非遍历分数Vasicek模型的极大似然估计

IF 0.7 Q3 STATISTICS & PROBABILITY
S. Lohvinenko, K. Ralchenko
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引用次数: 6

摘要

我们研究了由随机微分方程$dX_t=(\alpha -\beta X_t)\,dt+\gamma \,dB^H_t$, $X_0=x_0$描述的分数阶Vasicek模型,该模型由已知Hurst参数$H\in (1/2,1)$的分数阶布朗运动$B^H$驱动。我们研究了任意$x_0\in \mathbb{R}$的非遍历情况下未知参数$\alpha$和$\beta$的极大似然估计量(当$\beta <0$时),推广了特定$x_0=\alpha /\beta$的Tanaka, Xiao和Yu(2019)的结果,推导了它们的渐近分布并证明了它们的渐近独立性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Maximum likelihood estimation in the non-ergodic fractional Vasicek model
We investigate the fractional Vasicek model described by the stochastic differential equation $dX_t=(\alpha -\beta X_t)\,dt+\gamma \,dB^H_t$, $X_0=x_0$, driven by the fractional Brownian motion $B^H$ with the known Hurst parameter $H\in (1/2,1)$. We study the maximum likelihood estimators for unknown parameters $\alpha$ and $\beta$ in the non-ergodic case (when $\beta <0$) for arbitrary $x_0\in \mathbb{R}$, generalizing the result of Tanaka, Xiao and Yu (2019) for particular $x_0=\alpha /\beta$, derive their asymptotic distributions and prove their asymptotic independence.
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来源期刊
Modern Stochastics-Theory and Applications
Modern Stochastics-Theory and Applications STATISTICS & PROBABILITY-
CiteScore
1.30
自引率
50.00%
发文量
0
审稿时长
10 weeks
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