Large-scale Application of Automatic Differentiation in Computational Finance (and beyond)

自动微分在计算金融（及其他领域）中的大规模应用

• 批准号: RGPIN-2017-03860
• 负责人: Coleman, Thomas
• 金额: $ 2.48万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635128
• 关键词: Large; scale; Application; Automatic; Differentiation

The field of Automatic Differentiation (AD) has taken great strides in recent years. Nevertheless, AD is not heavily used for large-scale problems often due to efficiency concerns. This is true in many application areas, including economics and finance. For example, large portfolios of variable annuities can take many hours to evaluate by standard methods; hedging, typically requiring the determination of derivatives, would require significantly more time. Clearly, any rapid hedging methods based on derivatives for such portfolios is problematic. This poses a serious challenge for effective risk management. Similar challenges exist for other portfolios (e.g., Credit Value Adjustment (CVA’s)).

近年来，自动微分（AD）领域取得了长足的进步。然而，AD通常由于效率问题而不被大量用于大规模问题。这在许多应用领域都是如此，包括经济和金融。例如，用标准方法评估大型可变年金投资组合可能需要很多时间;对冲通常需要确定衍生工具，需要的时间要多得多。显然，任何基于衍生品的快速对冲方法都存在问题。这对有效的风险管理提出了严峻挑战。其他投资组合也存在类似的挑战（例如，信用价值调整（CVA））。