# Deep calibration of rough stochastic volatility models

@article{Bayer2018DeepCO, title={Deep calibration of rough stochastic volatility models}, author={Christian Bayer and Benjamin Stemper}, journal={ArXiv}, year={2018}, volume={abs/1810.03399} }

Techniques from deep learning play a more and more important role for the important task of calibration of financial models. The pioneering paper by Hernandez [Risk, 2017] was a catalyst for resurfacing interest in research in this area. In this paper we advocate an alternative (two-step) approach using deep learning techniques solely to learn the pricing map -- from model parameters to prices or implied volatilities -- rather than directly the calibrated model parameters as a function of…

## 50 Citations

Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models

- Computer Science
- 2020

A neural network-based calibration method that performs the calibration task within a few milliseconds for the full implied volatility surface, and stochastic volatility models (classical and rough) can be handled in great generality as the framework also allows taking the forward variance curve as an input.

Deep Learning Volatility

- Computer ScienceSSRN Electronic Journal
- 2019

A neural network based calibration method that performs the calibration task within a few milliseconds for the full implied volatility surface and brings several numerical pricers and model families within the scope of applicability in industry practice.

Fast direct calibration of interest rate derivatives pricing models

- EconomicsICAIF
- 2020

A Neural Network based calibration of a pricing model, where learning is directly performed on market data by using a non-trivial loss function, which includes the financial model adopted, is proposed.

Accuracy of deep learning in calibrating HJM forward curves

- Economics
- 2020

A new class of volatility operators is introduced which map the square integrable noise into the Filipovi\'{c} space of forward curves, and a deterministic parametrized version of it is specified.

A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models

- Computer ScienceRisks
- 2020

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, we…

Interpretability in deep learning for finance: a case study for the Heston model

- Computer ScienceArXiv
- 2021

This paper focuses on the calibration process of a stochastic volatility model, a subject recently tackled by deep learning algorithms, and finds that global strategies such as Shapley values can be effectively used in practice.

Tensoring Volatility Calibration

- Computer Science
- 2020

Tests indicate that when using Chebyshev Tensors, the calibration of the rough Bergomi volatility model is around 40,000 times more efficient than if calibrated via brute-force (using the pricing function).

Deep Option Pricing - Term Structure Models

- Computer ScienceSSRN Electronic Journal
- 2019

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options within the setting of interest rate term structure models. This aims to…

Tensoring volatility calibration Calibration of the rough Bergomi volatility model via Chebyshev Tensors

- Computer Science
- 2020

Tests indicate that when using Chebyshev Tensors, the calibration of the rough Bergomi volatility model is around 40,000 times more efficient than if calibrated via brute-force (using the pricing function).

Deep calibration of the quadratic rough Heston model

- Computer Science, Economics
- 2021

The quadratic rough Heston model provides a natural way to encode Zumbach eﬀect in the rough volatility paradigm. We apply multi-factor approximation and use deep learning methods to build an eﬃcient…

## References

SHOWING 1-10 OF 88 REFERENCES

Deep Learning Volatility

- Computer ScienceSSRN Electronic Journal
- 2019

A neural network based calibration method that performs the calibration task within a few milliseconds for the full implied volatility surface and brings several numerical pricers and model families within the scope of applicability in industry practice.

A neural network-based framework for financial model calibration

- Computer ScienceJournal of Mathematics in Industry
- 2019

The rapid on-line learning of implied volatility by ANNs, in combination with the use of an adapted parallel global optimization method, tackles the computation bottleneck and provides a fast and reliable technique for calibrating model parameters while avoiding, as much as possible, getting stuck in local minima.

Machine learning for quantitative finance: fast derivative pricing, hedging and fitting

- Computer ScienceQuantitative Finance
- 2018

It is illustrated that for many classical problems, the price of extra speed is some loss of accuracy, but this reduced accuracy is often well within reasonable limits and hence very acceptable from a practical point of view.

Volatility Model Calibration With Convolutional Neural Networks

- Computer Science
- 2018

We use a supervised deep convolution neural network to replicate the calibration of the Heston model to equity volatility surfaces. For this purpose we treat the implied volatility surface together…

Unbiased deep solvers for parametric PDEs

- Computer Science
- 2018

Several deep learning algorithms for approximating families of parametric PDE solutions are developed that are robust with respect to quality of the neural network approximation and consequently can be used as a black-box in case only limited a priori information about the underlying problem is available.

Following the Bayes path to option pricing

- Economics
- 1998

Conventional modeling techniques for option pricing have systematic biases resulting from the assumption of constant volatility (homoscedasticity ) for the price of the underlying asset.…

Turbocharging Monte Carlo pricing for the rough Bergomi model

- Economics
- 2017

The rough Bergomi model, introduced by Bayer et al. [Quant. Finance, 2016, 16(6), 887–904], is one of the recent rough volatility models that are consistent with the stylised fact of implied…

Calibrating rough volatility models: a convolutional neural network approach

- Computer ScienceQuantitative Finance
- 2019

In this paper, we use convolutional neural networks to find the Hölder exponent of simulated sample paths of the rBergomi model, a recently proposed stock price model used in mathematical finance. We…

Deeply Learning Derivatives

- Computer Science
- 2018

The deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models, and is broadly applicable to value derivatives.

Low-Rank Tensor Approximation for Chebyshev Interpolation in Parametric Option Pricing

- Computer ScienceSIAM J. Financial Math.
- 2020

The core of the method is to express the tensorized interpolation in tensor train (TT) format and to develop an efficient way, based on tensor completion, to approximate the interpolation coefficients.