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Target-Driven Bayesian Stacking of Realized and Implied Volatility Forecasts

Author

Listed:
  • Guo, Hongfei
  • Marín Díazaraque, Juan Miguel
  • Veiga, Helena

Abstract

We propose target-driven Bayesian stacking for a fixed six-model ensemble of GARCH and stochastic-volatility forecasts with realised- and VIX-based extensions. Two rolling stacking rules target either log predictive density or QLIKE. In S&P 500, the objective changes the preferred information channel: LPD stacking remains centred on GARCH-RV, whereas QLIKE stacking shifts toward GARCH-VIX. Across 56 rolling windows, the QLIKE stack improves certainty-equivalent returns by roughly one to one-and-a-half percentage points per year, depending on the investor's risk aversion. In the 30 windows where the QLIKE stack assigns material weight to implied volatility models, the gain exceeds two percentage points per year with a 90% win rate. However, LPD stacking delivers tighter 5% Value-at-Risk calibration

Suggested Citation

  • Guo, Hongfei & Marín Díazaraque, Juan Miguel & Veiga, Helena, 2026. "Target-Driven Bayesian Stacking of Realized and Implied Volatility Forecasts," DES - Working Papers. Statistics and Econometrics. WS 49851, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:49851
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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