Machine Learning and Network Analysis in the Options Market

该项目通过利用机器学习和网络分析文献中的工具，专注于期权市场（基于基础证券价值的衍生金融工具）的统计分析。使用的数据集包含期权交易的数据，包括价格和数量信息，按市场参与者分列。该项目非常适合信息和通信技术（ICT）研究领域；这样的数据集不仅可以提供无数潜在的应用，而且还可以为开发或增强现有的大型时间序列数据分析技术提供动力。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各种学科，以分析有关传感器测量、个性化医疗、社交网络、天文观测或任何其他显示内在时间结构的数据。该项目的一个主要目标是对期权数据进行降维，主要目标是提取潜在工具的风险因素，从而潜在地增强现有的风险模型，无论是统计的还是基于基本面的，都是学术界和实践者感兴趣的。随后的下游任务可能包括检测即将到来或正在发生的市场变化的异常信号。一旦确定了低维结构摘要的相关时间序列，将探索各种最先进的异常检测和变化点分析技术。还将执行为时间序列数据设计的社区检测算法，提供各种工具的聚类，这通常与基础工具的传统部门分类有很大不同。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。在过去十年中，为了预测或发现系统结构变化的早期迹象，还对财务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。然而，据我们所知，目前还没有基于期权市场数据进行这样的研究。对期权和股票市场内在维度的可靠估计，以及对市场机制变化的识别，将对从业人员和监管机构都有同样的兴趣。加强风险模型，提高预测交易量的算法的准确性，是从业者，特别是做市商的重要任务。另一方面，政策制定者对更好地理解系统性风险感兴趣，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。对政策制定者来说，一个相关的问题是，通过密切跟踪控制市场动态的各种风险因素，人们能否发现压力或即将到来的危机的早期信号。该项目与以下主题一致：(1)人工智能技术，(2)统计与应用概率，(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.