Efficient Calibration in the rough Bergomi model by Wasserstein distance

Despite the empirical success in modeling volatility of the rough Bergomi (rBergomi) model, it suffers from pricing and calibration difficulties stemming from its non-Markovian structure. To address this, we propose a comprehensive computational framework that enhances both simulation and calibration. First, we develop a modified Sum-of-Exponentials (mSOE) Monte Carlo scheme which hybridizes an exact simulation of the singular kernel near the origin with a multi-factor approximation for the remainder. This method achieves high accuracy, particularly for out-of-the-money options, with an $\mathcal{O}(n)$ computational cost. Second, based on this efficient pricing engine, we then propose a distribution-matching calibration scheme by using Wasserstein distance as the optimization objective. This leverages a minimax formulation against Lipschitz payoffs, which effectively distributes pricing errors and improving robustness. Our numerical results confirm the mSOE scheme's convergence and demonstrate that the calibration algorithm reliably identifies model parameters and generalizes well to path-dependent options, which offers a powerful and generic tool for practical model fitting.

• Publication year

  2025

• Venue

  Unknown venue

• Publication date

  2025-11-29

• Fields of study

  Mathematics, Business, Computer Science, Economics

• Identifiers
  arXiv 2512.00448
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Weak Markovian Approximations of Rough Heston

  2023cited by this paper
• Deep calibration with random grids

  2023cited by this paper
• Learning Fractional White Noises in Neural Stochastic Differential Equations

  2022cited by this paper
• Neural SDEs as Infinite-Dimensional GANs

  2021cited by this paper
• Why the 1-Wasserstein distance is the area between the two marginal CDFs

  2021influential reference
• Markovian approximations of stochastic Volterra equations with the fractional kernel

  2021influential reference
• Neural Options Pricing

  2021cited by this paper
• Markovian Approximation of the Rough Bergomi Model for Monte Carlo Option Pricing

  2020cited by this paper
• The Hybrid-Exponential Scheme for Stochastic Volterra Equations

  2020influential reference
• A neural network-based framework for financial model calibration

  2019cited by this paper
• Strong convergence rates for Markovian representations of fractional Brownian motion

  2019influential reference
• Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models

  2019cited by this paper
• Risk-Neutral Densities: A Review

  2018cited by this paper
• Multifactor Approximation of Rough Volatility Models

  2018cited by this paper
• On the martingale property in the rough Bergomi model

  2018influential reference
• Gaussian stochastic volatility models: Scaling regimes, large deviations, and moment explosions

  2018influential reference
• TensorFlow: A system for large-scale machine learning

  2016cited by this paper
• Fast evaluation of the Caputo fractional derivative and its applications to fractional diffusion equations

  2015influential reference
• Pricing under rough volatility

  2015influential reference
• Hybrid scheme for Brownian semistationary processes

  2015cited by this paper
• Volatility is rough

  2014cited by this paper
• Topics in Optimal Transportation

  2003cited by this paper
• Parsimonious modeling of yield curves

  1987cited by this paper
• Nonlinear Approximation Theory

  1986influential reference

• Unsupervised Learning-based Calibration Scheme for Rough Volatility Models

  2024influential citation