Abstract
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 language | English |
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Pages (from-to) | 881-902 |
Number of pages | 22 |
Journal | International Journal of Forecasting |
Volume | 40 |
Issue number | 3 |
Early online date | 6 Aug 2023 |
DOIs | |
Publication status | Published - 1 Jul 2024 |
Keywords
- Bootstrapping
- False discovery rate
- Model selection
- Multiple hypothesis testing
- Volatility forecasting
ASJC Scopus subject areas
- Business and International Management