Zobrazeno 1 - 10
of 792
pro vyhledávání: '"Pagès, Gilles"'
We design a fully implementable scheme to compute the invariant distribution of ergodic McKean-Vlasov SDE satisfying a uniform confluence property. Under natural conditions, we prove various convergence results notably we obtain rates for the Wassers
Externí odkaz:
http://arxiv.org/abs/2406.13370
Autor:
Pagès, Gilles, Yeo, Christian
We investigate propagation of convexity and convex ordering on a typical stochastic optimal control problem, namely the pricing of \q{\emph{Take-or-Pay}} swing option, a financial derivative product commonly traded on energy markets. The dynamics of
Externí odkaz:
http://arxiv.org/abs/2406.07464
Autor:
Pagès, Gilles
We investigate the properties of the solutions of scaled Volterra equations (i.e. with an affine mean-reverting drift) in terms of stationarity at both a finite horizon and on the long run. In particular we prove that such an equation never has a sta
Externí odkaz:
http://arxiv.org/abs/2401.15021
Autor:
Jourdain, Benjamin, Pagès, Gilles
In this paper, we are interested in the propagation of convexity by the strong solution to a one-dimensional Brownian stochastic differential equation with coefficients Lipschitz in the spatial variable uniformly in the time variable and in the conve
Externí odkaz:
http://arxiv.org/abs/2312.09779
Autor:
Bras, Pierre, Pagès, Gilles
We propose a new algorithm for variance reduction when estimating $f(X_T)$ where $X$ is the solution to some stochastic differential equation and $f$ is a test function. The new estimator is $(f(X^1_T) + f(X^2_T))/2$, where $X^1$ and $X^2$ have same
Externí odkaz:
http://arxiv.org/abs/2307.12703
We propose two parametric approaches to evaluate swing contracts with firm constraints. Our objective is to define approximations for the optimal control, which represents the amounts of energy purchased throughout the contract. The first approach in
Externí odkaz:
http://arxiv.org/abs/2306.03822
We propose a new theoretical framework that exploits convolution kernels to transform a Volterra path-dependent (non-Markovian) stochastic process into a standard (Markovian) diffusion process. This transformation is achieved by embedding a Markovian
Externí odkaz:
http://arxiv.org/abs/2306.02708
Autor:
Bras, Pierre, Pagès, Gilles
Stochastic Gradient Descent Langevin Dynamics (SGLD) algorithms, which add noise to the classic gradient descent, are known to improve the training of neural networks in some cases where the neural network is very deep. In this paper we study the pos
Externí odkaz:
http://arxiv.org/abs/2212.12018
Autor:
Jourdain, Benjamin, Pagès, Gilles
In this paper, we are interested in comparing solutions to stochastic Volterra equations for the convex order on the space of continuous $\R^d$-valued paths and for the monotonic convex order when $d=1$. Even if in general these solutions are neither
Externí odkaz:
http://arxiv.org/abs/2211.10186
Convergence of Langevin-Simulated Annealing algorithms with multiplicative noise II: Total Variation
Autor:
Bras, Pierre, Pagès, Gilles
We study the convergence of Langevin-Simulated Annealing type algorithms with multiplicative noise, i.e. for $V : \mathbb{R}^d \to \mathbb{R}$ a potential function to minimize, we consider the stochastic differential equation $dY_t = - \sigma \sigma^
Externí odkaz:
http://arxiv.org/abs/2205.15039