Adaptive Window Selection for Financial Risk Forecasting
Researchers have developed a new data-driven method called BAWS (bootstrap-based adaptive window selection) to dynamically adjust the look-back window for financial risk forecasting. This approach aims to improve accuracy by adapting to unknown structural changes in financial data, which traditional fixed-window methods struggle with. BAWS uses a bootstrap-based threshold to determine the optimal window size sequentially, offering better performance than standard rolling windows, especially when data patterns shift. AI