Abstract:We model the term-structure modeling of interest rates by considering the forward rate as the solution of a stochastic hyperbolic partial differential equation. First, we study the arbitrage-free model of the term structure and explore the completeness of the market. We then derive results for the pricing of general contingent claims. Finally we obtain an explicit formula for a forward rate cap in the Gaussian framework from the general results.
“…The result obtained bears similarities to those of Aihara and Bagchi (2005) (in particular to Theorem 3.12), and gives a discrete-time counterpart of the continuous-time Brownian models treated there. See also the section "Diffusion market models" of the recent Rokhlin (2007).…”
“…The result obtained bears similarities to those of Aihara and Bagchi (2005) (in particular to Theorem 3.12), and gives a discrete-time counterpart of the continuous-time Brownian models treated there. See also the section "Diffusion market models" of the recent Rokhlin (2007).…”
“…Stochastic parabolic equations are used in various economical and physical models, such as the term structure of interest rates for bonds with different maturities (Aihara and Bagchi [8], [9], Cont [16]), the temperature of the top layer of the ocean (Frankignoul [20], Piterbarg and Rozovskiȋ [57]), evolution of the population in time and space (Dawson [17], De [18]), spread of pollutants (Serrano and Adomian [70], Serrano and Unny [71]), etc. Equations of the type (1.1) provide a useful toy model for understanding the possible effect of the infinite number of dimensions and for deriving the bench-mark results about the corresponding estimators.…”
A parameter estimation problem is considered for a stochastic parabolic equation driven by additive Gaussian noise that is white in time and space. The estimator is of spectral type and utilizes a finite number of the spatial Fourier coefficients of the solution. The asymptotic properties of the estimator are studied as the number of the Fourier coefficients increases, while the observation time and the noise intensity are fixed. A necessary and sufficient condition for consistency and asymptotic normality of the estimator is derived in terms of the eigenvalues of the operators in the equation, and a detailed proof is provided. Other estimation problems are briefly surveyed.
This paper treats the filtering and parameter identification for the stochastic hyperbolic systems with jump noise processes. The physical situation of this model can be found in finance problem, e.g., the term structure of the bonds is a typical example. It is well-known that the filtering algorithm including jump processes is not possible to be a closed form like Kalman filter. We eliminate the jump process from the system dynamics by using one observation data and convert the system to the Gaussian frame work. After deriving the Kalman filter and the related likelihood function, the adaptive estimation algorithm is constructed with the aid of parallel filtering scheme. Some numerical examples are demonstrated.
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