Spillovers of Quantitative Easing on Financial Markets of Thailand, Indonesia, and the Philippines

Pastpipatkul, Pathairat; Yamaka, Woraphon; Wiboonpongse, Aree; Sriboonchitta, Songsak

doi:10.1007/978-3-319-25135-6_35

Cited by 14 publications

(2 citation statements)

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“…The reason may be because these developing markets are in newly industrialized countries which have abolished the capital inflow barrier and the foreign exchange restriction. Therefore, stock markets of these countries have been interested in US investors who expected the high return, and thereby leading to higher growth of these markets during the US crisis [39]. Comparing the standard deviations of the three sub-periods, we find that the standard deviations of stock returns are higher during the crisis period.…”

Section: Data and Summary Statisticsmentioning

confidence: 88%

“…This indicates that the US market return positively correlated with index returns in all stock markets during the crisis period (except Thailand). Pastpipatkul et al [39] revealed that during the United States' subprime crisis that occurred in 2008, the Thai stock market had experienced greater swings in capital inflows and thereby boosting the stock market return. Thus, it is reasonable to have a negative correlation between the US and Thai stock markets.…”

Section: The Degree Of Contagion: Static Copulamentioning

confidence: 99%

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Predicting Contagion from the US Financial Crisis to International Stock Markets Using Dynamic Copula with Google Trends

Maneejuk

Yamaka

2019

Mathematics

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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.

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Section: Data and Summary Statisticsmentioning

confidence: 88%

Section: The Degree Of Contagion: Static Copulamentioning

confidence: 99%