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系統識別號 U0002-2706201314462000
中文論文名稱 三篇有關非線性因果關係檢定在金融市場之應用之論文
英文論文名稱 Three Essays Related to the Application of the Nonlinear Causality Tests in the Financial Markets
校院名稱 淡江大學
系所名稱(中) 財務金融學系博士班
系所名稱(英) Department of Banking and Finance
學年度 101
學期 2
出版年 102
研究生中文姓名 歐宏國
研究生英文姓名 Hong-Kou Ou
學號 893490044
學位類別 博士
語文別 英文
口試日期 2013-06-21
論文頁數 72頁
口試委員 指導教授-聶建中
指導教授-陳達新
委員-聶建中
委員-許振明
委員-謝劍平
委員-林景春
委員-何宗武
委員-樓楨祺
委員-謝志柔
中文關鍵字 黃金期貨  因果關係  股價指數期貨  價量關係 
英文關鍵字 gold futures  Granger causality  stock index futures  lead-lag  Mackey-Glass  price-volume  MS-VAR 
學科別分類
中文摘要 過去二十多年已見證越來越多的非線性計量方法被應用在金融市場的實證研究。即使金融理論迄今還沒提供夠充分理由去解釋為何某些非線性實證模型可以使用,但由於非線性模型有時可捕捉到線性模型所無法解釋的現象,因此這些工具應該被研究人員適度的關注。本論文介紹三種最近發展的非線性計量方法,並呈現這些方法在金融市場之研究的應用。特別的是,這些非線性方法有助於分析變數間的動態因果關係。
本論文由三個篇文章所組成,每篇文章介紹一種非線性因果檢定方法之應用。第一篇文章是「美國與日本黃金期貨市場之線性與非線性動態」。這篇文章使用Diks與Panchenko (2006)所提出的無母數因果關係檢定法去探討世界前兩大黃金期貨之間的關聯。研究結果顯示,這兩個市場的報酬具有雙向的因果關係。但是,以FIGARCH模型控制市場波動後,僅發現美國對日本黃金期貨市場存在非線性因果關係。此結果表示,日本對美國的非線性因果關係幾乎純由市場波動所導致,但波動效果僅能解釋部分的反向非線性因果關係。
第二篇文章的題目是「台灣現貨與股價指數期貨之不對稱暨非線性動態關係」。這篇文章先利用Mackey-Glass時間數列模型去建構現貨與期貨的關係,進而執行基於此模型的因果關係檢定法,以便檢視這兩個市場變數的非線性動態關聯。研究結果顯示有雙向因果關係存在,而此非線性因果關幾乎係由市場波動所驅動。特別的是,進一步結果顯示,非線因果關係在股市多頭和股市空頭下呈現非常不對稱之情況。多頭市場下,現貨對期貨具有非線性因果關係,但在空頭市場下卻是期貨對現貨具有非線性因果關係。
第三篇文章的題目則是「台灣股價與交易量間狀態相依性之研究」。採用馬可夫轉換向量自我迴歸模型(Markov Switching Vector Autoregression model)去建構台灣股票市場的價量關係,我發現:2個狀態(高波動與低波動)的MS-VAR模型適用於描繪價量關係。另外,這篇文章分別運用狀態相依的Granger因果檢定法與衝擊反應函數,分析不同狀態下的價量因果關係,結果顯示:交易量變動對股價報酬的影響只發生在股市高波動的情況下,但不論股市處在高波動或低波動狀態,不論波動狀態為何,股價報酬對交易量都有影響。
英文摘要 The past twenty years have witnessed increasing nonlinear econometric methods used in the empirical study of financial markets. Despite financial theory commonly does not provide enough reason to support the use of the nonlinear models yet, the nonlinear tools sometimes can capture the phenomenon which cannot be explained by linear models. Therefore researchers should attach importance to these tools. This dissertation describes three types of recently-developed nonlinear econometric methods and display how these methods are applied to the studies of the financial markets. In particular, they can help researchers explore the dynamic causal relationship between financial variables.
This dissertation consists of three essays. The first essay is "Linear and Nonlinear Dynamics between U.S. and Japanese gold futures market". Using Diks and Panchenko (2006) non-parametric causality test to explore the linkage between the world's top two gold futures markets, the study results show evidence of two-way causality. After controlling for market volatility with FIAGRCH models, only the nonlinear causality from U.S. to Japanese gold futures market is found. This suggests that the nonlinear causal from Japanese to U.S. gold futures market is almost a result of market volatility, but the volatility effect can only explain a part of nonlinear reverse causality.
The second essay is "Asymmetric and Nonlinear Dynamic Relationship between Taiwan Spot and Stock Index Futures ". This article uses the Mackey-Glass (MG) time-series model to construct the relationship between spot and futures, and then performs the based MG causality tests. The study results show existence of the bidirectional nonlinear causality, and that the nonlinear causal almost is driven by market volatility. Further results demonstrate asymmetric nonlinear causality under good and bad news. Under good news, the spot returns nonlinearly causality index futures returns, but under the bad news the case is reversal.
The topic of the third essay is "Regime Dependence between Stock Prices and Trading Volume in Taiwan". This article uses the Markov Switching Vector Autoregression (MS-VAR) model to construct the price-volume relation in the Taiwan stock market. Results show that the two-regime MS-VAR model is suitable to describe the price-volume relationship. Moreover, the regime-dependent Granger causality tests and regime-independent impulse response functions are performed to analyze price-volume relationship under different regimes. Results show that the causality from volume changes to stock returns only exists in the high volatility regime, while stock returns cause volume changes irrespective of regimes.
論文目次 Acknowledgements in Chinese Ⅱ
Abstract in Chinese Ⅶ
Abstract Ⅷ
Chapter
1 Introduction to the Dissertation 1
2 Linear and Nonlinear Dynamics between the U.S. and Japanese Gold futures Markets 4
2.1 Introduction 6
2.2 Methodology 8
2.2.1 Linear Granger causality testing 9
2.2.2 Nonlinear Granger causality testing 10
2.3 Data 12
2.4 Results and Discussion 14
2.4.1 Unit root and Cointegration tests 14
2.4.2 Tests for Linear Granger Causality 17
2.4.3 Tests for Nonlinear Granger Causality 18
2.5 Conclusion 22
3 Asymmetrical and Nonlinear Dynamic Relationship between Spot and Stock Index Futures in Taiwan 24
3.1 Introduction 26
3.2 Methodology 29
3.2.1 Gregory-Hansen (GH) cointegration test 30
3.2.2 Tests for the nonlinear Granger causality 31
3.3 Data 32
3.4 Results and Discussion 32
3.5 Conclusion 40
4 The dynamics between stock prices and trading volume in Taiwan:
Evidence of regime dependence 42
4.1 Introduction 44
4.2 Literature Review 46
4.2.1 The theory 46
4.2.2 Empirical evidence 48
4.3 Methodology 50
4. 3.1 The Markov switching vector autoregression model 50
4. 3.2 Regime-independent and regime-dependent Granger causality tests 51
4. 3.3 Regime-dependent impulse response functions 52
4.4 The data 53
4.5 Empirical results and Discussion 53
4.6 Conclusion 61
5 Summary and Conclusion 63
Appendix 65
Reference 66

List of Figures
Figure 4.1 regime-dependent response of return to a shock in trading volume 62
Figure 4.2 regime-dependent response of trading volume to a shock in stock returns 63

List of Tables
Table 2.1 Preliminary statistics for daily returns of gold futures markets 13
Table 2.2 Unit Root Tests for daily gold futures prices 15
Table 2.3 Cointegration tests 17
Table 2.4 Results of Linear Granger causality test 18
Table 2.5 Results of Nonlinear Granger causality test 19
Table 2.6 Estimated FIGARCH Models 21
Table 2.7 Result of nonlinear causality with FIGARCH-filtered residuals 23
Table 3.1 Results of Unit Root Tests 34
Table 3.2 Results of the Gregory-Hansen cointegration tests 35
Table 3.3 Results of BDS test for spot and index futures return residuals 37
Table 3.4 Test for nonlinear causality 38
Table 3.5 Test for nonlinear causality under good and bad news 41
Table 4.1 Unit root test results 56
Table 4.2 Test for regime switching 57
Table 4.3 Estimated results of the MS-VAR model 60
Table 4.4 Regime-independent and regime-dependent Granger causality tests 61
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