---
title: Generalized Autoregressive Conditional Betas: A New Multivariate Score-Driven Filter
url: https://www.ml-quant.com/papers/ssrn/4602060/
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 4602060
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4602060
featured: 2023-10-18
citations: 1
topic: Asset Pricing & Factors
---


# Generalized Autoregressive Conditional Betas: A New Multivariate Score-Driven Filter

New: A new asset pricing model, the generalized ACB, is introduced, enhancing the autoregressive conditional beta model by driving dynamic interaction effects among beta coefficients.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4602060
- Identifier: SSRN 4602060
- Released: 2023-10-13
- First featured: Quant Letter No. 22 (2023-10-18): https://www.ml-quant.com/issues/2023-10-18/
- Citations (Semantic Scholar): 1
- Published in: Studies in Nonlinear Dynamics & Econometrics
- Topic: Asset Pricing & Factors

## Related

- [A Deep Structural Model for Empirical Asset Pricing](https://www.ml-quant.com/papers/ssrn/4602537/): ML and Theory Integration: The article introduces a new model that merges deep learning and structural models for better prediction of equity returns and covariances, leading to higher returns and sharpe ratios.
- [Bubble economics](https://www.ml-quant.com/papers/arxiv/2311.03638/): Nonstationary Phenomenon: The article discusses the theory of rational asset price bubbles, highlighting that bubbles linked to real assets like stocks and housing are nonstationary phenomena tied to unbalanced growth.
- [Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models](https://www.ml-quant.com/papers/arxiv/2309.08800/): A new technique using dynamic time warping has been created to identify lead-lag relationships in multivariate time series systems, demonstrated in financial markets.
- [Machine Learning and the Cross-Section of Emerging Market Corporate Bond Returns](https://www.ml-quant.com/papers/ssrn/4632924/): Machine learning models considering nonlinearities and interactions offer better predictions of corporate bond behavior in emerging markets with high transaction costs, with key predictors tied to low-risk macro and momentum factors.
- [The CAPM, APT, and PAPM](https://www.ml-quant.com/papers/ssrn/4566414/): CAPM, APT, and PAPM: The Popularity Asset Pricing Model (PAPM) improves on the Capital Asset Pricing Model (CAPM) by considering investor preferences and beliefs, addressing CAPM's empirical limitations.
- [Does Peer-Reviewed Research Help Predict Stock Returns?](https://www.ml-quant.com/papers/arxiv/2212.10317/): The research suggests that the predictability of cross-sectional return predictors decreases by half in post-sample scenarios, indicating that theory doesn't improve prediction and peer-review often misinterprets mispricing as risk.
