Data-driven methods for timeseries modelling across asset classes

跨资产类别时间序列建模的数据驱动方法

• 批准号: 2740734
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2740734
• 关键词: Data; driven; methods; timeseries; modelling

My current research sits at the intersection between engineering and mathematical sciences, aiming to improve the modelling of cross-asset market microstructure in equity and exchange traded fund (ETF) markets. This multidisciplinary approach leverages techniques from modern statistics and machine learning to obtain insights from multi-terabyte scale, low signal to noise timeseries datasets. This research will provide the first systematic analysis of trade co-occurrence between equities and ETFs. Trade co-occurrence captures the information content of trades occurring in a short time proximity of each other. By performing the first systematic analysis of trade co-occurrence between equities and ETFs, we aim to address fundamental questions in market microstructure such as identifying arbitrage flow and detecting lead-lag relationships. The practical applications of this research are of interest to both regulators as well as many other market participants such as high frequency trading firms (HFTs), market markets (MM) and hedge funds. For instance, the understanding of price formation mechanisms is vital to regulators who seek to understand the flow of information and systemic risk within a market. Analysis of lead-lag relationships is extremely important to market participants such as HFTs and MMs who seek to optimally provide liquidity across a broad set of asset classes.To maximise the impact of my research, I am collaborating with senior quantitative researchers from Man Group. Man Group is the world's largest publicly traded hedge fund, and this partnership will ensure that our research objectives and methodologies align with the practical needs of industry. The technical expertise and guidance from industry practitioners will allow us to maximise our research impact.This research offers multiple avenues for further exploration. The methodology is general enough to be adapted to other asset classes such as options and futures. We aim to extend our framework to go beyond pairwise interactions in asset classes and move to a more general graph-based framework.This research aligns with the EPSRC's objectives of fostering innovation in mathematical sciences and engineering. By leveraging innovative statistical methods and machine learning, we aim to advance understanding in market microstructure across asset classes. The collaboration with industry stakeholders like Man Group underscores its relevance to real-world challenges, fulfilling the EPSRC's mandate for impactful and practical research.

我目前的研究位于工程和数学科学之间的交叉点，旨在改善股票和交易所交易基金（ETF）市场中跨资产市场微观结构的建模。这种多学科方法利用现代统计学和机器学习技术，从多TB规模、低信噪比的时间序列数据集中获得见解。这项研究将首次系统地分析股票和ETF之间的交易共现。交易同现捕捉在短时间内彼此接近的交易的信息内容。通过对股票和ETF之间的交易共现进行首次系统分析，我们旨在解决市场微观结构中的基本问题，例如识别套利流和检测领先滞后关系。这项研究的实际应用是感兴趣的监管机构以及许多其他市场参与者，如高频交易公司（HFT），市场市场（MM）和对冲基金。例如，对价格形成机制的理解对于寻求了解市场内信息流动和系统性风险的监管机构至关重要。领先-滞后关系分析对于市场参与者（如HFT和Risk）来说极其重要，他们寻求在广泛的资产类别中提供最佳的流动性。为了最大限度地发挥我的研究影响，我与曼氏集团的高级定量研究人员合作。曼氏集团是全球最大的上市对冲基金，此次合作将确保我们的研究目标和方法与行业的实际需求保持一致。来自行业从业者的技术专长和指导将使我们能够最大限度地发挥研究影响力。这项研究为进一步探索提供了多种途径。该方法是通用的，足以适应其他资产类别，如期权和期货。我们的目标是扩展我们的框架，超越资产类别中的成对相互作用，并转向更通用的基于图形的框架。这项研究符合EPSRC促进数学科学和工程创新的目标。通过利用创新的统计方法和机器学习，我们的目标是促进对不同资产类别市场微观结构的理解。与曼氏集团等行业利益相关者的合作强调了其与现实世界挑战的相关性，履行了EPSRC进行有影响力和实用研究的任务。