scholarly journals Optimal portfolios based on weakly dependent data

2015 ◽  
Author(s):  
Ken-ichi Yoshihara ◽  
Shuya Kanagawa ◽  
Tatsuhiko Saigo ◽  
Hiroshi Takahashi
Keyword(s):  
Dependent Data ◽  
Optimal Portfolios ◽  
Weakly Dependent Data ◽  
Weakly Dependent

Convergence of changepoint estimators for weakly dependent data

1997 ◽  
Vol 8 (4) ◽  
pp. 379-392 ◽  
Author(s):  
Shuya Kanagawa ◽  
Seiji Takano ◽  
Ken-Ichi Yoshihara
Keyword(s):  
Dependent Data ◽  
Weakly Dependent Data ◽  
Weakly Dependent

BICKEL–ROSENBLATT TEST FOR WEAKLY DEPENDENT DATA

2012 ◽  
Vol 17 (3) ◽  
pp. 383-395 ◽  
Author(s):  
Janis Valeinis ◽  
Audris Locmelis
Keyword(s):  
Goodness Of Fit ◽  
Simulation Analysis ◽  
Dependent Data ◽  
Practical Implementation ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Dependent Observations ◽  
Simple Hypothesis ◽  
Weakly Dependent Data ◽  
Weakly Dependent

The aim of this paper is to analyze the Bickel–Rosenblatt test for simple hypothesis in case of weakly dependent data. Although the test has nice theoretical properties, it is not clear how to implement it in practice. Choosing different band-width sequences first we analyze percentage rejections of the test statistic under H0 by some empirical simulation analysis. This can serve as an approximate rule for choosing the bandwidth in case of simple hypothesis for practical implementation of the test. In the recent paper [12] a version of Neyman goodness-of-fit test was established for weakly dependent data in the case of simple hypotheses. In this paper we also aim to compare and discuss the applicability of these tests for both independent and dependent observations.

PREDICTION AND SIGNAL EXTRACTION OF STRONGLY DEPENDENT PROCESSES IN THE FREQUENCY DOMAIN

2002 ◽  
Vol 18 (3) ◽  
pp. 584-624 ◽  
Author(s):  
J. Hidalgo ◽  
Y. Yajima
Keyword(s):  
Spectral Density Function ◽  
Signal Extraction ◽  
Dependent Data ◽  
Canonical Factorization ◽  
Linear Least Squares ◽  
Preceding Work ◽  
Finite Set ◽  
Weakly Dependent Data ◽  
Dependent Processes ◽  
Weakly Dependent

We frequently observe that one of the aims of time series analysts is to predict future values of the data. For weakly dependent data, when the model is known up to a finite set of parameters, its statistical properties are well documented and exhaustively examined. However, if the model was misspecified, the predictors would no longer be correct. Motivated by this observation and because of the interest in obtaining adequate and reliable predictors, Bhansali (1974, Journal of the Royal Statistical Society, Series B 36, 61–73) examined the properties of a nonparametric predictor based on the canonical factorization of the spectral density function given in Whittle (1963, Prediction and Regulation by Linear Least Squares) and known as FLES.However, the preceding work does not cover the so-called strongly dependent data. Because of the interest in this type of processes, one of our objectives in this paper is to examine the properties of the FLES for these processes. In addition, we illustrate how the FLES can be adapted to recover the signal of a strongly dependent process, showing its consistency. The proposed method is semiparametric in the sense that, in contrast to other methods, we do not need to assume any particular model for the noise except that it is weakly dependent.

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