  Modern evolvements of the technologies have been leading to a profound
influence on the financial market. The introduction of constituents like
Exchange-Traded Funds, and the wide-use of advanced technologies such as
algorithmic trading, results in a boom of the data which provides more
opportunities to reveal deeper insights. However, traditional statistical
methods always suffer from the high-dimensional, high-correlation, and
time-varying instinct of the financial data. In this dissertation, we focus on
developing techniques to stress these difficulties. With the proposed
methodologies, we can have more interpretable models, clearer explanations, and
better predictions.
