论文标题
牛顿·拉夫森（Newton Raphson）仿真网络，用于高效计算众多隐含波动
Newton Raphson Emulation Network for Highly Efficient Computation of Numerous Implied Volatilities
论文作者
论文摘要
在金融中，隐含的波动是立即反映市场状况的重要指标。许多从业者使用迭代方法（例如牛顿 - 拉夫森（NR）方法）估算波动率。但是，如果必须经常计算大量隐含的波动，则迭代方法很容易达到处理速度限制。因此，我们使用Pytorch（一种众所周知的深度学习软件包）将NR方法效仿为网络，并使用TensOrrt（用于优化深度学习模型的软件包进一步优化网络。将优化的仿真方法与NR方法的流行实现中的NR函数进行比较，我们证明了仿真网络的速度比基准函数快1000倍。
In finance, implied volatility is an important indicator that reflects the market situation immediately. Many practitioners estimate volatility using iteration methods, such as the Newton--Raphson (NR) method. However, if numerous implied volatilities must be computed frequently, the iteration methods easily reach the processing speed limit. Therefore, we emulate the NR method as a network using PyTorch, a well-known deep learning package, and optimize the network further using TensorRT, a package for optimizing deep learning models. Comparing the optimized emulation method with the NR function in SciPy, a popular implementation of the NR method, we demonstrate that the emulation network is up to 1,000 times faster than the benchmark function.
