论文标题
用张量的dupire局部波动率模型中的灵敏度分析
Sensitivity Analysis in the Dupire Local Volatility Model with Tensorflow
论文作者
论文摘要
在最近的一篇论文中，我们证明了TPU和多维金融模拟之间的亲和力如何产生快速的蒙特卡洛模拟，这些模拟可以在几行Python Tensorflow代码中进行设置。我们还用自动化分化语言（例如Tensorflow：一行代码，使我们能够估算敏感性，即金融工具的价格变化率相对于另一种投入，例如利率，当前价格的当前价格或波动性），我们还带来了重大好处。这种敏感性（也称为著名的财务“希腊人”）是风险评估和降低风险的基础。在当前的后续短论文中，我们扩展了先前有关使用张量处理单元和Tensorflow在TPU的工作中所揭示的发展。
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.
