---
title: Multivariate Rough Volatility
url: https://www.ml-quant.com/papers/ssrn/5065415/
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: SSRN 5065415
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5065415
featured: 2025-01-01
citations: 4
topic: Derivatives & Volatility
---


# Multivariate Rough Volatility

The article introduces a multivariate version of the Rough Fractional Stochastic Volatility model for analyzing logvolatilities, providing an estimator and confirming its theory through simulation.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5065415
- Identifier: SSRN 5065415
- Released: 2024-12-20
- First featured: Quant Letter No. 80 (2025-01-01): https://www.ml-quant.com/issues/2025-01-01/
- Citations (Semantic Scholar): 4
- 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.
- [Rough differential equations for volatility](https://www.ml-quant.com/papers/arxiv/2412.21192/): The paper presents a method for jointly lifting a Brownian motion and a low-regularity adapted stochastic rough path, useful for modeling rough volatility.
- [Prediction of linear fractional stable motions using codifference, with application to non-Gaussian rough volatility](https://www.ml-quant.com/papers/arxiv/2507.15437/): A new method for predicting future changes in linear fractional stable motion (LFSM) has been proposed, which performs better than the fractional Brownian motion in predicting high-frequency FX rates and volatility time series.
- [Risk premium and rough volatility](https://www.ml-quant.com/papers/arxiv/2403.11897/): The paper examines the effect of unpredictable risk on pricing in a rough volatility model, emphasizing the random nature of the market price of volatility risk.
- [A path-dependent PDE solver based on signature kernels](https://www.ml-quant.com/papers/arxiv/2403.11738/): The article introduces a new, verifiably effective kernel-based solver for path-dependent partial differential equations (PPDEs). This provides a practical alternative to Monte Carlo methods, especially for option pricing under rough volatility.
- [Convergence of Heavy-Tailed Hawkes Processes and the Microstructure of Rough Volatility](https://www.ml-quant.com/papers/arxiv/2312.08784/): The research identifies the weak convergence of a nearly-unstable Hawkes process with a heavy-tailed kernel, useful for creating a scaling limit for a financial market model.