Learn More
Optimal quantization has been recently revisited in multi-dimensional numerical integration (see [18]), multi-asset American option pricing (see [1]), Control Theory (see [19]) and Nonlinear Filtering Theory (see [20]). In this paper, we enlighten some numerical procedures in order to get some accurate optimal quadratic quantization of the Gaussian(More)
We investigate in this paper the numerical performances of quadratic functional quantization with some applications to Finance. We emphasize the rôle played by the so-called product quantiz-ers and the Karhunen-Lò eve expansion of Gaussian processes, in particular the Brownian motion. We show how to build some efficient functional quantizers for Brownian(More)
In this paper we study the approximation of the distribution of X t Hilbert–valued stochastic process solution of a linear parabolic stochastic partial differential equation written in an abstract form as dX t + AX t dt = Q 1/2 dW t , X 0 = x ∈ H, t ∈ [0, T ], driven by a Gaussian space time noise whose covariance operator Q is given. We assume that A −α is(More)
In this paper we investigate a discrete approximation in time and in space of a Hilbert space valued stochastic process {u(t)} t∈[0,T ] satisfying a stochastic linear evolution equation with a positive-type memory term driven by an additive Gaussian noise. The equation can be written in an abstract form as du + t 0 b(t − s)Au(s) ds dt = dW Q , t ∈ (0, T ];(More)
  • 1