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Consistency of the drift parameter estimator for the discretized fractional Ornstein–Uhlenbeck process with Hurst index $Hin(0,rac{1}{2})$

机译:具有Hurst指数$ H in(0, frac {1} {2})$的离散分数阶Ornstein–Uhlenbeck过程的漂移参数估计量的一致性

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We consider the Langevin equation which contains an unknown drift parameter $heta$ and where the noise is modeled as fractional Brownian motion with Hurst index $Hin(0,rac{1}{2})$. The solution corresponds to the fractional Ornstein–Uhlenbeck process. We construct an estimator, based on discrete observations in time, of the unknown drift parameter, that is similar in form to the maximum likelihood estimator for the drift parameter in Langevin equation with standard Brownian motion. It is assumed that the interval between observations is $n^{-1}$, i.e. tends to zero (high-frequency data) and the number of observations increases to infinity as $n^{m}$ with $m>1$. It is proved that for strictly positive $heta$ the estimator is strongly consistent for any $m>1$, while for $hetaleq0$ it is consistent when $m>rac{1}{2H}$.
机译:我们考虑包含一个未知的漂移参数$ theta $的Langevin方程,其中的噪声被建模为具有Hurst索引$ H in(0, frac {1} {2})$的分数布朗运动。该解对应于分数Ornstein–Uhlenbeck过程。我们基于时间的离散观测值构造了未知漂移参数的估计器,该估计器的形式与标准布朗运动的Langevin方程中漂移参数的最大似然估计器相似。假设观测之间的间隔为$ n ^ {-1} $,即趋于零(高频数据),并且观测数量随着$ n ^ {m} $增大为无穷大,其中$ m> 1 $ 。证明了对于严格正的$ theta,估计对于任何$ m> 1 $都是高度一致的,而对于$ theta leq0 $,当$ m> frac {1} {2H} $时,估计是一致的。

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