Machine Learning and Network Analysis in the Options Market

期权市场中的机器学习和网络分析

• 批准号: 2444986
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2444986
• 关键词: Machine; Learning; Network; Analysis; Options

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.

本课题利用机器学习和网络分析等文献中的工具，对期权市场（以标的证券价值为基础的衍生金融工具）进行统计分析。所使用的数据集包含按市场参与者划分的价格和交易量信息等期权交易数据。该项目非常适合信息和通信技术（信通技术）研究领域;这样一个数据集不仅可以为无数潜在的应用提供自身，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，但这些数据广泛用于各个学科，以分析有关传感器测量、个性化医疗、社交网络、天文观测或显示内在时间结构的任何其他数据的数据。该项目的主要目标是对期权数据进行降维，其主要目标是提取基础工具的风险因素，从而有可能扩充学术界和从业者都感兴趣的现有风险模型，无论是统计模型还是基于基本原理的模型。随后的下游任务可能包括检测异常，这些异常表明市场即将发生或正在发生变化。一旦相关的时间序列的低维结构摘要已被确定，各种国家的最先进的技术异常检测和变点分析将进行探讨。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，已广泛用于研究金融市场。在过去的十年中，非线性方法也被用于金融指标的数据集，以捕获时间序列的低维表示，以预测或检测系统中结构变化的早期迹象。然而，据我们所知，还没有这样的研究基于期权市场的数据。对期权和股票市场内在维度的稳健估计，以及对市场机制变化的识别，对从业者和监管者都具有同等的兴趣。加强风险模型，提高预测流量的算法的准确性，是从业者，特别是做市商的重要任务。另一方面，决策者希望更好地了解系统性风险，即整个金融体系崩溃的风险和可能对整个经济产生影响的连锁反应。对于政策制定者来说，密切关注影响市场动态的各种风险因素，是否能够发现压力或即将到来的危机的早期信号，这是一个重要的问题。该项目与以下主题相一致：（1）人工智能技术，（2）统计学和应用概率，（3）非线性系统，（4）运筹学。