---
title: Long Memory and Fractality in Volatility Indices
url: https://www.ml-quant.com/papers/repec/hin-complx-6728432/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:hin:complx:6728432
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdownloads.hindawi.com%2Fjournals%2Fcomplexity%2F2022%2F6728432.pdf%3Bh%3Drepec%3Ahin%3Acomplx%3A6728432
featured: 2023-07-12
citations: unknown
topic: Derivatives & Volatility
---


# Long Memory and Fractality in Volatility Indices

A study of nine volatility indices reveals evidence of long memory and fractality, providing new insights for investment decisions and trading strategies.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdownloads.hindawi.com%2Fjournals%2Fcomplexity%2F2022%2F6728432.pdf%3Bh%3Drepec%3Ahin%3Acomplx%3A6728432
- Identifier: RePEc:hin:complx:6728432
- Released: 2022-09-26
- First featured: Quant Letter No. 7 (2023-07-12): https://www.ml-quant.com/issues/2023-07-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Rough Volatility: Fact or Artefact?](https://www.ml-quant.com/papers/arxiv/2203.13820/): Fact or Artifact: The study proposes a new method to estimate the roughness of financial asset volatility, attributing observed roughness to microstructure noise.
- [Risk Revisited](https://www.ml-quant.com/papers/ssrn/4825844/): The study identifies recency, cluster, and sign as three factors shaping investors' risk perceptions of a stock, influencing trading volume and future volatility.
- [A New Star Is Born: Does the VIX1D Render Common Volatility Forecasting Models for the U.S. Equity Market Obsolete?](https://www.ml-quant.com/papers/ssrn/4505785/): New Index for Volatility Forecasting: The Cboe's 1-Day Volatility Index overestimates S&P 500 volatility, but a simple proxy can correct this for more accurate forecasts with less data.
- [0DTE Option Pricing](https://www.ml-quant.com/papers/ssrn/4503344/): Capturing Volatility Dynamics: The market for ultra short-term zero days-to-expiry options has expanded, with a new pricing formula developed to account for factors like leverage and volatility-of-volatility.
- [Are There Dragon Kings in the Stock Market?](https://www.ml-quant.com/papers/arxiv/2307.03693/): A study of market volatility over 50 years shows that the highest volatility aligns with major economic crises, and these instances are classified as Black Swans, Dragon Kings, or Negative Dragon Kings based on their statistical significance.
- [Machine learning for option pricing: an empirical investigation of network architectures](https://www.ml-quant.com/papers/arxiv/2307.07657/): A study finds that the generalized highway network and a DGM variant improve the accuracy and training time of machine learning algorithms for option pricing.
