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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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336699132 · Jun 202019922001200920172026
48 results for Euler scheme

Paper develops Euler scheme for fractional delay diff. eqs with additive noise.

problem Developing a consistent Euler-Maruyama scheme for fractional stochastic delay diff. eqs.
method Euler-Maruyama scheme for fractional Brownian motion with additive noise.
result Achieved convergence rate of H+1/2 for smooth delays when H>1/2.

Develops multifactor approximations for SVEs with completely monotone kernels.

problem Approximating SVEs with kernels of completely monotone type.
method Multifactor approximation, Euler discretization, L2L^2-estimation, convergence analysis.
result New multifactor Euler scheme reduces computational cost and outperforms SVEs for option pricing.

This work proves a strong convergence result for a geometric EM scheme on Riemannian manifolds.

problem Convergence of numerical schemes for manifold-valued SDEs.
method Geometric Euler-Maruyama scheme for Riemannian manifolds.
result Strong convergence of order 1/2 for the geometric EM scheme on Riemannian manifolds.

In the present paper, we introduce a numerical scheme for the price of a barrier option when the price of the underlying follows a diffusion process. The numerical scheme is based on an extension of a static hedging formula of barrier options. For getting the static hedging formula, the underlying process needs to have…

2012-06-13abs ↗pdf ↗

Paper studies particle method for LSV model calibration, proving convergence and error bounds.

problem Calibration of local-stochastic volatility models with open well-posedness question.
method Regularized Euler--Maruyama scheme for particle approximation of McKean--Vlasov dynamics.
result Strong convergence of the Euler--Maruyama scheme with rate 1/2 in step-size.

Discrete time analogues of ergodic stochastic differential equations (SDEs) are one of the most popular and flexible tools for sampling high-dimensional probability measures. Non-asymptotic analysis in the L2L^2 Wasserstein distance of sampling algorithms based on Euler discretisations of SDEs has been recently develop…

2018-08-21abs ↗pdf ↗

The paper models financial asset prices with jumps and evaluates European option prices using numerical methods.

problem Modeling and pricing European options with jumps in delayed stochastic systems.
method Existence, uniqueness, and positivity of solutions to delayed stochastic differential equations with jumps. Application of Fourier transformation for analytical pricing and Monte-Carlo simulation with a logarithmic Euler-Maruyama scheme for numerical approximation.
result The logarithmic Euler-Maruyama scheme provides a positive and convergent method for approximating the solution to the delayed stochastic differential equations with jumps.

Ghost points affect stability in finite difference schemes for diffusion equations.

problem Impact of ghost points on stability of finite difference schemes.
method Exploration of explicit Euler finite difference scheme with ghost points on diffusion equation.
result Stability of the scheme is affected by ghost points.

Study simulates Heston-type local stochastic volatility model using particle method.

problem Simulate calibrated Heston-type local stochastic volatility model with non-standard coefficients.
method Monte Carlo particle method, Euler-Maruyama scheme, full truncation Euler scheme.
result Strong convergence of Euler-Maruyama scheme with rate 1/2 in time, up to a logarithmic factor.

New schemes improve error estimates for sampling from non-log-concave distributions.

problem Improving sampling from non-log-concave distributions with super-linear drift growth.
method Developed tamed Euler and randomized Euler schemes with error estimates.
result Near-optimal error bounds for sampling and optimization problems.

The paper presents two schemes for sampling matrices from specific distributions on a manifold.

problem Sampling matrices from Gibbs distributions on the manifold of positive semi-definite matrices with fixed rank.
method Two explicit schemes based on Euler-Maruyama discretization of the Riemannian Langevin equation with Brownian motion on the manifold.
result Numerical validation of the schemes using specific energy functions and metrics.

New algorithms sample from log concave distributions without gradient Lipschitz continuity.

problem Sampling from log concave distributions without gradient Lipschitz continuity.
method Two algorithms based on monotone polygonal (tamed) Euler schemes.
result Non-asymptotic 2-Wasserstein distance bounds between the process and target measure.

New time series generation models improve accuracy and correlation identification.

problem Generating accurate and correlated time series from limited data.
method Conditional Euler Generator (CEGEN) using Euler discretization of SDEs and Wasserstein metrics.
result CEGEN outperforms state-of-the-art models on various metrics and real-world datasets.

Quantization techniques have been applied in many challenging finance applications, including pricing claims with path dependence and early exercise features, stochastic optimal control, filtering problems and efficient calibration of large derivative books. Recursive Marginal Quantization of the Euler scheme has recen…

2017-01-06abs ↗pdf ↗

Asymptotic error distribution for approximation of a stochastic integral with respect to continuous semimartingale by Riemann sum with general stochastic partition is studied. Effective discretization schemes of which asymptotic conditional mean-squared error attains a lower bound are constructed. Two applications are …

2010-04-13abs ↗pdf ↗

Developed unbiased estimators for Heston model with stochastic interest rates.

problem Estimating the Heston model with stochastic interest rates.
method Combined unbiased estimators with the Heston model and developed a semi-exact log-Euler scheme.
result Convergence rate of O(h)O(h) in the L2L^2 norm for a wide range of models.

Study approximates weak error for specific stochastic models with rough and Gaussian mean-reverting volatility.

problem Approximating weak error for specific stochastic models with rough and Gaussian mean-reverting volatility.
method Used Euler type scheme with integrated kernels to study weak convergence rate.
result Obtained weak convergence rate of min(3α1,1)\min(3α-1,1) for discretised rough Ornstein-Uhlenbeck process and stochastic rough volatility model.

Enhances learning of structured distributions using nonlinear denoising score matching.

problem Learning structured distributions from noisy data.
method Latent Nonlinear Denoising Score Matching (LNDSM) integrating nonlinear dynamics with VAE-based latent score matching.
result LNDSM achieves superior sample quality and variability compared to structure-agnostic methods.

New method improves Euler approximation for local stochastic volatility models.

problem Well-posedness of Euler approximation for local stochastic volatility models.
method Start with a well-defined Euler approximation to the formal McKean-Vlasov equation, followed by a half-step scheme.
result Showed weak order one for the Euler discretization, plus error terms.

AES scheme improves Bermudan and American option pricing for Heston models.

problem Pricing Bermudan and American options under Heston models efficiently.
method AES scheme using non-central chi-square distribution for variance process.
result AES achieves higher accuracy and computational efficiency for Bermudan options.

In recent years, total variation (TV) and Euler's elastica (EE) have been successfully applied to image processing tasks such as denoising and inpainting. This paper investigates how to extend TV and EE to the supervised learning settings on high dimensional data. The supervised learning problem can be formulated as an…

2012-06-18abs ↗pdf ↗

A general, consistent and complete framework for geometrical formulation of mechanical systems is proposed, based on certain structures on affine bundles (affgebroids) that generalize Lie algebras and Lie algebroids. This scheme covers and unifies various geometrical approaches to mechanics in the Lagrangian and Hamilt…

2006-04-06abs ↗pdf ↗

In this paper we discuss the possibility of using multilevel Monte Carlo (MLMC) methods for weak approximation schemes. It turns out that by means of a simple coupling between consecutive time discretisation levels, one can achieve the same complexity gain as under the presence of a strong convergence. We exemplify thi…

2014-06-10abs ↗pdf ↗

We develop continuous time Markov chain (CTMC) approximation of one-dimensional diffusions with a lower sticky boundary. Approximate solutions to the action of the Feynman-Kac operator associated with a sticky diffusion and first passage probabilities are obtained using matrix exponentials. We show how to compute matri…

2019-10-31abs ↗pdf ↗

Aganagic and Shakirov propose a refinement of the SU(N) Chern-Simons theory for links in three manifolds with S^1-symmetry, such as torus knots in S^3, based on deformation of the S and T matrices, originally found by Kirillov and Cherednik. We relate the large N limit of the S matrix to the Hilbert schemes of points o…

2012-11-25abs ↗pdf ↗

Study on solutions to spinorial Yamabe equation on manifolds with boundary.

problem Existence of solutions to the spinorial Yamabe equation on compact manifolds with boundary.
method Iterative scheme combined with bootstrapping methods to establish existence under smallness assumptions.
result Existence of solutions established under smallness assumptions on parameters.

This paper uses Malliavin calculus to price and compute delta of financial derivatives in jump-diffusion models.

problem Pricing and delta computation of financial derivatives in jump-diffusion models with stochastic intensity.
method Utilizes Malliavin calculus to price and compute delta, applying the Euler scheme for convergence analysis.
result Established the convergence of approximated solution, financial derivative, and its delta Greeks.

We construct a categorification of the maximal commutative subalgebra of the type AA Hecke algebra. Specifically, we propose a monoidal functor from the (symmetric) monoidal category of coherent sheaves on the flag Hilbert scheme to the (non-symmetric) monoidal category of Soergel bimodules. The adjoint of this functo…

2016-08-25abs ↗pdf ↗

The purpose of this article is twofold. First we outline a general construction scheme for producing simply-connected minimal symplectic 4-manifolds with small Euler characteristics. Using this scheme, we illustrate how to obtain irreducible symplectic 4-manifolds homeomorphic but not diffeomorphic to $\CP#(2k+1)\CPb$

2007-03-16abs ↗pdf ↗

A risk of small defined-benefit pension schemes is that there are too few members to eliminate idiosyncratic mortality risk, that is there are too few members to effectively pool mortality risk. This means that when there are few members in the scheme, there is an increased risk of the liability value deviating signifi…

2011-07-07abs ↗pdf ↗

Efficiently simulates the Heston model with large time steps using a novel method.

problem Challenges in simulating the Heston model with large time steps.
method Implicit integrated variance scheme exploiting the near-linear nature between stochastic driver and conditional integrated variance process.
result Achieves near-exact accuracy with coarse discretizations, efficient for large time steps.

Paper analyzes Langevin dynamics for multimodal Gaussian mixtures, controlling errors across dimensions.

problem Challenges in obtaining stable diffusion-based samplers in high- and infinite-dimensional settings.
method Study of preconditioned Annealed Langevin Dynamics (ALD) for Gaussian mixtures, focusing on Euler-Maruyama (EM) and exponential-integrator schemes.
result Proves dimension-uniform KL bounds for the exponential-integrator scheme, allowing arbitrarily small divergence with dimension.