Calibration of the Modified Geometric Ornstein-Uhlenbeck Process Through the Maximum Log-Likelihood Method. An Example with Gold Prices (Calibraciin del Proceso Ornstein-Uhlenbeck Geommtrico Modificado a travvs del MMtodo de MMxima Log-Verosimilitud. Un Ejemplo con los Precios del Oro)

2017 ◽  
Author(s):  
Carlos Mejia
Author(s):  
Leonidas Sakalauskas ◽  
Jurgis Susinskas

In this paper the Bayesian approach to global optimization of univariate continuous functions is developed, when the objective function is modelled by Ornstein-Uhlenbeck process. The parameters of model of function to be optimised are calibrated by maximal likelihood method using the learning set. The resulting optimization algorithm is rather simple and consists of reselection of values of expected step utility function, which maximizes at each step the expected increment of minimal observed value of the objective function. The convergence of method developed is studied by theoretical and experimental way. Efficiency of the Bayes optimization method created is studied by computer simulation, too.


2020 ◽  
Vol 21 (02) ◽  
pp. 2150027
Author(s):  
Hui Jiang ◽  
Hui Liu

For the Ornstein–Uhlenbeck process in stationary and explosive cases, this paper studies Cramér-type moderate deviations for the log-likelihood ratio process. As an application, we give the negative regions of drift testing problem, and also obtain the decay rates of the error probabilities. The main methods of this paper consist of mod-[Formula: see text] convergence approach, deviation inequalities for multiple Wiener–Itô integrals and asymptotic analysis techniques.


2020 ◽  
Vol 23 (2) ◽  
pp. 450-483 ◽  
Author(s):  
Giacomo Ascione ◽  
Yuliya Mishura ◽  
Enrica Pirozzi

AbstractWe define a time-changed fractional Ornstein-Uhlenbeck process by composing a fractional Ornstein-Uhlenbeck process with the inverse of a subordinator. Properties of the moments of such process are investigated and the existence of the density is shown. We also provide a generalized Fokker-Planck equation for the density of the process.


2017 ◽  
Vol 429 ◽  
pp. 35-45 ◽  
Author(s):  
Krzysztof Bartoszek ◽  
Sylvain Glémin ◽  
Ingemar Kaj ◽  
Martin Lascoux

2012 ◽  
Vol 218 (23) ◽  
pp. 11570-11582 ◽  
Author(s):  
V. Giorno ◽  
A.G. Nobile ◽  
R. di Cesare

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