Nonparametric estimation of a scalar diffusion model from discrete time data: a survey

被引：2

作者：

Gourieroux, Christian ^{[1
,2
]}

Nguyen, Hung T. ^{[3
]}

Sriboonchitta, Songsak ^{[4
]}

机构：

[1] CREST, Paris, France

[2] Univ Toronto, Toronto, ON, Canada

[3] New Mexico State Univ, Las Cruces, NM 88003 USA

[4] Chiang Mai Univ, Fac Econ, Chiang Mai 52000, Thailand

来源：

ANNALS OF OPERATIONS RESEARCH | 2017年 / 256卷 / 02期

关键词：

Diffusion model; Local time; Low frequency data; Nonlinear canonical analysis; Prediction operator; SPECIFICATION; DENSITIES; EQUATIONS; DYNAMICS; SERIES;

D O I：

10.1007/s10479-016-2273-6

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

In view of rapid developments on nonparametric estimation of the drift and volatility functions in scalar diffusion models in financial econometrics, from discrete-time observations, we provide, in this paper, a survey of its state-of-the-art with new insights into current practices, as well as elaborating on our own recent contributions. In particular, in presenting the main principles of estimation for both stationary and nonstationary cases, we show the possibility to estimate nonparametrically the drift and volatility functions without distinguishing these two frameworks.

引用

页码：203 / 219

页数：17

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