Roughness in VIX Index and in Realized Volatility: Rolling Window Estimation by Randomized Kolmogorov-Smirnov Distribution

Sergio Bianchi and Daniele Angelini ()
Additional contact information
Sergio Bianchi: MEMOTEF, Sapienza University of Rome
Daniele Angelini: MEMOTEF, Sapienza University of Rome

A chapter in New Perspectives in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2025, pp 61-73 from Springer

Abstract: Abstract The modeling and forecasting of financial market volatility constitute fundamental components of effective risk management and optimal asset allocation. Traditional models like GARCH and SV often fail to capture the long memory and roughness empirically observed in volatility, prompting the adoption of fractional processes. Accurate estimation of the log-volatility roughness parameter is thus key to validating rough volatility models, with several methodologies proposed, including spectral, wavelet, and machine learning techniques. In contrast to approaches focused on moment behavior, we adopt a novel method based on the self-similarity of fractional processes, examining how the entire log-volatility distribution scales across time horizons. We deduce the variance of the estimator and study the roughness of both CBOE VIX and realized volatility.

Keywords: VIX; Realized volatility; Hurst exponent; Kolmogorov-Smirnov test (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-3-032-05551-4_6

Ordering information: This item can be ordered from
http://www.springer.com/9783032055514

DOI: 10.1007/978-3-032-05551-4_6

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().