论文标题
非连接分数vicek模型中的最大似然估计
Maximum likelihood estimation in the non-ergodic fractional Vasicek model
论文作者
论文摘要
我们研究了由随机微分方程$ dx_t =（α-βx_T）\，dt+γ\，db^h_t $，$ x_0 = x_0 $所描述的分数vasicek模型，由分数brownian Motion $ b^h $带有已知的hurst参数$ h \ in（1/2,1,1）。 We study the maximum likelihood estimators for unknown parameters $α$ and $β$ in the non-ergodic case (when $β<0$) for arbitrary $x_0\in \mathbb{R}$, generalizing the result of Tanaka, Xiao and Yu (2019) for particular $x_0=α/β$, derive their asymptotic distributions and prove their asymptotic independence.
We investigate the fractional Vasicek model described by the stochastic differential equation $dX_t=(α-βX_t)\,dt+γ\,dB^H_t$, $X_0=x_0$, 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 $α$ and $β$ in the non-ergodic case (when $β<0$) for arbitrary $x_0\in \mathbb{R}$, generalizing the result of Tanaka, Xiao and Yu (2019) for particular $x_0=α/β$, derive their asymptotic distributions and prove their asymptotic independence.
