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研究生:黃美玉
研究生(外文):MAYA MALINDA
論文名稱:外溢效果、結構改變和預測的研究:以消費性 ETF為例
論文名稱(外文):The Study of Spillover Effect, Structural Breaks and Forecasting: Evidence from Consumer Exchange-Trade Funds
指導教授:陳若暉陳若暉引用關係
指導教授(外文):Jo-Hui Chen
學位類別:博士
校院名稱:中原大學
系所名稱:商學博士學位學程
學門:商業及管理學門
學類:一般商業學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:203
中文關鍵詞:消費者ETF預測槓桿效果外溢效果波動性
外文關鍵詞:ForecastingVolatilityConsumer Exchange-Traded Funds (ETFs)the long memorySpillover and Leverage Effects
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本研究的目的為增添有關於外溢效果、長期記憶、波動性和預測消費者指數型基金(ETF)相關研究不足處,分為三部分分析。
第一部分側重於消費者指數型基金(非必需性消費和必需性消費)和生產者相關指數型基金的外溢和槓桿效應。本研究採用GARCH-M-ARMA模型,結果發現消費者ETF和生產者相關ETF分別與其追蹤股票指數間存有雙向影響關係。本文利用EGARCH-M-ARIMA 模型分析,結果顯示生產相關ETF之波動外溢效果較消費ETF效果為低。此外,消費者和生產者相關ETF均存在顯著之負面槓桿效應。
第二部分預測消費者ETF,並以國別區分,如美國、美國境外國(EX-US)、新興市場國家、巴西、中國和印度。根據灰色關聯分析(GRA)的分析結果顯示,前四名影響消費者ETF的因素,計有紐約證券交易所綜合指數、商品研究局商品指數、歐元兌美元之匯率、和賣權/買權比率。在涵蓋所有的資料和變數分析,並導入人工神經網絡(ANN)模式,結果顯示倒傳遞感知模型(BPN)可以更有效地進行預測。然而,根據不同的樣本,研究發現延時反饋神經網絡(TDRNN)和徑向基函數神經網絡(RBF神經網絡)均可提供一致的結果。與其他國家相比,透過人工神經網絡模型亦發現,巴西和中國之消費者ETF較容易預測。

在第三部分採用ARFIMA模型,發現與傳媒體、消費服務、食品及消費品產業相關之消費者ETF報酬較容易預測。另外,ARFIMA-FIGARCH模型顯示,長期記憶波動性只存在於遊戲和消費品行業。此外,透過迭代累加平方測試模型(ICSS),本研究發現消費者ETF出現存有多重結構性改變之不對稱的效果。
本研究之結果將不僅為發行人、投資者和基金經理提供相關經濟意涵,以利其規劃交易策略,實同時證結果和為學者和研究人員提供與消費者ETF相關之新趨勢看法。
The aim of this research is to close the gap in the literature of the spillover, the long memory, volatility and forecasting for consumer exchange-traded funds (ETFs). This research is divided in to three parts.
The first part focuses on spillover and leverage effects of Consumer ETFs (Consumer Discretionary and Consumer Staples) and Producer Related ETFs. This study used Generalized Autoregressive Conditional Heteroskedasticity-in-Mean Autoregressive Moving Average (GARCH-M-ARMA) and found a bilateral correlation between Consumer ETFs and Producer Related ETFs tracing fundamental indexes. With Exponentially Generalized Autoregressive Conditional Heteroskedasticity-in-Mean Autoregressive Moving Average (EGARCH-M-ARMA) models, this paper found that Producer Related ETFs have less spillover effects for compared with Consumer ETFs. There were strongly negative leverage effects of both consumer and Producer Related ETFs.
The second part forecasts consumer exchange-traded funds (ETFs) which classified by country, such as the United States (US), excluding the United States (EX-US), Emerging Markets, Brazil, China, and India. The findings of Grey Relational Analysis (GRA) showed that there are top four ranking to influence Consumer ETFs, such as New York Stock Exchange Composite Index, Commodity Research Bureau, exchange USD/EUR and Put/call ratio. Artificial Neural Network (ANN) approach connected with all data and variables revealed that Back-Propagation Perception (BPN) can be much more effective for prediction. However, based on different sample this paper found that Time-Delay Recurrent Neural Network (TDRNN) and Radial Basis Function Neural Network (RBFNN) provide consistent results. ANN model also found that Brazil and China Consumer ETFs can be easier to predict, comparing with others countries.
The methods used in the third part is known as Autoregressive Fractionally Integrated Moving Average which found that media, consumer service, food and beverage and consumer goods industries of Consumer ETFs returns can be a good prediction. Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (ARFIMA-FIGARCH) model revealed that the long memory in volatility existed only for gaming and consumer goods industries. Moreover, there are multiple structural breaks for asymmetrical effects in Consumer ETFs by applying the Iterated Cumulative Sums Squares Test (ICSS).
The outcome of this research will not only offer economic meaning for issuers, investors and fund managers to plan for their trading strategies, but also provide for academicians and researchers stepping stones in having empirical results and a new perceptive from Consumer ETFs.
Table of Contents
中文摘要…………………………………………………………………………………..…….i
Abstract iii
Acknowledgements…………………………………………………………………………….v
Table of Contents viii
List of Tables xi
List of Figures xii
I. Preface 1
1.1 Research Background 1
1.2 Research Purposes 14
References for Preface: 17

II. Essay One
Spillover and Leverage Effects of Consumer Exchange-Traded Funds (ETFs) 19
Abstract of Essay One 19
2.1 Introduction 20
2.2 Related Literature 26
2.2.1 Spillover and Leverage Effects 26
2.2.2 Asymmetric Volatility 27
2.3 Data and Methodology 29
2.3.1 Data 29
2.3.2 Methodology 31
2.4 Empirical Results 37
2.5 Conclusion 74
References for Essay One: 76

III. Essay Two
The Forecasting of Consumer Exchange-Trade Funds (ETFs): The Application of Grey Relational Analysis (GRA) and Artificial Neural Network (ANN) 81
Abstract of Essay Two 81
3.1 Introduction 82
3.2 Related Literature 87
3.3 Data and Methodology 91
3.3.1 Grey Relational Analysis (GRA) 100
3.3.2 Artificial Neural Network (ANN) for Consumer ETFs 103
3.3.2.1 Back-propagation Network (BPN) 104
3.3.2.2 The Recurrent Neural Network (RNN) 106
3.3.2.3 The Radial Basis Function Neural Network (RBFNN) 109
3.3.2.4 The Time-delay Recurrent Neural Network (TDRNN) 110
3.3.2.5 Model Evaluation Method 114
3.4 Empirical Results 116
3.5 Conclusion 133
References for Essay Two 135

IV. Essay Three
Testing for The Long Memory and Multiple Structural Breaks in Consumer ETFs 146
Abstract of Essay Three 146
4.1 Introduction 147
4.2 Related Literature 153
4.3 Data and Methodology 156
4.4 Empirical Results 163
4.5 Conclusion 163
References for Essay Three 176
V. General contribution ……………………………………………………………………..183
Limitations and Future Research………………………………………................................185
References for General Contribution and Limitations and Future Research …………........186

List of Tables
Preface

Table 1.1. Consumer Discretionary Equities ETFs.………………………………………….....5
Table 1.2. Consumer Staples Equities ETFs…………………………………………………....8
Table 1.3. The Summary of Producer Related ETFs…………………………………………..10

Essay One

Table 2.1. Comparison Among Exchange-Traded Fund and Mutual Fund………...…………21
Table 2.2. Spillover and Leverage Effect and Volatility Literatures.………………………....28
Table 2.3. The Summary of Consumer ETFs..………………………………………………...30
Table 2.4. The Summary of Producer Related ETFs…………………………………………..31
Table 2.5. The Sample Size and Period of Consumer ETFs and Stock Indexes……………....45
Table 2.6. The Sample Size and Period of Producer Related ETFs and Stock Indexes…….....47
Table 2.7. Summary of Unit Root of Consumer ETFs and Stock Indexes…………………….48
Table 2.8. Summary of Unit Root of Producer Related ETFs and Stock Indexes……………..50
Table 2.9. Spillover Effects of Return and Volatilities for Stock and Consumer ETF Returns…………………………………………………………………………………………51
Table 2.10. Spillover Effects of Return and Volatilities for Stock and Producer ETF Returns……………………………………………………………………………………...….52
Table 2.11a. GARCH-M-ARMA of Consumer Discretionary and Consumer Staples
ETFs Return ………….…………………………………………………………………….….55
Table 2.11b. GARCH-M-ARMA of Consumer Discretionary and Consumer Staples
ETFs Return ………….…………………………………………………………………….….56
Table 2.12. GARCH-M-ARMA of Producer Related ETFs Return…………………………..57
Table 2.13a. GARCH-M-ARMA of Consumer Discretionary and Consumer Staples ETFs
and Stock Indexes ………………………………………………………………………….….61
Table 2.13b. GARCH-M-ARMA of Consumer Discretionary and Consumer Staples ETFs
and Stock Indexes ………………………………………………………………………….….62
Table 2.14. GARCH-M-ARMA of Producer Related ETFs and Stock Indexes……………....63
Table 2.15a. EGARCH-M-ARMA of Consumer Discretionary and Consumer Staples ETFs
and Stock Indexes……………………………………………………………………….……..66
Table 2.15b. EGARCH-M-ARMA of Consumer Discretionary and Consumer Staples ETFs
and Stock Indexes……………………………………………………………………….……..67
Table 2.16. EGARCH-M-ARMA of Producer Related ETFs Return ………………………...68
Table 2.17a. EGARCH-M-ARMA of Consumer Discretionary and Consumer Staples ETFs
and Stock Indexes ………………………………………………………………………….….71
Table 2.17b. EGARCH-M-ARMA of Consumer Discretionary and Consumer Staples ETFs
and Stock Indexes ……………………………………………………………………….…….72
Table 2.18. EGARCH-M-ARMA of Producer Related ETFs and Stock Indexes…………….73

Essay Two
Table 3.1. Grey Relational Analysis and Artificial Neural Network on the
Forecasting Financial Instrument Literatures …….………………………………...…….…...91
Table 3.2. Summaries of ETFs Utilized for the Forecasting by ANN…………………...……92
Table 3.3. The Sources of Macroeconomic and Financial Variables……………………….....92
Table 3.4. Consumer ETFs and GRGs of Eight Determinants ………………………...…….118
Table 3.5. Testing the GRA Results for The Neural Network for Consumer ETFs Using
ANN ………………………………………………………..………………………………...120
Table 3.6. Testing the GRG results of ETFs for ANN Prediction Using BPN……………….122
Table 3.7. Testing the GRG results of ETFs for ANN Prediction Using RNN………………124
Table 3.8. Testing the GRG Results of ETFs for ANN Prediction Using RBFNN…...….…126
Table 3.9.Testing the GRG Results of ETFs for ANN Prediction Using TDRNN.…………127
Table 3.10. The Comparison of Forecasting Ability f Neural Network for Consumer ETFs. 128
Table 3.11. The Comparison of Forecasting Ability of Neural Network for Consumer
ETFs by Country.…………………………………………………………………………….130
Table 3.12. The Comparison of Forecasting Ability of Neural Network for Consumer ETFs
use MSE test………………………………………………………..………………………...132

Essay Three
Table 4.1. The Long Memory and Multiple Structural Breaks Literatures……..……..……..155
Table 4.2. Summarized of ETFs for The long memory and Multiple Structural Breaks ..…..157
Table 4.3. The Descriptive Statistics of Variables…….……………………………………...164
Table 4.4. Summary Statistics of Unit root, ARMA, LM, ARCH-LM and GARCH..………167
Table 4.5. Summary Statistics of ARFIMA and ARFIMA-FIGARCH Models With all
Period.………………………………………………………………………………………...168
Table 4.6. The Result of Multiple Structural Breaks…………………………………………172
Table 4.7. The Effect of Structural Breaks With Whole Period.……………………………..173

List of Figures
Preface
Figure 1.1. The Global Growth in ETF and iShares Asset……………………………………..2
Figure 1.2. Consumer Staples vs Consumer Discretionary Performance.……………...……...4
Figure 1.3. Several Consumers Discretionary ETFs Return.…………………………………..7
Figure 1.4. Several Consumer Staples ETFs Return.…………………………………………..10
Figure 1.5. Producer Related ETFs Return……………………………………………...……..11
Figure 1.6. Research Flow Chart.……………………………………………………………...16

Essay One

Figure 2.1. Essay One Flow Chart.…………………………………………………………….25
Figure 2.2a. Graph Daily Return of Consumer Discretionary ETFs and Stock Indexes.……..38
Figure 2.2b. Graph Daily Return of Consumer Discretionary ETFs and Stock Indexes.……..39
Figure 2.3a. Graph Daily Return of Consumer Staples ETFs and Stock Indexes……………..40
Figure 2.3b. Graph Daily Return of Consumer Staples ETFs and Stock Indexes……………..41
Figure 2.4a. Graph Daily Return of Producer Related ETFs and Stock Indexes………….......43
Figure 2.4b. Graph Daily Return of Producer Related ETFs and Stock Indexes……………...44

Essay Two
Figure 3.1. Essay Two Flow Chart.…………...…………………………………………….....86
Figure 3.2. Connection GLD/Put Call Ratios……………...………………………………… 93
Figure 3.3. Dollar Index and the United States Equity Return Correlation in 2009………......94
Figure 3.4. Volatility Index of S&;P 500…………………………..…………………………..95
Figure 3.5. Correlation Between CRB and S&;P 500 index……………………………..…….96
Figure 3.6. TRIN Index for New York Stock Exchange (NYSE)………...…………………..98
Figure 3.7. Inflation Rate of the United States 2008-2014.…..……………………………….99
Figure 3.8. The Cross-Correlation Statistic Between the Interest Rate and the Commodity Price Return.…………………………………………………………………...…………………...100
Figure 3.9. Artificial Neural Network Structure.…………………………………………….104
Figure 3.10. Back Propagation Network Architecture……………………………………….104
Figure 3.11. Recurrent Neural Network Architecture.……………………………………….107
Figure 3.12. Radial Basis Function Neural Network Architecture…………………………..109
Figure 3.13. The Architecture of Time-Delay Recurrent Neural Network…………………..111

Essay Three
Figure 4.1. Essay Three Flow Chart.…………………………………………………………152
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