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NEWS IV: A model with news and implied volatility for enhanced volatility prediction

Author

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  • Vitor Gentini

  • Marcio Issao Nakane

Abstract

This paper examines whether implied volatility and textual news jointly improve volatility forecasting. We propose the NEWS IV model, which combines the realized-variance components of the HAR model with option-implied variance and news topics extracted via Latent Dirichlet Allocation within a flexible machine learning framework. Using data for the Ibovespa ETF and major Brazilian stocks, we evaluate predictive performance relative to HAR-type benchmarks across multiple horizons. We show that the model augmented with implied volatility and news delivers performance comparable to standard models at the daily horizon and improves forecasts at weekly and monthly horizons. The results reveal a clear horizon-dependent pattern. Implied volatility plays a central role in short-term predictions, while news-based variables become increasingly relevant at longer horizons. These findings highlight the complementary informational content of market expectations and textual data for understanding volatility dynamics.

Suggested Citation

  • Vitor Gentini & Marcio Issao Nakane, 2026. "NEWS IV: A model with news and implied volatility for enhanced volatility prediction," Working Papers, Department of Economics 2026_25, University of São Paulo (FEA-USP).
    • Handle: RePEc:spa:wpaper:2026wpecon25
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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