Hedging Effectiveness of Implied Volatility vs Historical Volatility

Haocheng Yang

doi:10.54254/2754-1169/2025.BJ29517

Advances in Economics, Management and Political SciencesOpen access

Hedging Effectiveness of Implied Volatility vs Historical Volatility

Research Article

Open Access

Hedging Effectiveness of Implied Volatility vs Historical Volatility

Haocheng Yang ^1*

¹ Xi’an Jiaotong-Liverpool University

^*Corresponding author: Haocheng.Yang22@student.xjtlu.edu.cn

Published on 11 November 2025

AEMPS Vol.241

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-541-7

ISBN (Online): 978-1-80590-542-4

Download Cover

Abstract

This article investigates hedging efficiency of implied volatility (IV) and historical volatility (HV) for European call options on S&P500 (SPY) using Black–Scholes–Merton pricing model. We implement a five days’ hedging frequency with IV using option prices and HV using 30 days’ rolling return window. The hedging performances of implied volatility and historical volatility are measured with root mean squared error (RMSE), mean absolute error (MAE) and profit and loss (PnL) volatility. We conclude that the speed of IV increases following changes in the market and captures risk due to short-term factors better, but it generates larger errors in times of stable markets, while the HV method, with a much slower reaction, generates better results for calm markets with lower tracking errors and low frequency of rebalancing. Our conclusions lead to the proposal that the time to select between IV and HV is the market conditions and both could also offer better hedging methods when they are combined. Our analysis gives valuable input for both the comprehension of volatility and the construction of new more efficient hedging methods in equity index options.

Keywords:

Implied Volatility, Hedging Efficiency, Black-Scholes-Merton Model

View PDF

References

[1]. Han, B., & Zhou, H. (2009). Expected Stock Returns and Variance Risk Premia Management Science, 65(9), 4060–4079. https: //doi.org/10.1093/rfs/hhp008

[2]. Christoffersen, P., Feunou, B., and Jeon, Y., Option valuation with observable volatility and jump dynamics, Journal of Banking & Finance, vol. 61, suppl. 2, Dec 2015, pp. S101–S120, < https: //doi.org/10.1016/j.jbankfin.2015.08.002>

[3]. Guo, W., Ruan, X., Gehricke, S. A., and Zhang, J. E., Term Spreads of Implied Volatility Smirk and Variance Risk Premium, Journal of Futures Markets, vol. 43, no. 7, Jul 2023, pp. 829–857, < https: //doi.org/10.1002/fut.22409> .

[4]. Carr, P., Wu, L., and Zhang, Z., Using Machine Learning to Predict Realized Variance, arXiv preprint arXiv: 1909.10035, Sept 2019, < https: //arxiv.org/abs/1909.10035> .

[5]. Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https: //doi.org/10.5194/gmd-7-1247-2014

[6]. Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https: //doi.org/10.3354/cr030079

[7]. Bollerslev, T., Hood, B., Huss, J., & Pedersen, L. H. (2018). Risk everywhere: Modeling and managing volatility. The Review of Financial Studies, 31(7), 2729–2773. https: //doi.org/10.1093/rfs/hhy041

[8]. Bakshi, G., Cao, C., & Chen, Z. (1997). Empirical performance of alternative option pricing models. The Journal of Finance, 52(5), 2003–2049.

[9]. Christensen, B. J., & Prabhala, N. R. (1998). The relation between implied and realized volatility. Journal of Financial Economics, 50(2), 125–150.

[10]. Andersen, T. G., Bollerslev, T., & Diebold, F. X. (2007). Roughing it up: Including jump components in the measurement, modeling, and forecasting of return volatility. The Review of Economics and Statistics, 89(4), 701–720. https: //doi.org/10.1162/rest.89.4.701

References

[1]. Han, B., & Zhou, H. (2009). Expected Stock Returns and Variance Risk Premia Management Science, 65(9), 4060–4079. https: //doi.org/10.1093/rfs/hhp008

[4]. Carr, P., Wu, L., and Zhang, Z., Using Machine Learning to Predict Realized Variance, arXiv preprint arXiv: 1909.10035, Sept 2019, < https: //arxiv.org/abs/1909.10035> .

[8]. Bakshi, G., Cao, C., & Chen, Z. (1997). Empirical performance of alternative option pricing models. The Journal of Finance, 52(5), 2003–2049.

[9]. Christensen, B. J., & Prabhala, N. R. (1998). The relation between implied and realized volatility. Journal of Financial Economics, 50(2), 125–150.

Cite this article

Yang,H. (2025). Hedging Effectiveness of Implied Volatility vs Historical Volatility. Advances in Economics, Management and Political Sciences,241,59-65.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICFTBA 2025 Symposium: Global Trends in Green Financial Innovation and Technology

ISBN: 978-1-80590-541-7(Print) / 978-1-80590-542-4(Online)

Editor: Lukáš Vartiak, Sun Huaping

Conference website: https://www.icftba.org/Beijing.html

Conference date: 20 November 2025

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.241

ISSN: 2754-1169(Print) / 2754-1177(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).