Forecasting Volatility of Australian Stock Market Applying WTC‐DCA‐Informer Framework

ABSTRACT This article proposed a novel hybrid framework, the WTC‐DCA‐Informer, for forecasting volatility in the Australian stock market. The findings indicated that (1) through a comprehensive comparison with various machine learning and deep learning models, the proposed WTC‐DCA‐Informer framework significantly outperformed traditional methods in terms of predictive performance. (2) Across different training set proportions, the WTC‐DCA‐Informer model demonstrated exceptional forecasting capabilities, achieving a coefficient of determination ( R 2 ) as high as 0.9216 and a mean absolute percentage error (MAPE) as low as 13.6947%. (3) The model exhibited strong adaptability and robustness in responding to significant market fluctuations and structural changes before and after the outbreak of COVID‐19. This study offers a new perspective and tool for forecasting financial market volatility, with substantial theoretical and practical implications for enhancing the efficiency and stability of financial markets.