Forecasting the aggregate market volatility by boosted neural networks

Cetin Ciner

Finance Research Letters, 2025, vol. 72, issue C

Abstract: Prior work provides conflicting evidence on whether macro-finance variables can be used to improve predictability of aggregate volatility relative to the naïve benchmark. This paper contributes to this literature by introducing boosted neural networks as a novel statistical approach that learns from its errors and incorporates nonlinearity. This technique is utilized to reexamine the forecasting ability of macro-finance variables for market volatility. The findings show that out of sample predictability is significantly better when the proposed method is used, relative to the alternative approaches used in the literature, including the naïve benchmark, regardless of the state of the economy.

Keywords: Boosted neural network; Volatility; Forecasting (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:72:y:2025:i:c:s1544612324015344

DOI: 10.1016/j.frl.2024.106505

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