Work

Forecasting and Hedging the Volatility Index of Financial Markets via a Robust XGBoost Model

Year: 2025

Type: article

Abstract: This study explores predictive modelling of the S&P 500 VIX through machine learning, integrating macroeconomic indicators and market sentiment data to construct a robust XGBoost framework. Empirical results demonstrate the model’s superior capability in capturing non-linear dynamics compared to traditional models (e.g., GARCH, Logit), achieving 94% accuracy in stable markets. Through rigorous validation including rolling-window Value-at-Risk analysis and cumulative return evaluations, we establish a novel volatility forecasting system that provides actionable insights for dynamic risk management during market turbulence. This research contributes an actionable framework for dynamic risk exposure management, offering strategic insights for portfolio managers and policymakers, providing actionable insights for institutional investors and policymakers.

Source: SAGE Open

DOI: 10.1177/21582440251396044

Authors: Wei-bin Wang, Yao Wu

Institutions: Jiaxing University, Sanming University, Beijing Technology and Business University

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,600

Funder: Jiaxing University