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Forecasting the aggregate market volatility by boosted neural networks

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  • Ciner, Cetin

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.

Suggested Citation

  • Ciner, Cetin, 2025. "Forecasting the aggregate market volatility by boosted neural networks," Finance Research Letters, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:finlet:v:72:y:2025:i:c:s1544612324015344
    DOI: 10.1016/j.frl.2024.106505
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