Skew-symmetric Numerical Schemes for Stochastic Differential Equations with Non-Lipschitz Drift: Weak Convergence and Long-Time Behavior

Keskeiset käsitteet

The authors propose a new class of explicit numerical schemes for simulating stochastic differential equations with non-Lipschitz drift. The schemes are based on sampling increments at each time step from a skew-symmetric probability distribution, with the level of skewness determined by the drift and volatility of the underlying process. The authors establish weak convergence of the schemes to the true diffusion process as the step-size decreases, as well as geometric ergodicity and bias control for the long-time behavior of the numerical schemes.

Tiivistelmä

The authors introduce a new class of explicit numerical schemes for simulating stochastic differential equations (SDEs) with non-Lipschitz drift. The key idea is to sample increments at each time step from a skew-symmetric probability distribution, where the level of skewness is determined by the drift and volatility of the underlying process.

The main results are:

1. Weak convergence: The authors prove that the skew-symmetric schemes converge weakly to the true diffusion process as the step-size decreases, with weak order 1. This is achieved by establishing a regularity result that extends the theory of Milstein and Tretyakov to SDEs with non-Lipschitz drift.

2. Long-time behavior: The authors show that the skew-symmetric scheme has an invariant probability measure to which it converges at a geometric rate in total variation distance. They also characterize the bias between the equilibrium distribution of the numerical scheme and the true diffusion process as the step-size goes to 0.

3. Comparison to other schemes: The authors provide a conceptual comparison of the skew-symmetric scheme to the tamed and adaptive Euler schemes, highlighting the potential benefits of the skew-symmetric approach in terms of stability and robustness to unbounded drifts.

4. Numerical experiments: The authors support their theoretical results with numerical simulations on a range of benchmark examples.

The work introduces a new class of numerical schemes that can effectively simulate SDEs with non-Lipschitz drift, which is a common challenge in various application domains such as molecular simulation, Bayesian inference, and machine learning.

Tilastot

The authors do not provide any specific numerical data or statistics in the content. The focus is on the theoretical analysis of the proposed numerical schemes.

Lainaukset

"We propose a new simple and explicit numerical scheme for time-homogeneous stochastic differential equations. The scheme is based on sampling increments at each time step from a skew-symmetric probability distribution, with the level of skewness determined by the drift and volatility of the underlying process."
"Notably, our results do not require a global Lipschitz assumption on the drift, in contrast to those required for the Euler–Maruyama scheme for long-time simulation at fixed step-sizes."
"Under appropriate assumptions, as the step-size ∆t →0, the skew-symmetric scheme converges weakly to the true diffusion in O(∆t), meaning it is weak order 1."

Tärkeimmät oivallukset

Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm

by Samu... klo arxiv.org 09-26-2024

https://arxiv.org/pdf/2405.14373.pdf

Syvällisempiä Kysymyksiä

Potential Applications of Skew-Symmetric Numerical Schemes

The skew-symmetric numerical schemes for stochastic differential equations (SDEs) have a wide range of potential applications beyond the examples discussed in the provided context. These applications can be categorized into several domains:

Financial Mathematics: In finance, the modeling of asset prices often involves SDEs with non-Lipschitz drift, particularly in the presence of jumps or heavy-tailed distributions. The skew-symmetric scheme can be employed to simulate the dynamics of such asset prices, providing a more stable and accurate numerical solution compared to traditional methods like the Euler–Maruyama scheme.

Biological Systems: Many biological processes, such as population dynamics and the spread of diseases, can be modeled using SDEs. The skew-symmetric approach can be particularly useful in scenarios where the drift terms exhibit non-Lipschitz behavior, such as in models of population growth with carrying capacity or in models of infectious disease spread with saturation effects.

Machine Learning: In the context of Bayesian inference and machine learning, the skew-symmetric scheme can be applied to sample from posterior distributions that are not well-behaved. This includes cases where the likelihood function is non-Lipschitz due to the presence of outliers or discontinuities. The stability of the skew-symmetric scheme makes it a valuable tool for Markov Chain Monte Carlo (MCMC) methods.

Physics and Engineering: In fields such as statistical mechanics and control theory, SDEs are used to model systems subject to random perturbations. The skew-symmetric scheme can be applied to simulate the behavior of such systems, particularly when dealing with complex interactions that lead to non-Lipschitz drift terms.

Environmental Modeling: The modeling of environmental processes, such as climate dynamics or pollutant dispersion, often involves SDEs with non-standard drift terms. The skew-symmetric numerical scheme can provide a robust framework for simulating these processes, allowing for better predictions and understanding of environmental phenomena.

Extending the Skew-Symmetric Scheme for Correlated Volatility Structures

To extend the skew-symmetric scheme to handle correlated volatility structures, one could consider the following approaches:

Generalized Volatility Models: Instead of assuming diagonal volatility, one could model the volatility matrix as a function of the state variable that captures correlations between different dimensions. This would involve defining a volatility function σ(x) that is a full matrix rather than a diagonal one, allowing for interactions between different stochastic processes.

Cholesky Decomposition: One practical method to incorporate correlated volatilities is to use the Cholesky decomposition of the volatility matrix. By expressing the volatility matrix as σ(x) = L(x)L(x)ᵀ, where L(x) is a lower triangular matrix, one can generate correlated random variables by transforming independent standard normal variables through this decomposition.

Multivariate Skew-Symmetric Distributions: The proposal generating mechanism could be adapted to use multivariate skew-symmetric distributions, which would allow for the introduction of correlation structures directly into the sampling process. This would involve defining the probability functions pi in a way that accounts for the joint distribution of the increments.

Adaptive Step-Size Control: Implementing an adaptive step-size control mechanism that adjusts the step size based on the estimated volatility could help manage the complexities introduced by correlated volatilities. This would ensure that the numerical scheme remains stable and convergent even in the presence of strong correlations.

Other Classes of Stochastic Differential Equations Benefiting from the Skew-Symmetric Approach

Beyond the overdamped Langevin dynamics, several other classes of stochastic differential equations could benefit significantly from the skew-symmetric approach:

Jump Diffusions: SDEs that incorporate jump processes, such as those used in financial modeling (e.g., Merton's jump diffusion model), often exhibit non-Lipschitz drift due to the discontinuities introduced by jumps. The skew-symmetric scheme can provide a more stable numerical solution in these cases, allowing for accurate simulations of asset prices that include sudden jumps.

Mean-Reverting Processes: Models such as the Ornstein-Uhlenbeck process, which exhibit mean-reverting behavior, can have drift terms that are not globally Lipschitz, especially in the presence of strong mean-reversion forces. The skew-symmetric scheme can effectively handle these dynamics, providing reliable long-term simulations.

Stochastic Control Problems: In stochastic control, where the dynamics of the controlled process can be influenced by the control strategy, the resulting SDEs may have non-Lipschitz drift due to the feedback nature of the control. The skew-symmetric approach can be advantageous in numerically solving optimal control problems under such conditions.

Nonlinear Filtering: In the context of nonlinear filtering, where the state dynamics are governed by SDEs with potentially non-Lipschitz drift, the skew-symmetric scheme can be utilized to approximate the filtering distributions, leading to improved estimates of the hidden states.

Stochastic Partial Differential Equations (SPDEs): The skew-symmetric scheme could also be extended to SPDEs, where spatial correlations and non-Lipschitz drift terms arise. This extension would require careful consideration of spatial discretization methods, but the underlying principles of the skew-symmetric approach could still apply.

In summary, the skew-symmetric numerical schemes offer a versatile and robust framework for simulating a wide range of stochastic processes, particularly in scenarios where traditional methods struggle due to non-Lipschitz drift conditions.