Exploring Multisource High‐Dimensional Mixed‐Frequency Risks in the Stock Market: A Group Penalized Reverse Unrestricted Mixed Data Sampling Approach

Abstract

This paper introduces a novel forecasting approach that addresses a significant challenge in applied research: effectively utilizing high‐dimensional and mixed‐frequency data from multiple sources to explain and predict variables that respond at high frequency. This approach combines a mixed data sampling model and group variable selection methods, resulting in the development of the Group Penalized Reverse Unrestricted Mixed Data Sampling Model (GP‐RU‐MIDAS). The GP‐RU‐MIDAS model is designed to achieve various research objectives, including analyzing mixed‐frequency data in reverse, estimating high‐dimensional parameters, identifying key variables, and analyzing their relative importance and sensitivity. By applying this model to uncover uncertainties in stock market returns, the following notable results emerge: (1) GP‐RU‐MIDAS improves the selection of relevant variables and enhances forecasting accuracy; (2) various risks impact stock market returns in diverse ways, with effects varying over time and exhibiting continuous trends, phase shifts, or extreme levels; and (3) stock market volatility and the Euro to RMB exchange rate significantly influence stock market returns over different forecasting periods, with a generally positive and dynamic impact. In conclusion, the GP‐RU‐MIDAS model demonstrates robustness and utility in complex data analysis scenarios, providing insights into the nuanced realm of stock market risk assessment.