Andrea Gombani

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We consider interest rate models where the forward rates are allowed to be driven by a multidimensional Wiener process as well as by a marked point process. Assuming a deterministic volatility structure, and using ideas from systems and control theory, we investigate when the inputoutput map generated by such a model can be realized by a finite dimensional(More)
We present an alternative approach to the pricing of bonds and bond derivatives in a multivariate factor model for the term structure of interest rates that is based on the solution of an optimal stochastic control problem. It can also be seen as an alternative to the classical approach of computing forward prices by forward measures and as such can be(More)
We study the concept of a mixed matrix in connection with linear fractional transformations of lossless and passive matrix-valued rational functions, and show that they can be parametrized by sequences of elementary chain matrices. These notions are exemplified on a model of a Surface Acoustic Wave filter for which a state-space realization is carried out(More)
In this paper we investigate some aspect of the Nevanlinna-Pick and Schur interpolation problem formulated for Schur-functions considered on the right-half plane of C. We consider the well established parametrization of the solution Q = TΘ(S) := (SΘ12 + Θ22)(SΘ11 + Θ21) (see e.g. [2],[6]), where the J-inner function Θ is completely determined by the(More)
We present an approach for pricing of illiquid bonds (and bond derivatives) in an arbitrage-free way and consistently with observed prices of liquid bonds. The basic model is a multifactor term structure model with abstract latent factors. The approach is based on stochastic filtering techniques, leading to a continuous update of the distribution of the(More)
We present a new parametrization of inner functions based on the Schur algorithm. We make use of state space formulas (in practice we obtain a new parametrization of observable pairs). The main advantage of our parametrization is that for each chart the observability gramian is constant: this leads to a very good behavior in some approximation problems.