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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林建甫(Chien-Fu Lin) | |
| dc.contributor.author | Yu-Xian Zheng | en |
| dc.contributor.author | 鄭宇縣 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:06:07Z | - |
| dc.date.copyright | 2022-07-08 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-07-01 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84195 | - |
| dc.description.abstract | 近年來，環境保護、社會責任與公司治理(Environment, Social, and Governance；ESG) 浪潮逐漸襲捲全球，投資人愈來愈關注ESG 投資對績效的影響，雖關於ESG 的學術研究已有不少，但對於其與傳統資產間的相關性結構和風險所知甚少(Friede, 2019)。 近來關於ESG 投資與傳統投資比較的研究逐漸增加，但研究結果卻有相互矛盾的情形(Cornell, 2021)，沒有一致的結論，也鮮少以ESG 型ETF 作為研究對象。故本文結合「ESG」、「ETF」兩大概念，探討ESG 型ETF 相對於股票市場以及傳統型ETF 的投資風險。 本文考量報酬序列之變異數可能受到前期報酬率之影響而不為常數，以及報酬率可能受到前期報酬率及殘差的影響，配適ARMA-GARCH 之邊際分配模型，再使用Student-t Copula 函數降低估計的參數數量，以及描述殘差不為常態分配的現象。實證結果發現，以標準差來看，選取的傳統型ETF (IJR) 的風險最高，NASDAQ 指數次之，ESG 型ETF (ESGU) 最低，但ESGU 與NASDAQ 指數的相關性卻比IJR 與NASDAQ 指數的相關性還要高，尤其在左尾相關性方面，顯示ESGU 更可能與NASDAQ 指數同時下跌，意即ESG 型ETF 並未有較好的抗跌性。此外，在95% 信心水準的情況下，檢視預測期間內的風險情況，結果顯示ESGU 與NASDAQ 指數在這段期間內皆有4 次穿透預估之風險值，表示在此段期間內風險都較高。 | zh_TW |
| dc.description.abstract | In recent years, the environmental, social, and corporate governance (ESG) wave has gradually swept the world. Investors are paying more and more attention to the impact of ESG investment on performance. Although there has been a lot of research on ESG. However, little is known about its risk and correlation structure with traditional assets (Friede, 2019). Recently, the research on ESG investing compared to traditional investing has gradually increased. But the results of the studies are conflicting and have no consistent conclusions (Cornell, 2021). In addition, ESG ETFs are rarely used as research objects. Therefore, this study combines the concepts of ESG and ETF to discuss investment risks of ESG ETF relative to the stock market and traditional ETF. This study considers that the variance of the return series may be affected by the previous rate of return and the rate of return may be affected by the previous rate of return and residuals. We fit the marginal distribution model of ARMA-GARCH first, then use the Student-t Copula function to reduce the number of parameters and describe that the residuals are not normally distributed. The empirical results show that traditional ETF (IJR) has the highest risk in terms of standard deviation, followed by NASDAQ, and ESG ETF (ESGU) has the lowest risk. But the dependence between ESGU and NASDAQ is higher than that between IJR and NASDAQ, especially in lower tail dependence. It shows that ESGU is more likely to fall at the same time as the NASDAQ, which means that ESG ETF do not have preferred protection capabilities. Besides, under the 95% confidence level, the results show that both ESGU and the NASDAQ have 4 penetrations of the estimated VaR, indicating that the risk is higher during this period. | en |
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| dc.description.tableofcontents | 口試委員審定書 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .i 致謝 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .ii 摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .iii Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .v 目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .vii 圖目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .x 表目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xi 第一章緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 1.1 研究背景與動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 ESG 概述. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 ETF 概述. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2 研究目的. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3 研究流程. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 第二章文獻探討. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9 2.1 關聯結構(Copula) 探討. . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.1 在VaR 上的應用. . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.2 關於金融蔓延的應用. . . . . . . . . . . . . . . . . . . . . . . . 12 2.1.3 關於其他金融資產. . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.4 Vine Copula 的發展. . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2 ESG 相關議題探討. . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.1 ESG 興起的原因. . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.2 關於ESG 的爭論. . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.3 ESG 對財務績效的影響. . . . . . . . . . . . . . . . . . . . . . . 17 2.2.4 ESG 投資與傳統投資在報酬上的比較. . . . . . . . . . . . . . . 18 2.2.5 ESG 投資與傳統投資在風險上的比較. . . . . . . . . . . . . . . 19 2.2.6 ESG 評級(分) 差異的影響. . . . . . . . . . . . . . . . . . . . . 20 2.2.7 ESG 評級變化對報酬的影響. . . . . . . . . . . . . . . . . . . . 21 第三章研究方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22 3.1 時間序列. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2 單根檢定. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3 ARMA-GARCH 模型. . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4 Copula 理論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.5 常用的雙變數Copula . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.5.1 Gaussian Copula . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.5.2 Student’s t Copula . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.5.3 Clayton Copula . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.5.4 Gumbel Copula . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.5.5 Frank Copula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.6 參數估計. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.7 尾部相關性與下行風險之關聯. . . . . . . . . . . . . . . . . . . . . 33 3.8 風險值(Value at Risk) 的估計. . . . . . . . . . . . . . . . . . . . . . 34 第四章實證分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35 4.1 資料來源. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 敘述性統計. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.3 ADF 單根檢定. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.4 邊際分配模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.5 Copula 模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 第五章結論與建議. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .54 5.1 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.2 研究建議. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .57 附錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70 | |
| dc.language.iso | zh-TW | |
| dc.subject | CARCH | zh_TW |
| dc.subject | Copula | zh_TW |
| dc.subject | ARMA | zh_TW |
| dc.subject | 風險值 | zh_TW |
| dc.subject | ESG | zh_TW |
| dc.subject | Value at risk | en |
| dc.subject | ESG | en |
| dc.subject | CARCH | en |
| dc.subject | Copula | en |
| dc.subject | ARMA | en |
| dc.title | ESG 基金之風險分析 | zh_TW |
| dc.title | Risk Analysis on ESG Fund | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳元保(Yen-pao Chen),周冠男(Kuan-Nan Chou),廖仁哲(Jen-Che Liao) | |
| dc.subject.keyword | ESG,CARCH,Copula,ARMA,風險值, | zh_TW |
| dc.subject.keyword | ESG,CARCH,Copula,ARMA,Value at risk, | en |
| dc.relation.page | 72 | |
| dc.identifier.doi | 10.6342/NTU202201095 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-07-03 | |
| dc.contributor.author-college | 社會科學院 | zh_TW |
| dc.contributor.author-dept | 經濟學研究所 | zh_TW |
| dc.date.embargo-lift | 2027-01-06 | - |
| 顯示於系所單位： | 經濟學系 | |
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