Deep Learning from Implied Volatility Surfaces In this paper, we leverage the progress in deep learning for computer vision and image recognition to construct powerful, non-linear, local features of IV surfaces To this end, we exploit a particular neural network architecture called convolutional neural networks (CNNs)
Deep Smoothing of the Implied Volatility Surface - NeurIPS We present a neural network (NN) approach to fit and predict implied volatility surfaces (IVSs) Atypically to standard NN applications, financial industry prac-titioners use such models equally to replicate market prices and to value other financial instruments In other words, low training losses are as important as generalization capabilities
Implementing Deep Smoothing for Implied Volatility Surfaces . . . Deep smoothing focuses on applying deep learning methods to generate smooth, arbitrage-free implied volatility surfaces For someone unfamiliar with quantitative finance, this problem can be summarized as follows: Imagine you are given a set of points \ ( (k, \tau, iv)\) representing market data
Deep Learning from Implied Volatility Surfaces - SSRN We develop a novel methodology for extracting information from option implied volatility (IV) surfaces for the cross-section of stock returns, using image recognition techniques from machine learning (ML)
Deep smoothing of the implied volatility surface 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 models equally to replicate market prices and to value other financial instruments
N°23-60: Deep Learning from Implied Volatility Surfaces - s Fi We develop a novel methodology for extracting information from option implied volatility (IV) surfaces for the cross-section of stock returns, using image recognition techniques from machine learning (ML)