  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.
