Neural networks have been used as a nonparametric method for option pricing and hedging
since the early 1990s. Far over a hundred papers have been published on this topic. This …

A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate
financial asset price models using an Artificial Neural Network (ANN). Determining optimal …

In recent years, with the breakthrough of big data and deep learning technology in various
fields, many scholars have begun to study the stock market time series by using deep …

We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models,
circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this …

In order to conduct an in-depth study on financial transactions of block chain, the classical
back propagation (BP) neural network based on the artificial neural network (ANN) model is …

This paper examines the option pricing performance of the most popular Machine Learning
algorithms. The classic parametrical models suffer from several limitations in term of …

The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives
exchanges mark the beginning of a new era in Bitcoin price risk hedging. The need for these …

Purpose The purpose of this paper is to supplement and update previously published
articles about artificial intelligence (AI) instruments and operations in banking sectors with …

We present a neural network (NN) approach to fit and predict implied volatility surfaces
(IVSs). Atypically to standard NN applications, financial industry practitioners use such …

We propose, formalise and analyse the deep parametric PDE method to solve high-
dimensional parametric partial differential equations with a focus on financial applications. A …