Deep calibration of financial models: turning theory into practice

DOI10.1007/S11147-021-09183-7zbMath1495.91120OpenAlexW3195987645MaRDI QIDQ2165392

Maximilian Nagl, Daniel Rösch, Patrick Büchel, Michael Kratochwil

Publication date: 19 August 2022

Published in: Review of Derivatives Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11147-021-09183-7

zbMATH Keywords

derivativesmodel calibrationdeep learningglobal optimizerinterest rate term structure

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (1)

On a Neural Network to Extract Implied Information from American Options

Cites Work

• Unnamed Item
• Unnamed Item
• Stochastic duration and fast coupon bond option pricing in multi-factor models
• Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
• A neural network-based framework for financial model calibration
• An Algorithm for Least-Squares Estimation of Nonlinear Parameters
• Bond Pricing and the Term Structure of Interest Rates: A New Methodology for Contingent Claims Valuation
• Calibrating rough volatility models: a convolutional neural network approach
• Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models
• A method for the solution of certain non-linear problems in least squares
• Approximation by superpositions of a sigmoidal function

This page was built for publication: Deep calibration of financial models: turning theory into practice