Time-Varying Factor-Augmented Models for Volatility Forecasting

ArXiv ID: 2508.01880 “View on arXiv”

Authors: Duo Zhang, Jiayu Li, Junyi Mo, Elynn Chen

Abstract

Accurate volatility forecasts are vital in modern finance for risk management, portfolio allocation, and strategic decision-making. However, existing methods face key limitations. Fully multivariate models, while comprehensive, are computationally infeasible for realistic portfolios. Factor models, though efficient, primarily use static factor loadings, failing to capture evolving volatility co-movements when they are most critical. To address these limitations, we propose a novel, model-agnostic Factor-Augmented Volatility Forecast framework. Our approach employs a time-varying factor model to extract a compact set of dynamic, cross-sectional factors from realized volatilities with minimal computational cost. These factors are then integrated into both statistical and AI-based forecasting models, enabling a unified system that jointly models asset-specific dynamics and evolving market-wide co-movements. Our framework demonstrates strong performance across two prominent asset classes-large-cap U.S. technology equities and major cryptocurrencies-over both short-term (1-day) and medium-term (7-day) horizons. Using a suite of linear and non-linear AI-driven models, we consistently observe substantial improvements in predictive accuracy and economic value. Notably, a practical pairs-trading strategy built on our forecasts delivers superior risk-adjusted returns and profitability, particularly under adverse market conditions.

Keywords: Volatility Forecasting, Factor Models, Time-Varying Factors, AI-Based Models, Portfolio Management

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced statistical and machine learning techniques (e.g., time-varying factor models, LSTM) requiring dense mathematical formulation, while also demonstrating strong empirical validation through backtested trading strategies and detailed performance metrics on real-world datasets.

  flowchart TD
    A["Research Goal<br>Improve Volatility Forecasting<br>for Risk Management"] --> B["Data Inputs<br>Large-cap Tech Equities &<br>Major Cryptocurrencies"]
    B --> C["Methodology<br>Time-Varying Factor Model"]
    C --> D["Computational Process<br>Extract Dynamic Market Factors"]
    D --> E["Integration<br>Augment AI & Statistical Models"]
    E --> F["Key Findings<br>Sustained Predictive Accuracy<br>& Economic Value"]
    E --> G["Outcome<br>Superior Pairs-Trading Strategy<br>High Risk-Adjusted Returns"]