18 research outputs found

    Attitudes of Pregnant Women toward the COVID-19 Vaccine

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    Objective: It was carried out to determine the attitudes of pregnant women toward the Covid-19 vaccine and related factors. Methods: The correlational and descriptive study was conducted with 356 pregnant women. In the study, data were collected using an introductory questionnaire, "Attitudes towards Covid-19 Vaccine Scale", "Vaccination Hesitancy in Pandemics Scale", and "Covid-19 Vaccine Literacy Scale". Research data were analyzed with SPSS 25 package program. One-way ANOVA and Student-t test were used to determine the difference between the descriptive characteristics of the pregnant women participating in the study and the total and sub-dimension mean scores of the Attitude Scale towards the Covid-19 Vaccine. Pearson correlation analysis was used to determine the relationship between the Vaccine Hesitancy Scale in Pandemics, the Covid-19 Vaccine Literacy Scale, and the Attitudes Towards Covid-19 Vaccine Scale. Linear regression analysis was used to determine the factors affecting the Attitudes of Pregnants towards the Covid-19 Vaccine. Results: It was concluded that 37.4% of the pregnant women did not have any Covid-19 vaccine, 62.6% had the Covid-19 vaccine before pregnancy, and 22.5% had the vaccine during pregnancy. In pregnant women, those who have hesitations about the vaccine in cases such as working, increase in education level, fear of contracting Covid-19 before birth, having pre-pregnancy Covid-19 vaccine, thinking that pregnant women may have Covid-19 vaccine, getting Covid-19 vaccine during pregnancy, etc. increase their attitudes towards vaccination. Conclusion: Consider to change the conclusion: It was found that quite a few pregnant women received the Covid-19 vaccine during their pregnancy. Pregnant women's vaccination hesitancy influences their attitudes toward Covid-19

    Does ADR Listing Affect the Dynamics of Volatility in Emerging Markets?

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    This paper analyzes the time-series variation in the return volatility of non-US stocks from emerging markets that are cross-listed on US exchanges. Unlike previous studies in the cross-listing literature, return volatility is modeled using conditional heteroscedasticity models. We find that firmsÔÇÖ exposure to risks such as local and global market betas remain unchanged after cross-listing. Moreover, we do not identify notable changes in the dynamics of the volatility of cross-listed stocks after cross-listing except for leverage effects. We further show that the mean level of conditional variance is not affected after cross-listing. Thus, our results provide counter-evidence to the belief that foreign investor participation drives volatility upward

    Does ADR Listing Affect the Dynamics of Volatility in Emerging Markets?

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    This paper analyzes the time-series variation in the return volatility of non-US stocks from emerging markets that are cross-listed on US exchanges. Unlike previous studies in the cross-listing literature, return volatility is modeled using conditional heteroscedasticity models. We find that firmsÔÇÖ exposure to risks such as local and global market betas remain unchanged after cross-listing. Moreover, we do not identify notable changes in the dynamics of the volatility of cross-listed stocks after cross-listing except for leverage effects. We further show that the mean level of conditional variance is not affected after cross-listing. Thus, our results provide counter-evidence to the belief that foreign investor participation drives volatility upward

    Time-Varying Betas Help in Asset Pricing: The Threshold CAPM

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    Although there is a consensus about time variation in market betas, it is not clear how this variation should be captured. Several researchers continue to analyze different versions of the conditional CAPM. However, Ghysels (1998) shows that these conditional CAPM models fail to capture the dynamics of beta risk. In this study, we introduce a new model, threshold CAPM, which outperforms both the conditional and unconditional CAPMs by generating smaller pricing errors. We also show that the beta risk changes through time with the changes in the economic environment and the dynamics of time variation of beta differ across industries. These findings have important implications for asset allocation, portfolio selection, and hedging decisions.
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