Modified non-transformed principal component and adaptive penalized high dimension for grouping effect of stock market price

Nonstationary time series is complex and difficult to be modelled. Many researchers resolved it by transforming it into stationary time series. However, loss of generality will occur which make its inference more difficult. To overcome this, therefore a modified non-transformed approach is proposed...

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Main Author:	Andu, Yusrina
Format:	Thesis
Language:	English
Published:	2020
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/102444/1/YusrinaAnduPFS2020.pdf.pdf
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spelling	my-utm-ep.1024442023-08-28T06:40:39Z Modified non-transformed principal component and adaptive penalized high dimension for grouping effect of stock market price 2020 Andu, Yusrina QA Mathematics Nonstationary time series is complex and difficult to be modelled. Many researchers resolved it by transforming it into stationary time series. However, loss of generality will occur which make its inference more difficult. To overcome this, therefore a modified non-transformed approach is proposed using generalized dynamic principal component on the nonstationary series. On the other hand, the selection of informative variables is more importance, especially when the number of explanatory variables is larger than the number of observations. This is pertinent in order to achieve a better model interpretation of the highly correlated variables. Thus, the penalized likelihood methods are mostly adapted since they are able to perform variable selection and model estimation concomitantly. Nevertheless, the scarceness in the consistency of variable selection, encouragement of grouping effects and robustness can be found in the majority of these methods. Therefore, to overcome these shortcomings, several improvements in the high dimensional penalized methods are proposed in this study. The performance of homogenous variable selection was improved using ordered homogeneity pursuit least absolute shrinkage and selection operator method. An initial weight which is distance correlation is proposed in the adaptive elastic net to encourage grouping effects between highly correlated variables in high dimension data. Furthermore, this proposed method also has the capability to improve the robustness in the regression model, especially when outliers are presence in the response variable or there is a heavy-tailed distribution in the error. Three algorithms were developed for the simulation of the modified non-transformed principal component and proposed adaptive penalized high dimension methods. In this study, the effectiveness of all the modified and proposed methods was examined through three simulation studies and also through the application of stock market price. It is known that the studies that perform variable selection, encouraging grouping effects and robustness in high dimensional stock market price using the statistical approach is still scarce. In conclusion, the modified and proposed high dimensional penalized methods provide much better performance results both for the simulation and real data application as compared to their counterpart. 2020 Thesis http://eprints.utm.my/id/eprint/102444/ http://eprints.utm.my/id/eprint/102444/1/YusrinaAnduPFS2020.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:145895 phd doctoral Universiti Teknologi Malaysia Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Andu, Yusrina Modified non-transformed principal component and adaptive penalized high dimension for grouping effect of stock market price
description	Nonstationary time series is complex and difficult to be modelled. Many researchers resolved it by transforming it into stationary time series. However, loss of generality will occur which make its inference more difficult. To overcome this, therefore a modified non-transformed approach is proposed using generalized dynamic principal component on the nonstationary series. On the other hand, the selection of informative variables is more importance, especially when the number of explanatory variables is larger than the number of observations. This is pertinent in order to achieve a better model interpretation of the highly correlated variables. Thus, the penalized likelihood methods are mostly adapted since they are able to perform variable selection and model estimation concomitantly. Nevertheless, the scarceness in the consistency of variable selection, encouragement of grouping effects and robustness can be found in the majority of these methods. Therefore, to overcome these shortcomings, several improvements in the high dimensional penalized methods are proposed in this study. The performance of homogenous variable selection was improved using ordered homogeneity pursuit least absolute shrinkage and selection operator method. An initial weight which is distance correlation is proposed in the adaptive elastic net to encourage grouping effects between highly correlated variables in high dimension data. Furthermore, this proposed method also has the capability to improve the robustness in the regression model, especially when outliers are presence in the response variable or there is a heavy-tailed distribution in the error. Three algorithms were developed for the simulation of the modified non-transformed principal component and proposed adaptive penalized high dimension methods. In this study, the effectiveness of all the modified and proposed methods was examined through three simulation studies and also through the application of stock market price. It is known that the studies that perform variable selection, encouraging grouping effects and robustness in high dimensional stock market price using the statistical approach is still scarce. In conclusion, the modified and proposed high dimensional penalized methods provide much better performance results both for the simulation and real data application as compared to their counterpart.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Andu, Yusrina
author_facet	Andu, Yusrina
author_sort	Andu, Yusrina
title	Modified non-transformed principal component and adaptive penalized high dimension for grouping effect of stock market price
title_short	Modified non-transformed principal component and adaptive penalized high dimension for grouping effect of stock market price
title_full	Modified non-transformed principal component and adaptive penalized high dimension for grouping effect of stock market price
title_fullStr	Modified non-transformed principal component and adaptive penalized high dimension for grouping effect of stock market price
title_full_unstemmed	Modified non-transformed principal component and adaptive penalized high dimension for grouping effect of stock market price
title_sort	modified non-transformed principal component and adaptive penalized high dimension for grouping effect of stock market price
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Science
publishDate	2020
url	http://eprints.utm.my/id/eprint/102444/1/YusrinaAnduPFS2020.pdf.pdf
_version_	1776100924453814272

Modified non-transformed principal component and adaptive penalized high dimension for grouping effect of stock market price

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