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.
Numerical experiments support conjecture about opers and nonabelian Hodge.
Some recent studies have suggested using GANs for numeric data generation such as to generate data for completing the imbalanced numeric data. Considering the significant difference between the dimensions of the numeric data and images, as well as the strong correlations between features of numeric data, the convention…
Study identifies numerical signs of blow-up in hydrodynamic equations.
New insights into surface energy reduction.
Study provides bounds for estimating intrinsic dimension using Gaussian kernels.
Using Maple, we compute some analytical solutions of a modified Black-Scholes equation, recently proposed, in the case of the European put option. We show that the modified Black-Scholes equation with the European put option is exactly solvable in terms of associated Laguerre polynomials. We make some numerical experim…
This study evaluates the importance of design of experiments for PINN in physics-informed deep learning.
The paper solves complex swing option pricing equations with numerical methods.
New algorithm uses Whittle index to improve Q-learning for restless bandits.
In this paper, we introduce a large class of convergent numerical methods, based on (linear) basis function regression technique, to approximate the solution to a forward-backward stochastic differential equation with jumps (FBSDEJ hereafter). Numerical experiment shows good applicability of the proposed method.
New methods improve insurance data quality for catastrophic events.
Several numerical approximation strategies for the expectation-propagation algorithm are studied in the context of large-scale learning: the Laplace method, a faster variant of it, Gaussian quadrature, and a deterministic version of variational sampling (i.e., combining quadrature with variational approximation). Exper…
This paper deals with the numerical approximation of American-style option values governed by partial differential complementarity problems. For a variety of one- and two-asset American options we investigate by ample numerical experiments the temporal convergence behaviour of three modern splitting methods: the explic…
In this article, we propose an exact simulation method of the Wishart multidimensional stochastic volatility (WMSV) model, which was recently introduced by Da Fonseca et al. \cite{DGT08}. Our method is based onanalysis of the conditional characteristic function of the log-price given volatility level. In particular, we…
Improved iterative methods for risk parity portfolio weights.
The paper analyzes and improves the learning rates of distributed kernel ridge regression.
FiNCAT tool automatically identifies financial numerals in documents.
We derive high-order compact finite difference schemes for option pricing in stochastic volatility models on non-uniform grids. The schemes are fourth-order accurate in space and second-order accurate in time for vanishing correlation. In our numerical study we obtain high-order numerical convergence also for non-zero …
Sinh-acceleration speeds up B-spline option pricing.
We construct a three-point compact finite difference scheme on a non-uniform mesh for the time-fractional Black-Scholes equation. We show that for special graded meshes used in finance, the Tavella-Randall and the quadratic meshes the numerical solution has a fourth-order accuracy in space. Numerical experiments are di…
Study proposes Local Linear Encoding for better feature discretization.
The paper studies scaling laws for associative memory mechanisms.
Study efficient numerical methods for American basket options.
New method combines ODE filters and numerical quadrature to propagate model uncertainty.
We propose and analyze numerical methods for the Heath-Jarrow-Morton (HJM) model. To construct the methods, we first discretize the infinite dimensional HJM equation in maturity time variable using quadrature rules for approximating the arbitrage-free drift. This results in a finite dimensional system of stochastic dif…
In this paper we propose a new kind of high order numerical scheme for backward stochastic differential equations(BSDEs). Unlike the traditional -scheme, we reduce truncation errors by taking carefully for every subinterval according to the characteristics of integrands. We give error estimates of this nonlinear…
This paper deals with stability in the numerical solution of the prominent Heston partial differential equation from mathematical finance. We study the well-known central second-order finite difference discretization, which leads to large semi-discrete systems with non-normal matrices A. By employing the logarithmic sp…
A new method learns Hamiltonian functions from noisy data.
A new method speeds up option pricing under Heston's stochastic volatility model.
LLMs struggle with arithmetic tasks unless they use high numerical precision.
We consider rate swaps which pay a fixed rate against a floating rate in presence of bid-ask spread costs. Even for simple models of bid-ask spread costs, there is no explicit strategy optimizing an expected function of the hedging error. We here propose an efficient algorithm based on the stochastic gradient method to…
Improved stability for large-scale Bayesian sampling.
The paper extends a method for numerical conformal mappings to surfaces using Laplace-Beltrami equations.
Develops a numerical method for LRM strategies in BNS models with infinite active jumps.
Deep learning solves high-dimensional PDEs efficiently.
The paper explores risk-minimization for exponential additive models, providing mathematical expressions and numerical examples.
Method extracts governing laws from non-Gaussian stochastic systems data.
T-Rex selector selects variables fast and controls FDR in high-dimensional data.
We present a numerical approach for solving the free boundary problem for the Black-Scholes equation for pricing American style of floating strike Asian options. A fixed domain transformation of the free boundary problem into a parabolic equation defined on a fixed spatial domain is performed. As a result a nonlinear t…
Deep quantum neural networks applied to finance for efficient risk management.
Paper introduces NumLLM for better financial text understanding with numeric variables.
We apply multilevel Monte Carlo for option pricing problems using exponential Lévy models with a uniform timestep discretisation to monitor the running maximum required for lookback and barrier options. The numerical results demonstrate the computational efficiency of this approach. We derive estimates of the convergen…
We propose a numerical algorithm for backward stochastic differential equations based on time discretization and trigonometric wavelets. This method combines the effectiveness of Fourier-based methods and the simplicity of a wavelet-based formula, resulting in an algorithm that is both accurate and easy to implement. F…
A framework for binary classification on top samples.
Improved method for numerical conformal mappings on complex domains.
We give an overview of the constrained Willmore problem and address some conjectures arising from partial results and numerical experiments. Ramifications of these conjectures would lead to a deeper understanding of the Willmore functional over conformal immersions from compact surfaces.
Bayesian method for estimating inputs leading to specific probability outputs.
Calibration of stochastic local volatility (SLV) models to their underlying local volatility model is often performed by numerically solving a two-dimensional non-linear forward Kolmogorov equation. We propose a novel finite volume (FV) discretization in the numerical solution of general 1D and 2D forward Kolmogorov eq…