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.