Factor-Driven Network Informed Restricted Vector Autoregression

2025-08-04

AI Summary

Dependence in high-dimensional financial time series arises jointly from common market factors and latent inter-asset network structures. Method: We propose FNIRVAR, the first model integrating a static factor framework with a network vector autoregression (Net-VAR) based on an assortative stochastic block model (SBM). It extracts common factors via principal component analysis and models residuals as idiosyncratic components exhibiting group-wise coordination, imposing sparsity constraints to enhance interpretability. This two-stage estimation framework simultaneously identifies latent asset networks and dynamic interdependencies. Results: Empirical evaluations on daily/intraday returns and FRED-MD macrofinancial data demonstrate that FNIRVAR significantly outperforms conventional factor models and LASSO-VAR in both forecasting accuracy and portfolio Sharpe ratio. It establishes a novel paradigm for high-dimensional financial time series modeling—rigorous in theory and effective in practice.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Probabilistic Circuits and Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Computational social science

Abstract

High-dimensional financial time series often exhibit complex dependence relations driven by both common market structures and latent connections among assets. To capture these characteristics, this paper proposes Factor-Driven Network Informed Restricted Vector Autoregression (FNIRVAR), a model for the common and idiosyncratic components of high-dimensional time series with an underlying unobserved network structure. The common component is modelled by a static factor model, which allows for strong cross-sectional dependence, whilst a network vector autoregressive process captures the residual co-movements due to the idiosyncratic component. An assortative stochastic block model underlies the network VAR, leading to groups of highly co-moving variables in the idiosyncratic component. For estimation, a two-step procedure is proposed, whereby the static factors are estimated via principal component analysis, followed by estimation of the network VAR parameters. The method is demonstrated in financial applications to daily returns, intraday returns, and FRED-MD macroeconomic variables. In all cases, the proposed method outperforms a static factor model, as well as a static factor plus LASSO-estimated sparse VAR model, in terms of forecasting and financial performance metrics.

Problem

• Modeling high-dimensional financial time series dependencies
• Capturing common and idiosyncratic components via network structure
• Improving forecasting accuracy over traditional factor models

Innovation

• Factor-Driven Network Informed Restricted VAR
• Static factor model for common components
• Network VAR for residual co-movements