Information, Not Flexibility: Attributing Forecast Gains in Neural Stochastic Volatility

Hongfei Guo, Juan Miguel Marín and Helena Veiga

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

Abstract: When a neural volatility model beats a benchmark, is it the network or the data? We change one ingredient at a time within one Bayesian stochastic volatility framework, for three stock indices at one-, five-, and ten-day horizons. It is the data. Adding realised variance lowers the quasi-likelihood (QLIKE) loss by 2.8% to 13.4% in all nine cases and improves the one-day log score in every market. A neural network does not beat a linear function in one-day QLIKE with identical inputs, even inputs of the heterogeneous autoregressive (HAR) type. Combining the two neural models adds no stable gain. HAR and Realized GARCH have the lowest QLIKE in the nine-model comparison. With Student-t innovations, linear return feedback makes multi-step variance means infinite, while a bounded network does not. In practice, add realised information and choose the innovation distribution carefully, because it changes one-day tail risk rankings. Flexibility comes last.

Keywords: Forecast; combination; Neural; networks; Predictive; distributions; Realised; variance; Stochastic; volatility; Volatility; forecasting (search for similar items in EconPapers)
Date: 2025-09-16
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ets, nep-for and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:47944

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