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.
翻译:现代技术的演变导致对金融市场的深刻影响。 引入汇兑-交易基金等要素以及广泛使用算法交易等先进技术,导致数据繁荣,为揭示更深层次的见解提供了更多的机会。然而,传统的统计方法总是受到金融数据高维、高度关联和时间变化本能的影响。在本次论文中,我们侧重于开发技术来强调这些困难。在拟议的方法中,我们可以有更可解释的模型、更清晰的解释和更好的预测。