Geodesic
Three-factor mean reverting Ornstein-Uhlenbeck process with stochastic drift term innovations: nonlinear autoregressive approach with dependent error
Filomat, Tome 36 (2022) no. 7, p. 2345

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DOI

This paper introduces a novel approach, withen the context of energy market, by employing a three-factor mean reverting Ornstein-Uhlenbeck process with a stochastic nonlinear autoregressive drift term having a dependent error. Initially the unique solvability for the given nonlinear system is investigated. Then, to estimate the nonlinear regression function, a semiparametric method, based on the conditional least square estimator for the parametric approach, and the nonparametric kernel method for autoregressive modification estimation have been presented. A maximum likelihood estimator has been used for parameter estimation of the Ornstein-Uhlenbeck process. Finally, some numerical simulations and real data studies have been provided to support the main conclusions of the study.
DOI : 10.2298/FIL2207345N
Classification : 60H10, 37M10, 62F10
Keywords: Energy markets, Mean reverting Ornstein Uhlenbeck process, Nonlinear autoregressive models, Semiparametric estimation 
Parisa Nabati; Arezoo Hajrajabi. Three-factor mean reverting Ornstein-Uhlenbeck process with stochastic drift term innovations: nonlinear autoregressive approach with dependent error. Filomat, Tome 36 (2022) no. 7, p. 2345 . doi: 10.2298/FIL2207345N
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     author = {Parisa Nabati and Arezoo Hajrajabi},
     title = {Three-factor mean reverting {Ornstein-Uhlenbeck} process with stochastic drift term innovations: nonlinear autoregressive approach with dependent error},
     journal = {Filomat},
     pages = {2345 },
     year = {2022},
     volume = {36},
     number = {7},
     doi = {10.2298/FIL2207345N},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.2298/FIL2207345N/}
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