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題名：COVID-19大流行對股市報酬風險影響研究―以美國、中國大陸和臺灣為例
作者：林芫鋐
作者(外文)：LIN, YUAN-HUNG
校院名稱：逢甲大學
系所名稱：金融博士學位學程
指導教授：張倉耀
楊永列
學位類別：博士
出版日期：2023
主題關鍵詞：股市報酬率；COVID19感染人數；貝氏分量中分量方法；stock return；the cumulative number of COVID-19 infections；Bayesian Quantile on quantile with GARCH Approach
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本文採用貝氏分量中分量方法 (Bayesian Quantile on Quantile with GARCH Approach)提供了中國、臺灣和美國不同市場下與股票報酬率和 COVID-19累積感染人數之間探討2020年至2022年日資料。本文兩大貢獻，首先，中國的部分在低分量的時候影響比較大，而臺灣的分量模型在0.6之後的分量兩變數之間才開始產生影響，相比中國大陸和臺灣，美國的分量模型波動最劇烈，且不論高分量還是低分量，COVID-19累積感 染人數對股市風險報酬率的影響程度都很劇烈。其次，COVID-19累積感染人數對股市衝擊時間以美國時間最長，中國和臺灣平均時間是相同；這三國家地區中美國最早開放市場，因此染疫人數是最多，因此，美國股市受影響衝擊是最大也最長，相對中國和臺灣使得 COVID-19累積感染人數相對為低。
關鍵字：股市報酬率、COVID19感染人數、貝氏分量中分量方法
This article uses the Bayesian Quantile on Quantile with GARCH approach to investigate the relationship between stock returns and the cumulative number of COVID-19 infections in different markets in China, Taiwan, and the United States from 2020 to 2022 based on daily data. This article has two main contributions. Firstly, in China, the impact is greater at lower quantiles. In Taiwan, the quantile model only begins to have an impact on the two variables after the 0.6 quantile. Compared to mainland China and Taiwan, the quantile model in the United States has the most drastic fluctuations, and the impact of the cumulative number of COVID-19 infections on stock market risk and returns is significant at both high and low quantiles. Secondly, the impact time of COVID-19 on the stock market is longest in the United States, while the average time in China and Taiwan is the same. The United States was the first to open its market, and the number of infections was the highest, which is why the impact on the US stock market is the largest and longest, while China and Taiwan have relatively lower cumulative numbers of COVID-19 infections.
Keywords: stock return, the cumulative number of COVID-19 infections, Bayesian Quantile on quantile with GARCH Approach
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