The Causal Relationship Between Stock Markets: Awavelet Transform-Based Approach

Ahmed Dghais, Amel Abdoullah (2016) The Causal Relationship Between Stock Markets: Awavelet Transform-Based Approach. PhD thesis, Universiti Sains Malaysia.

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    Abstract

    Dalam tahun kebelakangan ini, harga pasaran saham adalah salah satu daripada petunjuk ekonomi yang paling penting yang mendedahkan status ekonomi sesuatu negara serta menerokai hubungan kalangan negara-negara di dunia. Seperti yang sedia maklum, harga pasaran saham adalah tidak menentu dan mengandungi data hingar yang memberi kesan kepada ketepatan dan kesahihan keputusan sesuatu model. Oleh itu, para penyelidik semasa memberi tumpuan kepada memeriksa kaedah penguraian untuk menyelesaikan masalah data hingar dan menentukan kemeruapan pasaran saham dengan lebih tepat. Terkini, penurasan wavelet telah digunakan sebagai alat yang berkesan untuk mengurangkan hingar dalam siri masa kewangan. Selain itu, penurasan wavelet mempunyai beberapa ciri-ciri yang lebih berbanding penuras yang lain. Maka dari sudut ini, tesis ini mencadangkan teknik yang berbeza untuk menyiasat hubungan antara pasaran saham dengan menggabungkan penurasan wavelet dan model tradisional dalam usaha menyelesaikan masalah kesan hingar dalam data siri masa kewangan, dan mendapatkan keputusan lebih tepat. Stock market index has recently become one of the most important economic indicators that reveals the economic status of a country and explores the causal relationship among countries. Stock market indices are typically chaotic and contain noise data, which affect the accuracy and validity of the results of some models. Therefore, this study focuses on decomposition methods to solve the problem on noisy data and to determine stock market volatilities accurately. Recently, wavelet filtering has been applied as an efficient tool for reducing noise in financial time series. Wavelet filtering exhibits several properties that are not found in other filters. Thus, this thesis proposes different techniques to investigate causal relationships among stock markets by combining wavelet filtering and traditional models to solve the noise problem in financial time series data and therefor to obtain accurate results.

    Item Type: Thesis (PhD)
    Subjects: Q Science > QA Mathematics > QA1 Mathematics (General)
    Divisions: Pusat Pengajian Sains Matematik (School of Mathematical Sciences)
    Depositing User: Mr Noorazilan Noordin
    Date Deposited: 16 Feb 2017 09:42
    Last Modified: 16 Feb 2017 09:42
    URI: http://eprints.usm.my/id/eprint/32102

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