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.
Paper presents a new backward deep BSDE method for solving nonlinear FBSDE problems.
Paper introduces solving financial problems using time-stepped FBSDE and deep learning.
The paper extends NUP representations to factor graphs for better estimation.
In this paper, we further study the forward-backward envelope first introduced in [28] and [30] for problems whose objective is the sum of a proper closed convex function and a twice continuously differentiable possibly nonconvex function with Lipschitz continuous gradient. We derive sufficient conditions on the origin…
Backward exploration reduces sample complexity in policy evaluation.
Backward SDEs help price XVA for OTC derivatives.
Study proves existence of equilibrium in incomplete economies with discontinuous volatility.
This paper formulates and studies a stochastic maximum principle for forward-backward stochastic Volterra integral equations (FBSVIEs in short), while the control area is assumed to be convex. Then a linear quadratic (LQ in short) problem for backward stochastic Volterra integral equations (BSVIEs in short) is present …
Method solves optimisation problems on non-Riemannian surfaces with bilateral curvature bounds.
We establish existence, uniqueness and regularity of solution results for a class of backward stochastic partial differential equations with singular terminal condition. The equation describes the value function of non-Markovian stochastic optimal control problem in which the terminal state of the controlled process is…
The paper develops methods to price options under rough volatility models using BSPDEs.
New method uses backward SDEs for deep learning uncertainty.
Formula found for heat equation control and backward problems.
Study BSΔE on lattices for asset price analysis.
We consider controller-stopper problems in which the controlled processes can have jumps. The global filtration is represented by the Brownian filtration, enlarged by the filtration generated by the jump process. We assume that there exists a conditional probability density function for the jump times and marks given t…
A new method detects changes in data sequences by comparing backward and forward confidence sequences.
SGD converges with perturbed forward-backward passes, explained by geometric amplification.
Optimal wealth strategy derived for jump-diffusion models with liabilities.
We propose a new method for the numerical solution of backward stochastic differential equations (BSDEs) which finds its roots in Fourier analysis. The method consists of an Euler time discretization of the BSDE with certain conditional expectations expressed in terms of Fourier transforms and computed using the fast F…
New algorithm for aggregate inference in HMMs with continuous observations.
We generalize the primal-dual methodology, which is popular in the pricing of early-exercise options, to a backward dynamic programming equation associated with time discretization schemes of (reflected) backward stochastic differential equations (BSDEs). Taking as an input some approximate solution of the backward dyn…
We study utility maximization problem for general utility functions using dynamic programming approach. We consider an incomplete financial market model, where the dynamics of asset prices are described by an -valued continuous semimartingale. Under some regularity assumptions we derive backward stochastic partial…
Unified approach solves Kyle model with dynamic information.
We study a constrained optimal control problem with possibly degenerate coefficients arising in models of optimal portfolio liquidation under market impact. The coefficients can be random in which case the value function is described by a degenerate backward stochastic partial differential equation (BSPDE) with singula…
DG improves policy gradient efficiency by selectively backpropagating only valuable samples.
Paper tackles backwards-compatible data adaptation for confounded covariate and label shifts.
New method for dynamic valuation in markets with random endowments.
We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonst…
Solves wealth maximization problem using variational analysis.
BC-Aligner maintains backward compatibility of embeddings after frequent updates.
Backward stochastic partial differential equations of parabolic type in bounded domains are studied in the setting where the coercivity condition is not necessary satisfied and the equation can be degenerate. Some generalized solutions based on the representation theorem are suggested. In addition to problems with a st…
We consider forward-backward greedy algorithms for solving sparse feature selection problems with general convex smooth functions. A state-of-the-art greedy method, the Forward-Backward greedy algorithm (FoBa-obj) requires to solve a large number of optimization problems, thus it is not scalable for large-size problems…
The paper solves MMV and MV problems with random coefficients and finds shared optimal strategies.
New algorithms reduce variance in solving complex mathematical problems.
FLUID uses flows to unify filtering and smoothing for complex systems.
Study time-inconsistent consumption-investment in incomplete markets with general discount functions.
Deep-learning method solves BSVIEs and coupled systems.
In this paper, we study the evolving behaviors of the first eigenvalue of Laplace-Beltrami operator under the normalized backward Ricci flow, construct various quantities which are monotonic under the backward Ricci flow and get upper and lower bounds. We prove that in cases where the backward Ricci flow converges to a…
Study optimal investment in large populations of competitive, heterogeneous agents.
In this paper, we propose an implicit gradient descent algorithm for the classic -means problem. The implicit gradient step or backward Euler is solved via stochastic fixed-point iteration, in which we randomly sample a mini-batch gradient in every iteration. It is the average of the fixed-point trajectory that is c…
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…
New HMC method handles features in POS tagging, outperforming MEMM.
We propose a numerical recipe for risk evaluation defined by a backward stochastic differential equation. Using dual representation of the risk measure, we convert the risk valuation to a stochastic control problem where the control is a certain Radon-Nikodym derivative process. By exploring the maximum principle, we s…
Paper finds a new principle for optimizing consumption and wealth using Tsallis entropy.
Paper develops a new probabilistic method for American options using entropy regularization.
Goals for reinforcement learning problems are typically defined through hand-specified rewards. To design such problems, developers of learning algorithms must inherently be aware of what the task goals are, yet we often require agents to discover them on their own without any supervision beyond these sparse rewards. W…
This paper considers a non-Markov control problem arising in a financial market where asset returns depend on hidden factors. The problem is non-Markov because nonlinear filtering is required to make inference on these factors, and hence the associated dynamic program effectively takes the filtering distribution as one…
A new algorithm solves high-dimensional nonlinear BSDEs using deep learning.