Mathematical Challenges in Financial Risk Analysis

金融风险分析中的数学挑战

• 批准号: RGPIN-2018-05880
• 负责人: Ku, Hyejin
• 金额: $ 1.17万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759025
• 关键词: Mathematical; Challenges; Financial; Risk; Analysis

My research project consists of the conception of various risks, the measurement, the analysis, and the management of financial risks under unexpected circumstances. Mathematical modeling, analyzing, computing (simulations) approaches are required to solve the exciting and challenging research problems proposed in the application. In the current generation of data-rich environment, high-frequency trading algorithms and advanced learning techniques in financial markets become important issues. As a relatively new phenomenon, much of the discussion is not backed by solid academic research. They operate and improve market quality in usual times, but may stop operating and thus cause the prices to disappear (or clearly unreasonable traded prices) in extreme circumstances. Market stability also relates to the issue of liquidity risk in financial markets. At the time of stress, the high-frequency trading stops providing liquidity and becomes the demander of liquidity, the provision of liquidity seriously deteriorates, which can cause substantial volatility. This is explained by the fact that market participants react to immediate information, in other words, that noise creates noise. Both the models and the people who use them are lacking the ability to articulate the risk of the underlying and that related to the actual market. Models become more sophisticated, but the same logic has persisted. Today, it is crucial to develop new tools (and train professionals) for risk analysis, by reworking on the determination of the exposure to risk. It is more important than ever to explore new research approaches for facing current challenges, and this proposal reflects a big part of this new trend.

我的研究项目包括在意外情况下的各种风险，测量，分析和财务风险的管理。需要数学建模，分析，计算（模拟）方法来解决应用程序中提出的令人兴奋且具有挑战性的研究问题。在当前的数据丰富的环境中，金融市场中的高频交易算法和高级学习技术成为重要问题。作为一种相对较新的现象，许多讨论并未得到扎实的学术研究的支持。它们在通常的时间运营和提高市场质量，但可能会停止运营，从而导致价格在极端情况下消失（或明显不合理的交易价格）。市场稳定也与金融市场流动性风险问题有关。在压力时，高频交易停止提供流动性，并成为流动性的消失，提供的流动性严重恶化，这可能导致大量波动。这是通过市场参与者对噪音产生噪音的直接信息做出反应的事实来解释的。模型和使用它们的人都缺乏阐明基础风险以及与实际市场有关的能力。模型变得更加复杂，但是相同的逻辑也持续存在。如今，通过重新制定风险暴露，开发新工具（和培训专业人员）进行风险分析至关重要。探索面临当前挑战的新研究方法比以往任何时候都重要，该提案反映了这一新趋势的很大一部分。