Multi-model transfer function approach tuned by PSO for predicting stock market implied volatility explained by...

Area of Science:

• * Financial Econometrics
• * Computational Finance
• * Time Series Analysis

Background:

• * The CBOE Volatility Index (VIX) is a key indicator of expected market volatility.
• * Understanding and forecasting VIX is crucial for risk management and investment strategies.
• * Existing models often struggle to capture the complex, nonlinear relationships influencing VIX.

Purpose of the Study:

• * To investigate the forecasting power of various uncertainty sources on the VIX.
• * To develop and validate an advanced forecasting model for market volatility.
• * To compare the performance of the proposed model against established econometric and deep learning techniques.

Main Methods:

• * Employed a multi-model transfer function technique for analyzing relationships between uncertainty indices and VIX.
• * Utilized Particle Swarm Optimization (PSO) for parameter optimization and model enhancement.
• * Estimated relationships over the period 2012-2022, incorporating commodities, energy, economic policy, and geopolitical uncertainty.

Main Results:

• * The CBOE Volatility Index (VIX) exhibits nonlinear responses to uncertainty indices.
• * The Oil Volatility Index (OVX) demonstrated superior predictive performance compared to other individual uncertainty indices.
• * An aggregate model combining all predictors significantly improved forecasting accuracy (R²: 98.93%) over individual models.
• * The PSO-optimized multi-model transfer function method outperformed autoregressive, traditional econometric, and deep learning models.

Conclusions:

• * The developed multi-model transfer function technique, optimized with PSO, is a highly effective tool for VIX forecasting.
• * This approach accurately captures complex, nonlinear dynamics between market uncertainty and volatility.
• * Findings offer valuable insights for traders and policymakers for hedging, portfolio diversification, and reliable volatility predictions.