基于张量流的Dupire局部波动模型的灵敏度分析
Sensitivity Analysis in the Dupire Local Volatility Model with Tensorflow
摘要：
在最近的一篇论文中，我们展示了TPU和多维金融模拟之间的密切关系如何导致快速Monte Carlo模拟，这些模拟可以在几行Python Tensorflow代码中设置。我们还展示了使用自动微分语言（如Tensorflow）编写高性能仿真的主要好处：只需一行代码，我们就可以估计敏感度，即金融工具价格相对于另一个输入（如利率、当前标的价格或波动率）的变化率。这种敏感性（也被称为著名的金融“希腊人”）是风险评估和风险缓解的基础。在这篇后续短文中，我们扩展了我们之前关于Tensor Processing Units和Tensorflow用于TPU的工作中所暴露的发展。
英文摘要：
In a recent paper, we have demonstrated how the affinity between TPUs and multi-dimensional financial simulation resulted in fast Monte Carlo simulations that could be setup in a few lines of python Tensorflow code. We also presented a major benefit from writing high performance simulations in an automated differentiation language such as Tensorflow: a single line of code enabled us to estimate sensitivities, i.e. the rate of change in price of financial instrument with respect to another input such as the interest rate, the current price of the underlying, or volatility. Such sensitivities (otherwise known as the famous financial "Greeks") are fundamental for risk assessment and risk mitigation. In the present follow-up short paper, we extend the developments exposed in our previous work about the use of Tensor Processing Units and Tensorflow for TPUs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
