Sparse grid method for highly efficient computation of exposures for xVA
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Publication:2168601
DOI10.1016/j.amc.2022.127446OpenAlexW3167804879MaRDI QIDQ2168601
Publication date: 26 August 2022
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2104.14319
xVAChebyshev polynomialsstochastic collocationClenshaw-Curtisexpected exposuresSCSmolyak's sparse gridsvaluation adjustment
Numerical approximation and computational geometry (primarily algorithms) (65Dxx) Actuarial science and mathematical finance (91Gxx) Approximations and expansions (41Axx)
Cites Work
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- A method for numerical integration on an automatic computer
- Chebyshev interpolation for parametric option pricing
- Smolyak's algorithm: a powerful black box for the acceleration of scientific computations
- High dimensional polynomial interpolation on sparse grids
- Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain
- A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options
- XVA PRINCIPLES, NESTED MONTE CARLO STRATEGIES, AND GPU OPTIMIZATIONS
- Speed-up credit exposure calculations for pricing and risk management
- XVA analysis from the balance sheet
- Low-Rank Tensor Approximation for Chebyshev Interpolation in Parametric Option Pricing
- Mathematical Modeling and Computation in Finance
- The stochastic collocation Monte Carlo sampler: highly efficient sampling from ‘expensive’ distributions
- On Cross-Currency Models with Stochastic Volatility and Correlated Interest Rates
- Is Gauss Quadrature Better than Clenshaw–Curtis?
- High-Order Collocation Methods for Differential Equations with Random Inputs
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