Machine Learning and Network Analysis in the Options Market

该项目利用机器学习和网络分析文献中的工具，对期权市场(基于标的证券价值的衍生金融工具)进行统计分析。所使用的数据集包含期权交易的数据，包括按市场参与者细分的价格和成交量信息。该项目非常适合信息和通信技术(信通技术)研究领域；这样的数据集不仅可用于无数潜在的应用，而且可推动开发或加强现有的大型时间序列数据分析技术。虽然金融数据是时间序列的经典实例，但这些数据被广泛用于分析关于传感器测量、个性化医学、社交网络、天文观测或任何其他显示内在时间结构的数据。该项目的主要目标是对期权数据进行降维，主要目标是提取基础工具的风险因素，从而潜在地增强现有的风险模型，无论是统计上的还是基于基本面的，学者和从业者都感兴趣。随后的下游任务可能包括检测预示即将发生或正在发生的市场变化的异常情况。一旦确定了低维结构概要的相关时间序列，就将探索各种最先进的异常检测和变点分析技术。还将执行针对时间序列数据设计的社区检测算法，提供各种工具的聚类，这往往与基础工具的传统行业分类有很大不同。线性降维方法，如主成分分析(PCA)，已被广泛用于研究金融市场。在过去的十年中，还对金融指标的数据集使用了非线性方法，以捕捉时间序列的低维表示，目的是预测或检测系统结构变化的早期迹象。然而，据我们所知，还没有基于期权市场数据的这样的研究。稳健地估计期权和股票市场的内在维度，并确定市场制度的变化，将是从业者和监管者同等感兴趣的事情。加强风险模型，提高预测流量算法的准确性，对于从业者，特别是做市商来说，是非常重要的任务。另一方面，政策制定者感兴趣的是更好地理解系统性风险，系统性风险被解释为整个金融体系崩溃的风险，以及可能对整个经济产生影响的连锁行为。对于政策制定者来说，一个相关的问题是，通过密切跟踪支配市场动态的各种风险因素，能否发现压力或即将到来的危机的早期信号。该项目与以下主题保持一致：(1)人工智能技术，(2)统计和应用概率，(3)非线性系统，(4)运筹学。

This project focuses on the statistical analysis of the options market (financial instruments that are derivatives based on the values of underlying securities), by leveraging tools from the literature of machine learning and network analysis.The data set used contains data for options transactions including price and volume information, broken down by market participant. The project fits well into the Information and communication technologies (ICT) research area; such a data set could not only offer itself for a myriad of potential applications but also provides motivation for the development or augmentation of existing techniques for analysis of large time-series data. While financial data are a classic instance of time series, these are widely used across various disciplines to analyse data regarding sensor measurements, personalised medicine, social networks, astronomical observations, or any other data which display an intrinsic temporal structure.A main objective of the project is to perform dimension reduction on the options data with the primary goal of extracting risk factors for the underlying instruments, thus potentially augmenting existing risk models, either statistical or based on fundamentals, of interest to both academics and practitioners. Subsequent downstream tasks could include the detection of anomalies that signal upcoming or undergoing market shifts. Once the relevant time series of low-dimensional structural summaries have been identified, various state-of-the-art techniques for anomaly detection and change-point analysis will be explored. Community detection algorithms designed for time series data will also be performed, providing a clustering of the various instruments, which often differs significantly from the traditional sectorial classifications of the underlying instruments.Linear dimensionality reduction methods, such as Principal Component Analysis (PCA), have been used extensively to study financial markets. In the last decade, nonlinear methods have also been used on data sets of financial indicators to capture low-dimensional representations of time series, for the purpose of prediction or detection of early signs of structural changes in the system. However, to the best of our knowledge, there has not yet been such a study based on the option market data. Robust estimation of the intrinsic dimensionality in the options and equity markets, and identification of shifts in the market regimes, would be of equal interest to both practitioners and regulators. Enhancing risk models, and improving the accuracy of algorithms predicting volume flow, are tasks of major importance for practitioners, especially market makers. On the other hand, policymakers are interested in a better understanding of systemic risk, construed as the risk of breakdown of the entire financial system and cascading behaviour that can have repercussions on the entire economy. A question of relevance for policymakers is whether, by closely tracking the various risk factors that govern the dynamics of the market, one can detect early signals of stress or upcoming crises.The project is aligned with the following topics: (1) Artificial intelligence technologies, (2) statistics and applied probability, (3) non-linear systems, (4) operational research.