Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/67897
Title: Predicting contagion from the US financial crisis to international stock markets using dynamic copula with google trends
Authors: Paravee Maneejuk
Woraphon Yamaka
Keywords: Mathematics
Issue Date: 1-Nov-2019
Abstract: © 2019 by the authors. The accuracy of contagion prediction has been one of the most widely investigated and challenging problems in economic research. Much effort has been devoted to investigating the key determinant of contagion and enhancing more powerful prediction models. In this study, we aim to improve the prediction of the contagion effect from the US stock market to the international stock markets by utilizing Google Trends as a new leading indicator for predicting contagion. To improve this contagion prediction, the dynamic copula models are used to investigate the structure of dependence between international markets and the US market, before, during, and after the occurrence of the US financial crisis in 2008. We also incorporate the Google Trends data as the exogenous variables in the time-varying copula equation. Thus, the ARMAX process is introduced. To investigate the predictive power of Google Trends, we employ the likelihood ratio test. Our empirical findings support that Google Trends is a significant leading indicator for predicting contagion in seven out of 10 cases: SP-FTSE, SP-TSX, SP-DAX, SP-Nikkei, SP-BVSP, SP-SSEC, and SP-BSESN pairs. Our Google-based models seem to predict particularly well the effect of the US crisis in 2008. In addition, we find that the contribution of Google Trends to contagion prediction varies among the different stock market pairs. This finding leads to our observation that the more volatile the market time-varying correlation, the more useful Google Trends.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85075367016&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/67897
ISSN: 22277390
Appears in Collections:CMUL: Journal Articles

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