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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.

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48 results for PIDEs

INEUS solves high-dimensional PIDEs efficiently with neural networks.

problem Solving high-dimensional partial integro-differential equations (PIDEs) efficiently.
method INEUS uses iterative neural networks to replace nonlocal integrals with sampling and reformulates PIDE solving as recursive regression.
result INEUS delivers accurate and scalable solutions for high-dimensional linear and nonlinear PIDEs.

An unsupervised deep learning method solves PIDEs for option pricing.

problem Solving partial integro-differential equations for financial option pricing.
method Employing unsupervised deep learning to directly solve PIDEs without requiring labeled data.
result An unsupervised neural network accurately solves PIDEs and calculates derivatives and integrals.

Paper analyzes and proves convergence of a new method for solving complex PDEs.

problem Solving high-dimensional nonlinear PDEs and PIDEs with random neural networks.
method Random deep splitting method using random neural networks.
result The method converges to the unique viscosity solution of nonlinear PDEs and PIDEs.

Study of financial models using PIDEs with and without market liquidity.

problem Financial models under illiquid markets and their PIDEs.
method Investigation of linear and nonlinear PIDEs, including Lévy processes, using abstract semilinear parabolic equation theory.
result Existence and uniqueness of solutions to PIDEs for admissible Lévy measures.

Paper proves existence and uniqueness of solutions to PIDEs in Bessel spaces for option pricing.

problem Existence and uniqueness of solutions to PIDEs in Bessel spaces.
method Abstract semilinear parabolic equations and Bessel potential spaces.
result Proves existence and uniqueness of solutions in Bessel potential spaces.

Develops a PIDE framework for option pricing with stochastic volatility and jumps.

problem Option pricing under stochastic volatility and jumps.
method PIDE framework derived from Lévy-type process, implemented via finite-difference discretization with FFT for nonlocal jump operator, calibrated using GMM.
result Stochastic volatility accounts for most pricing improvement, reducing implied-volatility RMSE by 39% compared to Black-Scholes.

The paper solves complex swing option pricing equations with numerical methods.

problem Valuation of swing options with jumps under a mean-reverting model.
method Proposes second-order numerical methods to solve PIDEs convection-dominated and with nonlocal integral terms.
result Numerical methods confirm second-order convergence behavior.

Paper analyzes multidimensional PIDEs for financial modeling, proving existence and uniqueness in Bessel spaces.

problem Analyzing solutions of non-local nonlinear PIDEs in multidimensional spaces.
method Employing abstract semilinear parabolic equations theory in Bessel potential spaces.
result Existence and uniqueness of solutions for a wide class of Lévy measures in multidimensional spaces.

A new method for pricing derivatives using self-exciting dynamics and finite-difference transforms.

problem Pricing derivatives with accumulated marks using a self-exciting marked point process.
method Derive discounted pricing equation as a PIDE, transform to one-dimensional PIDEs, use Laplace/Fourier transform, approximate jump term, solve using finite difference scheme.
result Efficiently price derivatives with accumulated marks using a novel finite-difference and transform approach.

Study pricing derivatives in markets with long-range dependence and jumps.

problem Deriving pricing formulas for derivatives in markets with long-range dependence and jumps.
method Developed a fractional integro-partial differential equation (PIDE) and used semigroup theory and finite-difference schemes for numerical solutions.
result Closed-form pricing formula for European options and numerical solution for general options.

FBSJNN solves PIDEs and FBSDEJs with deep learning, offering theoretical and numerical efficiency.

problem Solving Partial Integro-Differential Equations and Forward-Backward Stochastic Differential Equations with Jumps.
method FBSJNN framework using a single neural network for both solution approximation and non-local integral.
result FBSJNN achieves numerical solutions with a relative error of 10310^{-3}, demonstrating efficiency.

This paper is dedicated to the construction of high-order (in both space and time) finite-difference schemes for both forward and backward PDEs and PIDEs, such that option prices obtained by solving both the forward and backward equations are consistent. This approach is partly inspired by Andreasen & Huge, 2011 who re…

2014-03-07abs ↗pdf ↗

Protocol diagnoses neural HJB-PIDE solvers for Lévy jumps, revealing a missing factor in their importance-proposal density.

problem Neural PDE solvers can match scalar diagnostics but miscompute operators, leading to systematic errors.
method Five-step diagnostic protocol decomposes neural solve into components, compares them with independent reference solutions.
result Corrected a missing 1/2-mixture factor in the neural method's importance-proposal density, improving control accuracy.

This paper develops a novel numerical method for pricing American options in a two-asset jump-diffusion model.

problem Pricing American options under correlated two-asset jump-diffusion models using finite difference methods often fails to preserve monotonicity and accurately discretize jump integrals.
method Introduces a novel monotone integration scheme to solve 2-D Partial Integro-Differential Equations (PIDEs) efficiently and accurately.
result The proposed method ensures convergence to the viscosity solution of the variational inequality and is both \ell_{\infty}-stable and consistent.

We provide a bound for the error committed when using a Fourier method to price European options when the underlying follows an exponential \levy dynamic. The price of the option is described by a partial integro-differential equation (PIDE). Applying a Fourier transformation to the PIDE yields an ordinary differential…

2015-02-27abs ↗pdf ↗

For α(1,2)α\in (1,2), we present a generalized central limit theorem for αα-stable random variables under sublinear expectation. The foundation of our proof is an interior regularity estimate for partial integro-differential equations (PIDEs). A classical generalized central limit theorem is recovered as a special case, p…

2014-09-28abs ↗pdf ↗

One popular approach to option pricing in Lévy models is through solving the related partial integro differential equation (PIDE). For the numerical solution of such equations powerful Galerkin methods have been put forward e.g. by Hilber et al. (2013). As in practice large classes of models are maintained simultaneous…

2016-03-27abs ↗pdf ↗

Reinsurance counterparty credit risk (RCCR) is the risk of a loss arising from the fact that a reinsurance company is unable to fulfill her contractual obligations towards the ceding insurer. RCCR is an important risk category for insurance companies which, so far, has been addressed mostly via qualitative approaches. …

2019-09-10abs ↗pdf ↗

A new method for pricing options with stochastic volatility and jumps.

problem Pricing options under stochastic volatility and jumps.
method Fourth-order compact finite-difference scheme with implicit-explicit Crank-Nicolson framework.
result The method achieves near-fourth-order spatial accuracy and up to two orders of magnitude lower runtime than quadratic finite elements.

Paper derives Thiele's equation for unit-linked policies in a stochastic volatility model.

problem Deriving pricing formula for unit-linked policies in a stochastic volatility model.
method Derives Thiele's differential equation for a unit-linked policy in the Heston-Hawkes model.
result Established a method to compute reserves in life insurance via solving Thiele's equation.

A new model uses a Levy-driven process to value credit index swaptions.

problem Valuation of credit index swaptions in financial markets.
method Proposes a Levy-driven Ornstein-Uhlenbeck process to model risk-free rate and default intensities.
result Derives formulas for characteristic function, moments, and stationary distribution.

A machine learning method for short-maturity options with jumps and stochastic volatility.

problem Short-maturity options with jumps and stochastic volatility.
method Differential machine learning method combining supervision and PIDE-residual penalty.
result Improves jump-term approximation and reduces Greeks errors compared to baselines.

We extend the Deep Galerkin Method (DGM) introduced in Sirignano and Spiliopoulos (2018)} to solve a number of partial differential equations (PDEs) that arise in the context of optimal stochastic control and mean field games. First, we consider PDEs where the function is constrained to be positive and integrate to uni…

2019-11-30abs ↗pdf ↗