Speeds up complex portfolio exposure calculations.
problem Calculating exposure of portfolios with exotic derivatives.
method Least Squares Monte Carlo (LSMC) technique.
result Significantly reduces computation time for nested Monte Carlo.
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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.
169,341 papers · 148 categories
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48 results for “regression Monte Carlo”
Local Control Regression: Improving the Least Squares Monte Carlo Method for Portfolio Optimizationq-fin.PM
Local control regression improves portfolio optimization accuracy.
problem Expensive and inaccurate global control regression for portfolio optimization.
method Introduced local control regression combined with adaptive grids.
result Choosing a coarse grid for local regression produces accurate results.
Simplifies American option pricing with reduced complexity.
problem Complexity reduction in American option pricing.
method Regression-based dual approach for nested Monte Carlo methods.
result Reduces complexity of nested Monte Carlo methods.
Develops a numerical algorithm for stochastic impulse control using regression surrogates.
problem Optimal impulse control in stochastic processes.
method Generates statistical surrogates for continuation and intervention functions, recursively trained over simulated state trajectories.
result Demonstrates flexibility and extensibility of the numerical scheme through case studies.
Paper proposes a new method to solve Schrödinger Bridge Problem using kernel regression.
problem Schrödinger Bridge Problem in the context of entropic optimal transport.
method Forward-reverse iterative Monte Carlo procedure using kernel regression.
result Developed a provably convergent algorithm for approximating Schrödinger potentials.
New algorithms improve MCMC efficiency for complex distributions.
problem High variance and low effective sample size in MCMC samplers.
method Antithetic Riemannian Manifold and Quantum-Inspired Hamiltonian Monte Carlo.
result Improved effective sample size and variance reduction.
Improved Bayesian regression for large datasets using multilevel Gibbs sampling.
problem Efficiently handling large-scale Bayesian regression with complex posterior distributions.
method Developed a multilevel Gibbs sampler for linear mixed models, incorporating data clustering and correlated samples for variance reduction.
result Significant speed-up achieved for Bayesian regression without sacrificing predictive performance.
New method uses reinforced regression for solving optimal stopping problems.
problem Solving optimal stopping problems in mathematical finance.
method Reinforced regression based on previously estimated continuation values.
result Illustrated by a numerical example from mathematical finance.
This paper compares LSM and ANN/GBM for pricing American put options under a complex model.
problem Pricing American put options using advanced techniques.
method Least-Squares Monte Carlo (LSM) and Artificial Neural Network (ANN) and Gradient Boosted Machine (GBM) Trees.
result LSM outperforms ANN and GBM in pricing American put options.
A new two-step LSMC method improves game option pricing accuracy.
problem Improving game option pricing accuracy using Monte Carlo methods.
method Proposed a two-step Longstaff Schwartz Monte Carlo approach with two regression models fitted at each time step.
result Our method produces more reliable results compared to the original LSMC.
Bayesian framework for semiparametric regression of discrete data.
problem Complex distributional features of discrete data.
method Semiparametric modeling with nonparametric marginal and latent linear regression.
result Posterior consistency and analytical/posterior predictive distributions.
Improved Hamiltonian Monte Carlo for Bayesian inference reduces variance and improves performance.
problem Efficiently sampling from posterior distributions in Bayesian inference with stochastic gradients.
method Variance reduction techniques applied to Hamiltonian Monte Carlo.
result Theoretical and experimental improvements in convergence and performance compared to variance-reduced Langevin dynamics.
Many problems in financial engineering involve the estimation of unknown conditional expectations across a time interval. Often Least Squares Monte Carlo techniques are used for the estimation. One method that can be combined with Least Squares Monte Carlo is the "Regress-Later" method. Unlike conventional methods wher…
A new GP method enforces physical constraints in probabilistic terms.
problem Unbounded model in GP regression leading to infeasible values.
method Introduces a new GP method using QHMC to enforce soft inequality and monotonicity constraints.
result Improves accuracy and reduces variance in GP model.
This paper offers a simple method for Bayesian regression with unknown transformations.
problem Joint inference of unknown transformations and model parameters in Bayesian regression is computationally inefficient and cumbersome.
method The paper introduces a Bayesian nonparametric model via the Bayesian bootstrap to directly target the posterior distribution of the transformation.
result The approach delivers joint posterior consistency and efficient Monte Carlo inference for the transformation and all parameters.
Unified platform for optimal stopping problems in R.
problem Optimal stopping problems in machine learning.
method Unified implementation of Regression Monte Carlo algorithms.
result Unified and reproducible platform for RMC algorithms.
Proposes a Monte-Carlo method for sparse signal reconstruction.
problem Reconstructing sparse signals in high-dimensional settings.
method Greedy Monte-Carlo (GMC) search algorithm.
result GMC can achieve perfect reconstruction in undersampling situations.
The paper explores how control variates can reduce variance in Monte Carlo simulations, especially for Sobolev functions.
problem Efficiency of control variates in reducing variance for Monte Carlo simulations.
method Study of a specific quadrature rule using nonparametric regression-adjusted control variates.
result A specific quadrature rule can improve the Monte Carlo rate and achieve the minimax optimal rate under sufficient smoothness assumptions.
Study uses statistical methods to solve control problems with probabilistic constraints.
problem Optimizing control of systems with low probability of failure.
method Monte Carlo algorithms and statistical regression techniques.
result Logistic and Gaussian process regression outperform other methods in estimating admissibility probability.
Stochastic Earned Value Analysis using Monte Carlo Simulation and Statistical Learning Techniquesq-fin.RM
Paper introduces a new project control method using Monte Carlo and statistical learning.
problem Project control under uncertainty.
method Integrates Earned Value Methodology with Monte Carlo simulation and statistical learning.
result Estimates probabilities of project success and duration.
Gaussian process is a very promising novel technology that has been applied to both the regression problem and the classification problem. While for the regression problem it yields simple exact solutions, this is not the case for the classification problem, because we encounter intractable integrals. In this paper we …
New regression-based algorithms for optimal stopping problems reduce computational cost.
problem Optimal stopping problems in dynamic programming.
method Pseudo-regression approach using Monte Carlo approximation of inner products.
result The method asymptotically leads to lower computational cost and complexity.
The paper introduces new estimators for multivariate functions using Fourier methods.
problem Estimating multivariate functions like densities and regression functions.
method Monte Carlo estimators based on the Fourier integral theorem.
result Established rates of convergence for new estimators, often superior to existing methods.
Quantum algorithm speeds up financial option pricing.
problem Optimizing stopping times in stochastic processes for finance.
method Combines quantum computing techniques with LSM for optimal stopping.
result Achieves nearly quadratic speedup in runtime.
Improved method for estimating derivatives of discontinuous functions using stochastic algorithmic differentiation and regression.
problem High Monte-Carlo error in finite difference approximation of discontinuous functions.
method Combining stochastic algorithmic differentiation and regression to estimate derivative of expectations of discontinuous functions.
result Reduction in Monte-Carlo error through decoupling integration of Dirac delta and conditional expectation.
RQMC improves kernel-based learning by reducing deterministic error and offering computational advantages.
problem Improving kernel-based learning methods to reduce deterministic error and computational complexity.
method Randomized quasi-Monte Carlo (RQMC) methods applied to random feature approximations.
result RQMC methods improve deterministic approximation error bound from to , matching QMC methods.
Tensor networks improve integration accuracy for high-dimensional problems.
problem Integration of high-dimensional functions with exponential convergence.
method Regression-free tensor network representations for integration.
result Exponential convergence achieved for non-analytic integrands.
Improved Least-Squares Monte Carlo with finite-difference ansatz.
problem Improving accuracy and stability in option pricing.
method Constructing an ansatz using finite-difference solution for conditional expected continuation payoffs.
result Reduces mean squared error and final pricing error.
Numerical Generalized Randomized Hamiltonian Monte Carlo for piecewise smooth target densitiesstat.CO
New method samples from piecewise smooth distributions using Hamiltonian Monte Carlo.
problem Sampling from distributions with discontinuous gradients.
method Generalized Randomized Hamiltonian Monte Carlo (GRHMC) for piecewise smooth targets.
result GRHMC processes sample from piecewise smooth target distributions with the desired distribution as the invariant distribution.
Unbiased method for Bayesian posterior means using kinetic Langevin dynamics.
problem Estimating Bayesian posterior means efficiently and accurately.
method Combines advanced splitting methods with enhanced gradient approximations in a multilevel Monte Carlo approach.
result The method achieves unbiased estimates with finite variance and central limit theorem properties.
A new algorithm reduces bias in LSM for Bermudan option pricing.
problem Look-ahead bias in LSM for Bermudan options.
method Leave-one-out least squares Monte Carlo (LOOLSM) algorithm.
result LOOLSM eliminates look-ahead bias without doubling simulations.
FBMS R package simplifies Bayesian model selection and averaging.
problem Complex regression settings with multi-modal posterior landscapes.
method Efficient MJMCMC and GMJMCMC algorithms for Bayesian model exploration.
result FBMS effectively handles Bayesian generalized linear and nonlinear models.
New algorithm broadens BART models applicability.
problem Limited applicability of Bayesian additive regression trees (BART) models due to conditional conjugacy.
method Introduces a reversible jump Markov chain Monte Carlo algorithm for generalized BART models.
result Extends BART models to arbitrary generalized BART models without conditional conjugacy.
Paper develops a probabilistic regressor chain method using Monte Carlo methods.
problem Improving multi-output regression with probabilistic chains.
method Develops a sequential Monte Carlo scheme for probabilistic regressor chains.
result Monte Carlo scheme for probabilistic regressor chains can be effective and useful.
An analysis of linear regression and neural networks approximation for the pricing of swing optionsq-fin.MF
This paper compares linear regression and neural networks for pricing swing options.
problem Pricing swing options using approximation methods.
method Linear regression and neural networks for approximating the continuation value and swing price.
result The approximation methods converge to the actual swing price as the number of functions or Monte Carlo samples increases.
New algorithms for Bayesian inference in decentralized learning.
problem Bayesian inference in decentralized learning settings.
method Decentralized SGLD and Decentralized SGHMC.
result Convergence of iterates to target distribution in 2-Wasserstein distance.
New method detects hidden market predictability despite anomalies.
problem Inference issues in predictive regressions with small violations.
method Novel testing framework resistant to violations of ideal assumptions.
result Large improvements in robust evidence of market predictability.
Regularized linear regression under the penalty, such as the Lasso, has been shown to be effective in variable selection and sparse modeling. The sampling distribution of an -penalized estimator is hard to determine as the estimator is defined by an optimization problem that in general can only…
Improves sampling efficiency for complex Bayesian models.
problem Inference challenges in hierarchical Gaussian-process models.
method Optimised Riemannian-manifold Hamiltonian Monte Carlo (RMHMC) with dynamic programming.
result Significant improvement in sampling efficiency and model evidence calculation.
Unified algorithm for solving stochastic storage problems using statistical learning.
problem Solving stochastic storage problems through regression Monte Carlo methods.
method Developed the dynamic emulation algorithm (DEA) that unifies different approaches.
result Illustrated the DEA template with examples from natural gas storage and microgrid control.
This article reviews and explains HMC-based methods for sampling constrained continuous distributions.
problem Sampling from continuous distributions with constraints.
method HMC and related methods for constrained sampling.
result HMC and related methods are more efficient for constrained sampling.
A new method uses Monte Carlo sampling for Bayesian optimization.
problem Optimizing expensive function evaluations with uncertainty.
method Sequential Monte Carlo approach to Thompson sampling.
result Thompson sampling applied to continuous input space optimization.
Two methods using Chebyshev tensors improve accuracy and speed in computing Dynamic Initial Margin.
problem Computing Dynamic Initial Margin (DIM) with high accuracy and speed.
method Two methods based on Chebyshev tensors implemented in Monte Carlo engine.
result Better accuracy, speed, and implementation efforts compared to benchmarks.
New bounds for VIX derivatives pricing using LS Monte Carlo.
problem Pricing VIX derivatives due to the square root of expected realised variance.
method Least Squares Monte Carlo with stochastic duality and adjustments.
result Effective upper and lower bounds for VIX derivatives pricing.
metabeta uses neural networks to speed up Bayesian mixed-effects regression.
problem Bayesian mixed-effects regression is computationally expensive.
method metabeta is a neural network model that pre-trains to estimate posterior distributions.
result metabeta achieves comparable performance to MCMC at a fraction of the time.
Bayesian meta learning improves uncertainty quantification in regression.
problem Trusting uncertainty quantification in Bayesian regression.
method Trust-Bayes framework for Bayesian meta learning, optimizing for trustworthy uncertainty quantification.
result Lower bounds and sample complexity for trustworthy uncertainty quantification are characterized.
Algorithm uncovers treatment effect heterogeneity in educational RD designs.
problem Discovering sources of treatment effect heterogeneity in regression discontinuity designs.
method Causal supervised machine learning algorithm to build a 'regression discontinuity tree'.
result Algorithm uncovers various sources of heterogeneity in the impact of attending a better secondary school.
Unified framework for CVA sensitivities, hedging, and risk assessment.
problem Computing and managing Credit Value Adjustment (CVA) sensitivities and risks.
method Probabilistic machine learning and refined regression on simulated data, validated by Monte Carlo methods.
result Identification of optimal sensitivities for practical tasks like hedging and risk assessment.