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Extracting implied information, like volatility and/or dividend, fromobserved option prices is a challenging task when dealing with Americanoptions, because of the computational costs needed to solve the correspondingmathematical problem many thousands of times. We will employ a data-drivenmachine learning approach to estimate the Black-Scholes implied volatility andthe dividend yield for American options in a fast and robust way. To determinethe implied volatility, the inverse function is approximated by an artificialneural network on the computational domain of interest, which decouples theoffline (training) and online (prediction) phases and thus eliminates the needfor an iterative process. For the implied dividend yield, we formulate theinverse problem as a calibration problem and determine simultaneously theimplied volatility and dividend yield. For this, a generic and robustcalibration framework, the Calibration Neural Network (CaNN), is introduced toestimate multiple parameters. It is shown that machine learning can be used asan efficient numerical technique to extract implied information from Americanoptions.

On Calibration Neural Networks for extracting implied information from American options