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.
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Decomposition methods to solve the problem on noisy data and , to determine stock market volatilities accurately.
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