On Adaptive Spectrum Estimation of Multivariate Autoregressive Locally Stationary Processes

2019 ◽  
Author(s):  
Michal Meller ◽  
Maciej Niedzwiecki ◽  
Damian Chojnacki
Keyword(s):  
Stationary Processes ◽  
Spectrum Estimation ◽  
Locally Stationary Processes ◽  
Multivariate Autoregressive

Estimation and Forecasting of Locally Stationary Processes

2011 ◽  
Vol 32 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Wilfredo Palma ◽  
Ricardo Olea ◽  
Guillermo Ferreira
Keyword(s):  
Stationary Processes ◽  
Locally Stationary Processes

STATISTICAL ESTIMATION OF OPTIMAL PORTFOLIOS FOR LOCALLY STATIONARY RETURNS OF ASSETS

2007 ◽  
Vol 10 (01) ◽  
pp. 129-154 ◽  
Author(s):  
HIROSHI SHIRAISHI ◽  
MASANOBU TANIGUCHI
Keyword(s):  
Kernel Method ◽  
Parametric Models ◽  
Stationary Processes ◽  
Optimal Portfolios ◽  
Numerical Studies ◽  
Locally Stationary Processes ◽  
Quasi Maximum Likelihood ◽  
Non Gaussian ◽  
Asymptotically Efficient ◽  
Vector Valued

This paper discusses the asymptotic property of estimators for optimal portfolios when the returns are vector-valued locally stationary processes. First, we derive the asymptotic distribution of a nonparametric portfolio estimator based on the kernel method. Optimal bandwidth and kernel function are given by minimizing the mean squares error of it. Next, assuming parametric models for non-Gaussian locally stationary processes, we prove the LAN theorem, and propose a parametric portfolio estimator ĝ based on a quasi-maximum likelihood estimator. Then it is shown that ĝ is asymptotically efficient based on the LAN. Numerical studies are provided to investigate the accuracy of the portfolio estimators parametrically and nonparametrically. They illuminate some interesting features of them.

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