Target-Driven Bayesian Stacking of Realized and Implied Volatility Forecasts

Hongfei Guo, Juan Miguel Marín Díazaraque and Helena Veiga

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

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

Keywords: Bayesian; stacking; QLIKE; Implied; volatility; Realised; variance; Value-at-risk; Volatility; forecasting (search for similar items in EconPapers)
JEL-codes: C11 C53 G17 (search for similar items in EconPapers)
Date: 2026-04-15
New Economics Papers: this item is included in nep-ets and nep-for
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