Options-driven volatility forecasting

Nikolas Michael, Mihai Cucuringu and Sam Howison

Quantitative Finance, 2025, vol. 25, issue 3, 443-470

Abstract: We augment the Heterogeneous Autoregressive Regression model for forecasting realized volatility, using various measurements for the daily, weekly, and monthly volatilities, in addition to other predictive features. The main focus is on novel methods for extracting volatility estimators using option price data. Firstly, we provide a dimensionality reduction method for implied volatility surfaces built under the Black–Scholes model, whereby we combine simple row-wise and column-wise decompositions of the implied volatility surface with principal component analysis. Secondly, we provide a method for extracting the implied volatility under the Heston and Bates models. This is achieved by a calibration of these models while assuming that some of the model parameters remain constant. We demonstrate that these augmentations result in improved daily forecasts for realized volatility in a selection of different stocks. These volatility forecasts can also be utilized to further increase predictive performance for the realized volatility of other instruments, and can be combined for forecasting VIX.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2025.2454623 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:25:y:2025:i:3:p:443-470

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2025.2454623

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().