Time-Varying Factor-Augmented Models for Volatility Forecasting

TL;DR

This paper introduces a novel time-varying factor-augmented framework for volatility forecasting that improves accuracy and economic value across asset classes by capturing evolving market co-movements efficiently.

Contribution

It develops a flexible, computationally efficient model-agnostic approach that integrates dynamic factors into both statistical and AI-based volatility forecasts.

Findings

Enhanced predictive accuracy over benchmarks

Improved risk-adjusted returns in pairs trading

Effective across stocks and cryptocurrencies

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.