---
title: Multivariate Affine GARCH
url: https://www.ml-quant.com/papers/ssrn/5260415/
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 5260415
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5260415
featured: 2025-05-21
citations: unknown
topic: Econometrics & Forecasting
---


# Multivariate Affine GARCH

A specific financial model can capture time-varying volatility and dynamic correlation across asset returns, useful for portfolio optimization and option pricing.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5260415
- Identifier: SSRN 5260415
- Released: 2025-05-19
- First featured: Quant Letter No. 98 (2025-05-21): https://www.ml-quant.com/issues/2025-05-21/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

## Related

- [Deep Learning Enhanced Multivariate GARCH](https://www.ml-quant.com/papers/arxiv/2506.02796/): A new volatility modeling framework, LSTM-BEKK, is introduced, integrating deep learning into multivariate GARCH processes for improved robustness and forecasting in financial return data.
- [Data-Driven Risk Measurement by SV-GARCH-EVT Model](https://www.ml-quant.com/papers/arxiv/2201.09434/): A new financial risk measurement model that considers fat-tailed distribution and leverage effect performs better than other models in capturing financial return characteristics.
- [Time Series Analysis](https://www.ml-quant.com/papers/ssrn/5140015/): The paper discusses common time series models used in finance for asset price prediction, risk management, and portfolio optimization, and outlines future research challenges.
- [Robust Optimization in Causal Models and G-Causal Normalizing Flows](https://www.ml-quant.com/papers/arxiv/2510.15458/): We show interventionally robust optimization is continuous under a G‑causal Wasserstein distance and introduce a causal normalizing flow that respects this, improving data augmentation for causal prediction and portfolio optimization.
- [Disciplining Forecasts](https://www.ml-quant.com/papers/ssrn/5046369/): The research introduces a portfolio optimization framework for the top 500 U.S. stocks, showing that efficient use of characteristic information and risk management can surpass value-weighted portfolios.
- [MGARCH Model](https://www.ml-quant.com/papers/ssrn/4990401/): A new study using a multivariate GARCH model identifies shocks and volatility spillovers in speculative return systems, using SP 500 returns, Treasury yields, and the U.S. Dollar Index.
