Approximation rates for deep calibration of (rough) stochastic volatility models

Numerical methods (including Monte Carlo methods) (91G60) Artificial neural networks and deep learning (68T07) Derivative securities (option pricing, hedging, etc.) (91G20) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35)

Cites Work

The pricing of options and corporate liabilities
Selected aspects of fractional Brownian motion.
Perfect hedging in rough Heston models
Multilayer feedforward networks are universal approximators
Generalized autoregressive conditional heteroscedasticity
Approximation properties of a multilayered feedforward artificial neural network
A neural network-based framework for financial model calibration
DNN expression rate analysis of high-dimensional PDEs: application to option pricing
A theoretical analysis of deep neural networks and parametric PDEs
A rough SABR formula
Optimal approximation of piecewise smooth functions using deep ReLU neural networks
Deep ReLU network expression rates for option prices in high-dimensional, exponential Lévy models
Precise asymptotics: robust stochastic volatility models
Error bounds for approximations with deep ReLU networks
On the martingale property in the rough Bergomi model
Stochastic calculus with respect to fractional Brownian motion
Moment explosions in stochastic volatility models
An overview on deep learning-based approximation methods for partial differential equations
Deep vs. shallow networks: an approximation theory perspective
Lévy Processes and Stochastic Calculus
Universal approximation bounds for superpositions of a sigmoidal function
Volatility is rough
Pathwise large deviations for the rough Bergomi model
Pricing under rough volatility
Deep Neural Network Approximation Theory
Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models
Optimal Approximation with Sparsely Connected Deep Neural Networks
Empirical analysis of rough and classical stochastic volatility models to the SPX and VIX markets
Deep ReLU networks and high-order finite element methods
Error bounds for approximations with deep ReLU neural networks in Ws,p norms
Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems
Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations
A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
Pricing Interest-Rate-Derivative Securities
Stock Price Distributions with Stochastic Volatility: An Analytic Approach
Stochastic Calculus for Fractional Brownian Motion and Applications
The characteristic function of rough Heston models
Deep ReLU neural networks overcome the curse of dimensionality for partial integrodifferential equations
A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations
Approximation by superpositions of a sigmoidal function
Hybrid scheme for Brownian semistationary processes

This page was built for publication: Approximation rates for deep calibration of (rough) stochastic volatility models

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6606848)