Theory and methods in mathematical and computational finance

数学和计算金融的理论和方法

• 批准号: RGPIN-2021-04112
• 负责人: Hyndman, Cody
• 金额: $ 1.53万
• 依托单位: Concordia 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=759001
• 关键词: Theory; methods; mathematical; computational; finance

The focus of the proposed research program is on fundamental problems, stochastic modelling, and statistical learning methods in mathematical and computational finance. The solution of many theoretical and applied problems in mathematical and computational finance often requires the creation of new stochastic modelling approaches and statistical methodology. The research program considers the development of new statistical and machine learning methods that incorporate underlying geometric properties of models and that are reflected in improved performance when applied to nonlinear data.  A long-term objective is to develop of a flexible new meta-algorithm, called non-Euclidean upgrading, that embeds universal approximation properties into a wide variety of classical statistical techniques and machine learning algorithms. The research program also includes the development of new stochastic factor models for the term-structure of interest rates, credit-risk, volatility that are arbitrage-free and better capture the observed nonlinear features of the data. We shall also investigate new approaches to the theory, applications, and numerical solution of forward-backward stochastic differential equations. The expected outcomes of this program of research are novel methods in statistical learning, machine learning algorithms, and quantitative finance that are applicable to real data.  The efficiency and competitiveness of the Canadian financial sector depends on the availability of a large and diverse pool of highly qualified personnel with training in quantitative finance, data-driven research, statistical theory, machine learning algorithms, and relevant industrial experience. The research program will train highly qualified personal on innovative methods and important applications and allow them to enter industrial or academic careers with the necessary skills.

关于基本问题，随机建模和统计学习方法的拟议研究计划的重点当应用于非线性数据时，会反映出一个长期目标。利益的术语结构是无套利的，是数据的套利和更好的非线性特征。 FE加拿大的财务取决于量化量的高度合格人员，并通过定量财务培训ATA驱动的研究，统计理论，机器学习算法和相关的工业经验。以及重要的应用程序，并允许他们进入或具有必要技能的学术职业。