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

自动微分(AD)领域近年来取得了长足的进步。然而，由于效率方面的考虑，AD在处理大规模问题时并未大量使用。这在包括经济和金融在内的许多应用领域都是如此。例如，用标准方法评估可变年金的大型投资组合可能需要几个小时；对冲通常需要确定衍生品，需要的时间要长得多。显然，任何基于衍生品的此类投资组合的快速对冲方法都是有问题的。这对有效的风险管理提出了严峻的挑战。其他投资组合也面临类似的挑战(例如，信用价值调整(CVA))。

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)).