Advances and Applications in Statistics
Volume 62, Issue 2, Pages 203 - 226
(June 2020) http://dx.doi.org/10.17654/AS062020203 |
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EFFICIENT ESTIMATORS FOR GEOMETRIC FRACTIONAL BROWNIAN MOTION PERTURBED BY FRACTIONAL ORNSTEIN-UHLENBECK PROCESS
Mohammed Alhagyan, Masnita Misiran and Zurni Omar
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Abstract: This paper discusses an enhanced model of geometric fractional Brownian motion where its volatility is assumed to be stochastic volatility model obey fractional Ornstein-Uhlenbeck process. The method of estimation for all parameters in this model are derived. After, simulation experiments are conducted to examine the performance of the proposed estimators. The result shows that the proposed method provides efficient estimates for the parameters. Thus, the proposed model is promising and can apply in real financial environments. |
Keywords and phrases: geometric fractional Brownian motion, fractional Ornstein-Uhlenbeck process, long memory stochastic volatility, innovation algorithm, constraint transcription method, segmentation.
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