For this purpose, we make use of a recently proposed generalized autoregressive conditional heteroskedasticity–mixed data sampling approach, which typically allows us to examine the role of economic a

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

• Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH‐MIDAS and deep learning models (2022) · doi

  In this paper, we forecast the stock market volatility incorporating low‐frequency macroeconomic variables based on a hybrid model integrating the deep learning method with generalized autoregressive conditional heteroskedasticity and mixed data sampling (GARCH‐MIDAS) model to process the mixing frequency data.

  Keywords: frequency model forecast stock market volatility incorporating macroeconomic variables based hybrid integrating deep learning generalized
• Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects (2020) · doi

  Abstract This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH‐MIDAS‐ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges.

  Keywords: extreme shocks abstract introduces novel generalized autoregressive conditional heteroskedasticity mixed sampling garch midas model stock
• Modeling and forecasting commodity market volatility with long‐term economic and financial variables (2019) · doi

  For this purpose, we make use of a recently proposed generalized autoregressive conditional heteroskedasticity–mixed data sampling approach, which typically allows us to examine the role of economic and financial variables of different frequencies.

  Keywords: purpose make recently proposed generalized autoregressive conditional heteroskedasticity mixed sampling approach typically allows examine role