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In finance, implied volatility is an important indicator that reflects themarket situation immediately. Many practitioners estimate volatility usingiteration methods, such as the Newton--Raphson (NR) method. However, ifnumerous implied volatilities must be computed frequently, the iterationmethods easily reach the processing speed limit. Therefore, we emulate the NRmethod as a network using PyTorch, a well-known deep learning package, andoptimize the network further using TensorRT, a package for optimizing deeplearning models. Comparing the optimized emulation method with the NR functionin SciPy, a popular implementation of the NR method, we demonstrate that theemulation network is up to 1,000 times faster than the benchmark function.

Newton Raphson Emulation Network for Highly Efficient Computation of Numerous Implied Volatilities