A False Discovery Rate approach to optimal volatility forecasting model selection☆

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 onestep -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. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).