本文讨论具有一致连续系数条件扩散过程的大偏差性质。设X(t)是具有Dirichlet空间(ξ、H_0~1(P_0~d))的扩散过程,其中 ξ(f,g)=1/2 integral from n=R^d to (〈▽f,▽g〉(x)dx)。 P_a^e是过程x_6(t)=x(∈t)满足条件x_6(0)=x,x_6(1)=y的...本文讨论具有一致连续系数条件扩散过程的大偏差性质。设X(t)是具有Dirichlet空间(ξ、H_0~1(P_0~d))的扩散过程,其中 ξ(f,g)=1/2 integral from n=R^d to (〈▽f,▽g〉(x)dx)。 P_a^e是过程x_6(t)=x(∈t)满足条件x_6(0)=x,x_6(1)=y的律。那么当∈→0时,(P_(?)^(?),y)具有大偏差性质,且具有速率函数 J_(x,y)(ω)=1/2 integral from n=0 to 1(〈(?)(t),a(-1)(ω(t)),(?)(t)〉dt-1/2 d^2(x,y)。展开更多
This paper addresses the estimation problem of an unknown drift parameter matrix for a fractional Ornstein-Uhlenbeck process in a multi-dimensional setting.To tackle this problem,we propose a novel approach based on r...This paper addresses the estimation problem of an unknown drift parameter matrix for a fractional Ornstein-Uhlenbeck process in a multi-dimensional setting.To tackle this problem,we propose a novel approach based on rough path theory that allows us to construct pathwise rough path estimators from both continuous and discrete observations of a single path.Our approach is particularly suitable for high-frequency data.To formulate the parameter estimators,we introduce a theory of pathwise Itôintegrals with respect to fractional Brownian motion.By establishing the regularity of fractional Ornstein-Uhlenbeck processes and analyzing the long-term behavior of the associated Lévy area processes,we demonstrate that our estimators are strongly consistent and pathwise stable.Our findings offer a new perspective on estimating the drift parameter matrix for fractional Ornstein-Uhlenbeck processes in multi-dimensional settings,and may have practical implications for fields including finance,economics,and engineering.展开更多
文摘本文讨论具有一致连续系数条件扩散过程的大偏差性质。设X(t)是具有Dirichlet空间(ξ、H_0~1(P_0~d))的扩散过程,其中 ξ(f,g)=1/2 integral from n=R^d to (〈▽f,▽g〉(x)dx)。 P_a^e是过程x_6(t)=x(∈t)满足条件x_6(0)=x,x_6(1)=y的律。那么当∈→0时,(P_(?)^(?),y)具有大偏差性质,且具有速率函数 J_(x,y)(ω)=1/2 integral from n=0 to 1(〈(?)(t),a(-1)(ω(t)),(?)(t)〉dt-1/2 d^2(x,y)。
基金supported by Shanghai Artificial Intelligence Laboratory.
文摘This paper addresses the estimation problem of an unknown drift parameter matrix for a fractional Ornstein-Uhlenbeck process in a multi-dimensional setting.To tackle this problem,we propose a novel approach based on rough path theory that allows us to construct pathwise rough path estimators from both continuous and discrete observations of a single path.Our approach is particularly suitable for high-frequency data.To formulate the parameter estimators,we introduce a theory of pathwise Itôintegrals with respect to fractional Brownian motion.By establishing the regularity of fractional Ornstein-Uhlenbeck processes and analyzing the long-term behavior of the associated Lévy area processes,we demonstrate that our estimators are strongly consistent and pathwise stable.Our findings offer a new perspective on estimating the drift parameter matrix for fractional Ornstein-Uhlenbeck processes in multi-dimensional settings,and may have practical implications for fields including finance,economics,and engineering.
