The Causal Relationship Between Stock Markets: Awavelet Transform-Based Approach
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Date
2016-08
Authors
Ahmed Dghais, Amel Abdoullah
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Abstract
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. This thesis is divided into three
parts. The first part introduces the difference between five functions based on two types
of wavelet, namely, discrete wavelet transform (DWT) and maximal overlap discrete
wavelet transform (MODWT). The differences between DWT and MODWT is are discussed.
Results reveal that differences between functions in their way reducing noise in
series, that DWT is better than MODWT. The second part proposed a new technique by
combining DWT and the vector error correction model (VECM) to investigate causal
relationships between the stock markets of developed countries (such as the US and
the UK) and the selected stock markets of the Middle East and North Africa (MENA)
region. This proposed technique is compared with the traditional VECM, the finding
shows that the former is a useful tool to modeling the causal relationship among the
stock market ,and fit as shown when applied to the financial stock market series in this
thesis. The final part combines DWT and Markov-switching intercept heteroskedastic-
VECM (MSIH-VECM), which is a nonlinear model, to investigate causal relationships
between the stock markets of developed nations (US and UK) and selected stock markets
of the MENA region. The proposed technique is compared with the traditional
MSIH-VECM. From the analysis, it shows that the former is better in terms of performance
and fit better with the financial stock market series in this thesis than the latter.
The proposed technique enables both models to become flexible and prevents the high
noise in stock market series. This technique is proven to be a useful tool in terms of
performance and fit with the financial stock market series in the linear and nonlinear
models. It also provides valuable information on the relationships among the studied
stock markets compared with traditional models. The proposed technique is a main
contribution to literature of studying causal relationship among stock market.
Description
Keywords
Decomposition methods to solve the problem on noisy data and , to determine stock market volatilities accurately.