On Calibration Neural Networks for extracting implied information from American options
This work addresses a domain-specific problem for financial practitioners by providing an efficient numerical technique to handle the inverse calibration problem for American options, though it is incremental as it applies existing neural network methods to a known bottleneck in finance.
The paper tackles the computational challenge of extracting implied volatility and dividend yield from American option prices by introducing a data-driven machine learning approach, specifically a Calibration Neural Network (CaNN), which achieves fast and robust estimation by decoupling offline training and online prediction phases.
Extracting implied information, like volatility and/or dividend, from observed option prices is a challenging task when dealing with American options, because of the computational costs needed to solve the corresponding mathematical problem many thousands of times. We will employ a data-driven machine learning approach to estimate the Black-Scholes implied volatility and the dividend yield for American options in a fast and robust way. To determine the implied volatility, the inverse function is approximated by an artificial neural network on the computational domain of interest, which decouples the offline (training) and online (prediction) phases and thus eliminates the need for an iterative process. For the implied dividend yield, we formulate the inverse problem as a calibration problem and determine simultaneously the implied volatility and dividend yield. For this, a generic and robust calibration framework, the Calibration Neural Network (CaNN), is introduced to estimate multiple parameters. It is shown that machine learning can be used as an efficient numerical technique to extract implied information from American options.