Proyecciones Journal of Mathematics Vol. 35, N^o 2, pp. 197-211, June 2016. Universidad Católica del Norte Antofagasta - Chile

Parametric Estimation and the CIR Model

Eugenio Saavedra

Universidad de Santiago de Chile

Chile 

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ABSTRACT

We study parametric estimation in the Cox-Ingersoll-Ross model and establish the stochastic differential equations for the parameters involved in it.

Keywords: estimating function, quasi—likelihood, stochastic differential equation.

AMS Classification : 60H10, 62P20, 62M99.

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REFERENCES

[1]    Brown, R. H., Schaefer, S. M., The Term Structure of Real Interest Rates and the Cox, Ingersoll and Ross Model, J. of Financial Eco-nomics 35, pp. 3-42, (1994).

[2]    Cox, J. S., Ingersoll, J. E., Ross, S. A., A theory of term structure of interest rates, Econometrica. 53, pp. 363-384, (1985).

[3]    Feigin, P., Maximum likelihood estimation for continuous-time stochas-tic processes, J. Appl. Probab. 8, pp. 712-736, (1976).

[4]    Heyde, C., Quasi-Likelihood and its Aplications, Springer, New York, (1997).

[5]    Kloeden, P., Platen, E., Numerical Solution of Stochastic Differential Equations, Second Edition, Springer, Berlin, (1995).

[6]    Kloeden, P., Platen, E., Shurz, H., Sorensen, M., On effects of dis-cretization on estimators of drift parameters for diffusion processes, J. Appl. Probab. 33, pp. 1061-1076, (1996).

[7]    Protter, P., Stochastic Integration and Differential Equations, Springer, New York, (1990).

Eugenio Saavedra G.

Departamento de Matematica y C. C. Universidad de Santiago de Chile, Casilla 307, Correo 2, Santiago,

Chile

e-mail : eugenio.saavedra@usach.cl

Part of this work was supported by DICYT Grant # 049833SG

Received : January 2014. Accepted : March 2016