Data driven confidence intervals for diffusion process using double smoothing empirical likelihood

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:348

Article

Yang, Qi¹  Song, Yuping² 

[1] Chinese Univ Hong Kong, Dept Stat, Hong Kong, Peoples R China

[2] Shanghai Normal Univ, Sch Finance & Business, Shanghai, Peoples R China

关键词: Empirical likelihood;    Diffusion process;    Volatility function;    Confidence interval;    Double smoothing;   

DOI  :  10.1016/j.cam.2018.08.027

来源: Elsevier

【 摘 要 】

In this paper, we propose an innovative estimation raethod for the volatility function in diffusion process and construct the empirical likelihood confidence interval for it. Compared with double smoothing local constant estimator, double smoothing local linear estimator proposed in this paper is an inventive estimation method. Moreover, we find that the empirical likelihood confidence interval constructed with the approximate estimating equation is much better than the one based on the estimating equation, since the latter undermines the predictability of the estimator. Accordingly, new algorithm for simulation is proposed. Through Monte Carlo simulation and empirical analysis, both these approaches are superior to traditional asymptotic normality confidence interval in terms of coverage rate, etc. (C) 2018 Elsevier B.V. All rights reserved.

【 授权许可】

Free