On Calibration Neural Networks for extracting implied information from American options

TL;DR

This paper introduces a neural network-based calibration method to efficiently extract implied volatility and dividends from American options, significantly reducing computational costs compared to traditional iterative approaches.

Contribution

The paper presents the Calibration Neural Network (CaNN), a novel machine learning framework for simultaneous estimation of implied volatility and dividend yield from American options.

Findings

Neural networks can accurately estimate implied volatility and dividends.

The CaNN framework reduces computational time for parameter extraction.

Machine learning provides a robust alternative to iterative calibration methods.

Abstract

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.