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| 系統識別號 |
U0002-3101201912535900 |
| 中文論文名稱
|
美國貨幣政策對國際金融市場的外溢效果：來自於中國ETF市場的證據 |
| 英文論文名稱
|
Spillover Effects of US Monetary Policy on International Financial Markets: Evidence from China's ETF Market |
| 校院名稱 |
淡江大學 |
| 系所名稱(中) |
經濟學系經濟與財務碩士班 |
| 系所名稱(英) |
Master's Program in Economics and Finance, Department of Economics |
| 學年度 |
107 |
| 學期 |
1 |
| 出版年 |
108 |
| 研究生中文姓名 |
卓忻斕 |
| 研究生英文姓名 |
Hsin-Lan Cho |
| 學號 |
606570074 |
| 學位類別 |
碩士 |
| 語文別 |
英文 |
| 口試日期 |
2019-01-10 |
| 論文頁數 |
55頁 |
| 口試委員 |
指導教授-鄭東光
指導教授-黃健銘
委員-陳柏儒
委員-洪瑞成
|
| 中文關鍵字 |
貨幣政策
ETF市場
馬可夫轉換模型
外溢效果
|
| 英文關鍵字 |
Monetary policy
ETFs market
Markov-switching model
spillover effects
|
| 學科別分類 |
|
| 中文摘要 |
本文以中國ETF市場為證，採用馬可夫轉換模型（Markov-switching model）進行實證，觀察在國際間不同的貨幣政策下，資金流通快速的移動，是否會增加中國ETF市場風險結構，並同時劃分為緊縮與寬鬆兩個子樣本進行觀察，捕捉真實的報酬與風險的行徑。最後，本研究也加入油市來分析外溢效果。
本文重點關注與滬深300指數相關的ETF，並取其收盤價與成交量的資料變數定義代表債市。中國與台灣的股市分別以滬深300指數和台灣加權指數作為代表，而油市以西德州原油作為代表，並用聯邦基準利率、一個月期國庫券固定期限利率和十年期公債固定期限利率來計算利差。資料使用期間為2012年7月18日至2018年9月28日。
本研究發現了油市對股市的外溢效果，研究結果顯示油價與FFR呈負向關係。因此可以預期當油價下跌時，FFR開始上升，則可進一步推知利差上升，而利差的增加使違約風險上升，由於市場處於貨幣緊縮政策下，市場資金逐漸從市場抽離，使流動性風險上升，股價開始下跌。相反的，在貨幣寬鬆政策下，熱錢開始湧入市場，使股票流動性上升，股價開始上升。另外，本研究也發現在緊縮的貨幣政策下對股票市場帶來的影響要大於在寬鬆的貨幣政策下。
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| 英文摘要 |
This paper mainly studies China's economic market that under the different international monetary policies, the rapid movement of capital circulation will increase the risk structure of China's ETF market or not. In addition, this paper uses the Markov-switching model to investigate the spot and ETFs market with the most representative ETF of the CSI 300 Index from Jul. 18, 2012 to Sep. 28, 2018 and is simultaneously divided into two subsamples of tight and expansionary for observation to effectively capture the real returns and the path of risks. Finally, this paper also joined the oil market to analyse the spillover effect.
The results of this paper show that the oil market's volatility spillover effect on the stock market, oil price and FFR are negatively related. So it can be expected that when the oil price falls, FFR starts to rise, it can further infer that the spread spreads, and the increase in spreads makes the default risk As the market is in a monetary tightening policy, market funds are gradually withdrawing from the market, causing liquidity risks to rise, and stock prices begin to fall. In addition, this paper also finds that the impact on the stock market in the state of tight monetary policy is greater than loose monetary policy.
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| 論文目次 |
[CONTENTS]
CHAPTER I Introduction 1
1.1 The Development of the ETFs Market 1
1.2 Monetary Environment and Capital Market 4
1.2.1 The implementation of the US monetary policy 4
1.2.2 The rise of the Chinese market and the bilateral exchange rate 7
1.3 Purpose of this paper 11
1.4 Chart of How to Research 13
CHAPTER II Literature Review 14
2.1 The Effects of Monetary Policies on the Financial Commodities 14
2.2 The Relationship between the ETFs Market and the Stock Market 16
2.3 Market Efficiency and Hedging Benefits 20
2.4 Spillover Effects on Financial Markets 24
2.4.1 Spillover Effects between Stock and Oil Markets 24
2.4.2 Spillover Effects on International Financial Markets 26
CHAPTER III Methodology 29
3.1 Definitions of Variables 29
3.2 Methodology 30
3.2.1 Unit Root Test 30
3.2.1.1 Augmented Dickey-Fuller Test (ADF Test) 30
3.2.1.2 Phillips-Perron Test (PP Test) 32
3.2.2 GARCH Model 32
3.2.3 Markov-switching Model 33
CHAPTER IV Data Source and Treatment 37
4.1 Source and Processing 37
4.2 Basic Statistics 38
CHAPTER V Empirical Results 42
5.1 Empirical Results from the Unit Root Test 42
5.2 Empirical Results from GARCH Model 44
5.3 Empirical Results from the Markov-switching Model 44
CHAPTER VI Conclusions 48
Refrerences 49
[Lists of Figures]
Figure 1 architecture diagram of the research 13
Figure 2 the trend of close and volume in each market 41
Figure 3 the trend of FFR and WTI 41
Figure 4 the smoothing probability of China and Taiwan under the regime of down-market and up-market 47
[Lists of Tables]
Table 1 basic statistic 40
Table 2 empirical results from the unit root test 42
Table 3 empirical results from GARCH model of the oil market 44
Table 4 empirical results from the Markov-switching model 46
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