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On Conditional least squares estimation for the $$ AD (1,n)$$ A D ( 1 , n ) model based on discrete-time observations

Author

Listed:
  • Mohamed Ben Alaya

    (Université de Rouen Normandie, Laboratoire de Mathématiques Raphaël Salem)

  • Houssem Dahbi

    (Université de Rouen Normandie, Laboratoire de Mathématiques Raphaël Salem
    Université de Sousse, Laboratoire LAMMDA)

  • Hamdi Fathallah

    (Université de Sousse, Laboratoire LAMMDA)

Abstract

This paper addresses the problem of global parameter estimation for the $$ AD (1,n)$$ A D ( 1 , n ) model, where n is a positive integer. The $$ AD (1,n)$$ A D ( 1 , n ) model is a subclass of affine diffusions introduced by Duffie, Filipovi?, and Schachermayer in Duffie et al. (2003). Affine diffusion models are widely used in the pricing of bonds and stock options, including the Vasicek, Cox-Ingersoll-Ross, and Heston models. Our main results concern the conditional least squares estimation of the drift parameters of the $$ AD (1,n)$$ A D ( 1 , n ) model, based on high-frequency discrete-time observations over an infinite horizon. We then analyze the asymptotic properties of the estimators in both ergodic and non-ergodic cases. Additionally, this paper presents some moment results related to the $$ AD (1,n)$$ A D ( 1 , n ) model.

Suggested Citation

  • Mohamed Ben Alaya & Houssem Dahbi & Hamdi Fathallah, 2025. "On Conditional least squares estimation for the $$ AD (1,n)$$ A D ( 1 , n ) model based on discrete-time observations," Statistical Inference for Stochastic Processes, Springer, vol. 28(3), pages 1-36, December.
    • Handle: RePEc:spr:sistpr:v:28:y:2025:i:3:d:10.1007_s11203-025-09337-6
    DOI: 10.1007/s11203-025-09337-6
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