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.
The paper solves a complex option pricing model using finite elements.
Finite element method applied to Leland's model for option pricing with transaction costs.
A new method for pricing options with stochastic volatility and jumps.
We present high-order compact schemes for a linear second-order parabolic partial differential equation (PDE) with mixed second-order derivative terms in two spatial dimensions. The schemes are applied to option pricing PDE for a family of stochastic volatility models. We use a non-uniform grid with more grid-points ar…
New probabilistic scheme combines deep learning with Runge-Kutta methods for solving PDEs.
The stability and robustness of compact schemes for parabolic PDEs are analyzed.
In this paper we focus on the subdiffusive Black Scholes model. The main part of our work consists of the finite difference method as a numerical approach to the option pricing in the considered model. We derive the governing fractional differential equation and the related weighted numerical scheme being a generalizat…
The Runge-Kutta-Legendre scheme improves pricing American options and other derivatives.
The paper explores efficient sampling for Bayesian wide neural networks.
In this paper, a standard PDE for the pricing of arithmetic average strike Asian call option is presented. A Crank-Nicolson Implicit Method and a Higher Order Compact finite difference scheme for this pricing problem is derived. Both these schemes were implemented for various values of risk free rate and volatility. Th…
SKT improves EKI for Bayesian inverse problems with non-Gaussian targets.
Efficient numerical method for time-fractional Black-Scholes model.
This paper studies the optimal VIX futures trading problems under a regime-switching model. We consider the VIX as mean reversion dynamics with dependence on the regime that switches among a finite number of states. For the trading strategies, we analyze the timings and sequences of the investor's market participation,…
Paper solves convertible bond valuation using finite elements with penalty method.
For the first time in mathematical finance field, we propose the local weak form meshless methods for option pricing; especially in this paper we select and analysis two schemes of them named local boundary integral equation method (LBIE) based on moving least squares approximation (MLS) and local radial point interpol…
In this article, a compact finite difference method is proposed for pricing European and American options under jump-diffusion models. Partial integro-differential equation and linear complementary problem governing European and American options respectively are discretized using Crank-Nicolson Leap-Frog scheme. In pro…
We consider a two-factor model for the valuation of a non callable defaultable bond which pays coupons at certain given dates. The model under consideration is the Jump to Default Constant Elasticity of Variance (JDCEV) model. The JDCEV model is an improvement of the reduced form approach, which unifies credit and equi…
The issue of developing simple Black-Scholes type approximations for pricing European options with large discrete dividends was popular since early 2000's with a few different approaches reported during the last 10 years. Moreover, it has been claimed that at least some of the resulting expressions represent high-quali…
We introduce a new family of MCMC samplers that combine auxiliary variables, Gibbs sampling and Taylor expansions of the target density. Our approach permits the marginalisation over the auxiliary variables yielding marginal samplers, or the augmentation of the auxiliary variables, yielding auxiliary samplers. The well…
EPGP surrogate outperforms finite elements in solving wave equations.
Bayesian method uses deep learning prior for CT reconstruction.
Pseudo-marginal Metropolis-Hastings (pmMH) is a powerful method for Bayesian inference in models where the posterior distribution is analytical intractable or computationally costly to evaluate directly. It operates by introducing additional auxiliary variables into the model and form an extended target distribution, w…
Bayesian method infers network topology and dynamics from noisy, sparse measurements.
Bayesian imaging uses neural networks to learn prior knowledge from data.
Fast ML framework for derivative valuation from volatility surfaces.
It was proved in 1998 by Ben-David and Litman that a concept space has a sample compression scheme of size d if and only if every finite subspace has a sample compression scheme of size d. In the compactness theorem, measurability of the hypotheses of the created sample compression scheme is not guaranteed; at the same…
Study SL(2,C) character schemes for finitely generated groups.
A new method for computing image curvature efficiently and accurately.
Reduces multiclass and regression compression schemes to binary ones.
Study evaluates UK CDC schemes, finding intergenerational cross-subsidies in flat-accrual schemes and dynamic-accrual schemes can reduce but not eliminate them.
We study first-order optimization methods obtained by discretizing ordinary differential equations (ODEs) corresponding to Nesterov's accelerated gradient methods (NAGs) and Polyak's heavy-ball method. We consider three discretization schemes: an explicit Euler scheme, an implicit Euler scheme, and a symplectic scheme.…
Generalizes soft noncommutative schemes to flag varieties.
AES scheme improves Bermudan and American option pricing for Heston models.
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…
Defines hypercomplex analytic spaces and schemes.
New schemes for SDEs on manifolds keep solutions close to the manifold.
We extend the scheme developed in B. Düring, A. Pitkin, "High-order compact finite difference scheme for option pricing in stochastic volatility jump models", 2019, to the so-called stochastic volatility with contemporaneous jumps (SVCJ) model, derived by Duffie, Pan and Singleton. The performance of the scheme is asse…
Characterizes Hilbert schemes and their geometric properties.
Vector fields on schemes have flows if rings are finitely generated.
In this paper we propose a generalized numerical scheme for backward stochastic differential equations(BSDEs). The scheme is based on approximation of derivatives via Lagrange interpolation. By changing the distribution of sample points used for interpolation, one can get various numerical schemes with different stabil…
New methods combine MALA and mGRAD for scalable Bayesian inference in high-dimensional state-space models.
Extends JKO scheme for iterative algorithms with unknown parameters.
A new scheme for FBSDEs simplifies computation without Monte Carlo.
A discretization scheme for nonnegative diffusion processes is proposed and the convergence of the corresponding sequence of approximate processes is proved using the martingale problem framework. Motivations for this scheme come typically from finance, especially for path-dependent option pricing. The scheme is simple…
Study on statistical estimation over Gaussian MAC, comparing analog and digital schemes.
Efficient simulation scheme for rough Heston model reduces computational cost.
Study finds risk management significantly improves pension scheme efficiency in Kenya.
In this paper, we propose an acceleration scheme for online memory-limited PCA methods. Our scheme converges to the first eigenvectors in a single data pass. We provide empirical convergence results of our scheme based on the spiked covariance model. Our scheme does not require any predefined parameters such as t…