Improves Monte-Carlo simulations for consistent mean and variance.
problem Artificial randomness in running mean calculations.
method Combining running mean and variance with accurate summing.
result Increased accuracy and robustness of Monte-Carlo estimates.
A new eigenvalue-based method speeds up Monte Carlo simulations.
problem Reducing the number of paths needed for accurate Monte Carlo simulations.
method Eigenvalue-based approximation of Markov Chain Monte Carlo.
result Significant variance reduction and comparable results to traditional Monte Carlo.
ParaMonte simplifies Monte Carlo simulations for various scientific fields.
problem Efficiently performing Monte Carlo simulations for complex models.
method Unified, high-performance, parallelized library for C, C++, Fortran.
result Automates and streamlines Monte Carlo sampling for arbitrary-dimensional functions.
We consider the problem of simulating loss probabilities and conditional excesses for linear asset portfolios under the t-copula model. Although in the literature on market risk management there are papers proposing efficient variance reduction methods for Monte Carlo simulation of portfolio market risk, there is no pa…
Quantum computing techniques applied to Monte Carlo simulations in finance.
problem Efficiently simulating quantum algorithms for financial modeling.
method Introduces quantum computing basics, amplitude estimation, and Grover's algorithm for unstructured search.
result Demonstrates quantum approaches to Monte Carlo integration and counting in finance.
Paper uses Monte Carlo simulations to predict retirement portfolios.
problem Retirement financial planning uncertainty.
method Monte Carlo simulations incorporating inflation, interest rates, etc.
result Probabilistic prediction of IRA and 401(k) values.
Teaching tool simplifies Monte Carlo simulation for project risk analysis.
problem Difficulty in students performing Monte Carlo Simulation in risk analysis.
method Introducing MCSimulRisk as a teaching tool.
result Students can perform Monte Carlo simulation and apply it to projects of any complexity.
New method reduces uncertainty in AI-driven Monte Carlo simulations.
problem Epistemic uncertainty in AI surrogate models affects Monte Carlo sampling outcomes.
method Penalty Ensemble Method (PEM) modifies Metropolis acceptance rule to increase rejection probability in uncertain regions.
result PEM enhances reliability of Monte Carlo simulations by reducing uncertainty propagation.
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.
Inference for normal and Monte Carlo distributions using minimum relative entropy.
problem Inference from partial information on expectations and covariances.
method Minimum relative entropy sub-manifolds, analytical formulas, Monte Carlo simulations.
result Improved numerical implementation for inference from partial information.
New method combines Monte Carlo and tensor networks for solving complex equations.
problem Solving high-dimensional partial differential equations efficiently.
method Uses Monte Carlo simulations and tensor train sketching for updates and re-estimations.
result Demonstrates versatility and efficacy in solving specific equations.
Improved ANN-based Monte Carlo simulation for Higgs decay events.
problem Accurate simulation of Higgs boson decay events.
method Monte Carlo simulation using an Artificial Neural Network (ANN) with improved training algorithm.
result The ANN simulation of Higgs decay is within 0.7% of the true value and achieves 26% unweighting efficiency.
The paper predicts cryptocurrency prices using a path-dependent Monte Carlo simulation.
problem Forecasting cryptocurrency prices with volatility and jumps.
method Merton's jump diffusion model with machine learning, traditional, and statistical methods.
result Introduced a path-dependent Monte Carlo simulation for cryptocurrency price prediction.
PEMC uses ML to enhance Monte Carlo simulations, reducing variance and runtime.
problem Computational inefficiency in Monte Carlo simulations for complex tasks.
method Prediction-Enhanced Monte Carlo (PEMC) framework that uses ML surrogates as predictors.
result PEMC provides unbiased evaluations with reduced variance and runtime compared to standard Monte Carlo.
ParaMonte::Python streamlines Bayesian data analysis with fast Monte Carlo and MCMC routines.
problem Efficiently sampling posterior distributions in Bayesian modeling and data science.
method Serial and MPI-parallelized Markov Chain Monte Carlo (MCMC) routines.
result Automated model calibration and uncertainty quantification in Bayesian analysis.
Monte Carlo simulations of diffusion processes often introduce bias in the final result, due to time discretization. Using an auxiliary Poisson process, it is possible to run simulations which are unbiased. In this article, we propose such a Monte Carlo scheme which converges to the exact value. We manage to keep the s…
This paper uses Monte Carlo simulation to value quality options in agricultural futures contracts.
problem Valuation of quality options in agricultural futures to prevent manipulation and improve hedging performance.
method Monte Carlo simulation with antithetic variables for efficiency.
result Demonstrates a method to estimate the value of quality options in agricultural futures contracts.
New method combines neural networks with Monte Carlo for complex system reliability.
problem Estimating small failure probabilities in complex systems.
method Subset Simulation with Hamiltonian Neural Networks.
result High acceptance rates and computational efficiency in low-probability regions.
GPU computing has become popular in computational finance and many financial institutions are moving their CPU based applications to the GPU platform. Since most Monte Carlo algorithms are embarrassingly parallel, they benefit greatly from parallel implementations, and consequently Monte Carlo has become a focal point …
Normal distributions ensure asymptotic variance reduction in moment matching Monte Carlo.
problem Asymptotic variance reduction in general integration problems.
method Characterization of conditions for asymptotic variance reduction using normal distributions.
result Asymptotic variance reduction is guaranteed for normal distributions in moment matching Monte Carlo.
Recommender systems play an essential role in the modern business world. They recommend favorable items like books, movies, and search queries to users based on their past preferences. Applying similar ideas and techniques to Monte Carlo simulations of physical systems boosts their efficiency without sacrificing accura…
Utility based methods provide a very general theoretically consistent approach to pricing and hedging of securities in incomplete financial markets. Solving problems in the utility based framework typically involves dynamic programming, which in practise can be difficult to implement. This article presents a Monte Carl…
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.
GPU speeds up Monte Carlo simulations for large time steps.
problem Slow convergence and inaccurate solutions with large time steps in Monte Carlo simulations.
method Generalizes the Seven League scheme for GPU acceleration.
result Significantly improved computational speed.
We show that deliberately introducing a nested simulation stage can lead to significant variance reductions when comparing two stopping times by Monte Carlo. We derive the optimal number of nested simulations and prove that the algorithm is remarkably robust to misspecifications of this number. The method is applied to…
Developed scalable Monte Carlo method for VIX option pricing.
problem VIX option pricing in stochastic Volterra rough volatility models with non-Markovian vol-of-vol.
method Infinite dimensional Markovian representation to devise scalable least squares Monte Carlo.
result Efficient VIX option pricing method for generalized models.
A new RL framework tackles asset allocation problems using Monte Carlo simulation.
problem Existing asset allocation methods fail to consider portfolio management and financial market characteristics.
method Proposes a new reinforcement learning framework that considers portfolio state and uses Monte Carlo simulation to prevent overfitting.
result The proposed method outperforms benchmarks in various test intervals.
Active Kriging Monte Carlo simulation method with conformal certification for failure probability estimation
problem Failure probability estimation in structural reliability analysis
method Active learning framework with conformal prediction
result Improved uncertainty quantification and reliability of failure probability estimates
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.
A new differentiable resampling method for Monte Carlo simulations.
problem Improving the efficiency and differentiability of resampling in Monte Carlo simulations.
method Proposes a diffusion model surrogate for resampling, proving consistency and outperforming existing methods.
result The proposed method outperforms state-of-the-art differentiable resampling methods on various benchmarks.
Enhances SBI accuracy with multilevel Monte Carlo for expensive simulators.
problem Limited accuracy in SBI due to expensive simulators.
method Multilevel Monte Carlo techniques for cost-effective SBI.
result Significant enhancement in SBI accuracy with fixed computational budget.
Pricing Chinese convertible bonds using Monte Carlo simulation and dynamic programming.
problem Pricing Chinese convertible bonds accurately.
method Monte Carlo simulation and dynamic programming with regression and backward induction.
result An underpriced strategy significantly outperforms benchmarks.
We discuss suitable classes of diffusion processes, for which functionals relevant to finance can be computed via Monte Carlo methods. In particular, we construct exact simulation schemes for processes from this class. However, should the finance problem under consideration require e.g. continuous monitoring of the pro…
Proposes a new simulator for complex arrival processes.
problem Modeling and simulating complex arrival processes with non-stationary and multi-dimensional rates.
method Integrates Monte Carlo and GANs to model a broad class of arrival processes.
result Consistent and efficient estimation of the simulator using Wasserstein distance.
New MC simulation methods use classifiers to estimate pdf ratios without explicit pdfs.
problem Estimating ratios of probability density functions (pdfs) without explicit pdfs.
method Proposes classifier-based pdf-free versions of MC simulation algorithms.
result Enables pdf-free simulation algorithms using surrogate functions computed by classifiers.
fintech-kMC simulates financial platforms for AI/ML model validation.
problem Validation of AI/ML models in real-world financial applications.
method Agent-based model with kinetic Monte Carlo engine.
result Generates realistic synthetic data for testing AI/ML models.
Study on interest rate model with jumps, proving strong convergence in simulations.
problem Analytical solutions for complex interest rate models with jumps are difficult.
method Employed truncated Euler-Maruyama techniques to prove strong convergence.
result Justified strong convergence for Monte Carlo calibration and valuation.
Optimizes K inner simulations for least-square Monte Carlo to reduce computational cost.
problem Computing conditional expectation E[f (Y)|X] with limited samples.
method Determines optimal number of Y samples (K) for given computational budget.
result Computational gain is maximized when sampling Y given X is inexpensive.
This paper discusses the exact simulation of the stock price process underlying the 3/2 model. Using a result derived by Craddock and Lennox using Lie Symmetry Analysis, we adapt the Broadie-Kaya algorithm for the simulation of affine processes to the 3/2 model. We also discuss variance reduction techniques and find th…
ParaDRAM automates parallel MCMC simulations across languages.
problem Efficiently sampling complex mathematical functions.
method Automated, parallel, high-performance MCMC with adaptive rejection.
result Significant memory reduction for complex simulations.
Adaptive Multilevel Monte Carlo improves probability estimation for complex random variables.
problem Estimating probabilities of complex random variables with multiple approximations.
method Adaptive Multilevel Monte Carlo framework for discontinuous functionals.
result Achieves optimal computational complexities for both smooth and discontinuous functionals.
In this paper, we discuss the application of quasi-Monte Carlo methods to the Heston model. We base our algorithms on the Broadie-Kaya algorithm, an exact simulation scheme for the Heston model. As the joint transition densities are not available in closed-form, the Linear Transformation method due to Imai and Tan, a p…
This paper develops a new framework to assess crypto portfolio risk using simulation methods.
problem Traditional financial risk models fail to capture crypto market characteristics like volatility and contagion.
method The framework integrates four components: volatility stress testing, hedging, contagion modeling, and Monte Carlo simulation.
result The framework robustly assesses crypto portfolio risk and is validated with real data.
Sequential Monte Carlo (SMC) methods have successfully been used in many applications in engineering, statistics and physics. However, these are seldom used in financial option pricing literature and practice. This paper presents SMC method for pricing barrier options with continuous and discrete monitoring of the barr…
Generation of pseudorandom numbers from different probability distributions has been studied extensively in the Monte Carlo simulation literature. Two standard generation techniques are the acceptance-rejection and inverse transformation methods. An alternative approach to Monte Carlo simulation is the quasi-Monte Carl…
The paper analyzes portfolio credit risk using Archimedean copulas and introduces efficient simulation methods.
problem Analyzing large losses from credit portfolio defaults with Archimedean copulas.
method Derives asymptotic results and develops variance reduction algorithms for Monte Carlo simulations.
result Proposed algorithms significantly enhance classical Monte Carlo methods for estimating portfolio credit risk.
Policy analysts wish to visualize a range of policies for large simulator-defined Markov Decision Processes (MDPs). One visualization approach is to invoke the simulator to generate on-policy trajectories and then visualize those trajectories. When the simulator is expensive, this is not practical, and some method is r…
Develops methods to simulate option prices for a specific stochastic volatility model.
problem No method exists to compute option prices numerically for a non-martingale jump-type model.
method Develops two Monte Carlo simulation methods under change of measure.
result Conducts numerical experiments to validate the developed methods.