Research Compliance

Rough Volatility Across Assets

Aug 17, 2026 Source ↗

Key Insights

We measure volatility roughness across asset classes using a common data infrastructure and pipeline.
Our data covers 3,926 United States equities, 34 CME futures roots, rates, FX, and commodities, an.

Cite this synthesis DOI: 10.48550/arXiv.2608.16749 ↗

Show formatted citation

@misc{ acaciacompliance-research-rough-volatility-across-assets,
  title = { Rough Volatility Across Assets },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2608.16749 },
  url = { http://arxiv.org/abs/2608.16749v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - Rough Volatility Across Assets
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2608.16749
UR  - http://arxiv.org/abs/2608.16749v1
ER  -

Edit on GitHub — registry.json

Overview

We measure volatility roughness across asset classes using a common data infrastructure and pipeline. Our data covers 3,926 United States equities, 34 CME futures roots, rates, FX, and commodities, an

Why It Matters

This item adds to the AML and compliance knowledge base. Practitioners can use it to stay current on Rough Volatility Across Assets, but should validate its claims against primary sources and more recent work before relying on it in production decisions.

Key Takeaways

Understand how Rough Volatility Across Assets relates to AML and compliance workflows and controls.
Assess evidence quality and freshness before acting on the findings.
Use the tagged topics to connect this item to related content in the library.

Article Metadata

Evergreen Synthesized

aml

Infrastructure

SQI 0.750