Hierarchical Machine Learning for Variance Risk Premium Estimation: From VIX Forecasting to Options Trading
Fouhy (2026) studies the volatility risk premium in volatility and options using tree ensembles. The abstract does not mention a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“For realised volatility, a comparison of five daily variance estimators including close-to-close, Parkinson, Garman-Klass, Rogers-Satchell, and Yang-Zhang demon-strates that range-based estimators achieve up to 2.46 times the statistical efficiency of close-to-close returns.”
AssetVolatility and optionsoptions
StrategyVolatility risk premium
TechniqueTree ensembleclassical time series
GeographyGlobal
Also stated: Daily, Price and volume, Fundamentals, Options-implied
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodVolatility risk premium by year
FamilyVolatility risk premium102Momentum and trend following289Factor investing240Machine learning return prediction15318 more · 900
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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