural gas, gold, and corn—spanning from January 2018 to December 2023. The study extracts a comprehensive set of option-implied risk measures including implied volatility, risk-neutral skewness, kurtosis, and tail risk indicators. Empirical results demonstrate that option-implied information significantly outperforms historical volatility-based approaches in forecasting realized volatility and extreme price movements. The predictive performance varies substantially across commodities and risk measures, with implied volatility dominating short-horizon forecasts while tail risk indicators excel at longer horizons. Cross-commodity analysis reveals distinctive response patterns to market shocks, with energy markets exhibiting rapid oscillatory responses while precious metals show more persistent mean reversion. Market liquidity significantly impacts forecast accuracy, with prediction errors amplifying during stress periods. The findings contribute to risk management theory and practice by validating the information efficiency of options markets and providing commodity-specific insights for enhanced market risk assessment in derivatives markets.

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