A False Discovery Rate Approach to Optimal Volatility Forecasting Model Selection

Arman Hassanniakalager, Paul Baker, Emmanouil Platanakis

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Estimating financial market volatility is integral to the study of investment decisions and behaviour. Previous literature has, therefore, attempted to identify an optimal volatility forecasting model. However, optimal volatility forecasting is dynamic. It depends on the asset being studied and financial market conditions. We propose a novel empirical methodology to account for this dynamism. Using our Multiple Hypothesis Testing with the False Discovery Rate (FDR) method, we identify buckets of superior-performing models relative to the literature’s benchmark models. We present evidence that our proposed FDR bucket with GJR-GARCH has the lowest forecast error in predicting one-step-ahead realized volatility. We also compare our FDR method with two Family-Wise Error Rate model selection frameworks, and the evidence supports our proposed FDR methodology.
Original languageEnglish
Number of pages22
JournalInternational Journal of Forecasting
Early online date6 Aug 2023
Publication statusE-pub ahead of print - 6 Aug 2023


  • Bootstrapping
  • False discovery rate
  • Model selection
  • Multiple hypothesis testing
  • Volatility forecasting

ASJC Scopus subject areas

  • Business and International Management


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