Ludovic Giet

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A Minimum Disparity Distance Estimator minimizes a φ-divergence between the marginal density of a parametric model and its non-parametric estimate. This principle is applied to the estimation of stochastic differential equation models, choosing the Hellinger distance as particular φ−divergence. Under an hypothesis of stationar-ity, the parametric marginal(More)
The linear Ornstein-Ulenbeck diffusion model is too simple to describe the movement of short term interest rates. However diffusions with a non-linear drift and volatility function have no closed form likelihood function which make inference either classical or Bayesian very problematic. A vast range of approximation were proposed in the literature. In this(More)
We propose a new approach for modelling non-linear multivariate interest rate processes based on copulas and reducible stochastic di¤erential equations (SDEs). In the modelling of the marginal processes, we consider a class of non-linear SDEs that are reducible to Ornstein-Uhlenbeck (OU) process or Cox, Ingersoll, and Ross (1985) (CIR) process. The(More)
We propose a new approach for modeling non-linear multivariate interest rate processes based on time-varying copulas and reducible stochastic differential equations (SDEs). In the modeling of the marginal processes, we consider a class of non-linear SDEs that are reducible to Ornstein-Uhlenbeck (OU) process or Cox, Ingersoll, and Ross (1985) (CIR) process.(More)
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