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博碩士論文 etd-0526117-223859 詳細資訊
Title page for etd-0526117-223859
論文名稱
Title
跨市場股票、石油、黃金、白銀及外匯之報酬相關性網絡分析
Network analysis of Cross markets stocks, Oil, Gold, Silver and Forex
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
84
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2017-06-21
繳交日期
Date of Submission
2017-06-27
關鍵字
Keywords
中心性、跨市場、金融商品分析、社會網絡分析、股票相關性網絡
commodity analysis, cross-market analysis, social network, centrality, return correlation network
統計
Statistics
本論文已被瀏覽 5741 次,被下載 195
The thesis/dissertation has been browsed 5741 times, has been downloaded 195 times.
中文摘要
在本研究中我們建構跨市場中上市股票、商品和外匯之相關性網路。不同以往研究關於市場傳染性傳統的文章,本研究試圖使用社會網絡拓樸學去分析跨市場股票及金融商品的資訊流動。股票相關性網路的變化可以透過eigenvector centrality、degree、betweenness、closeness、modularity及density等網路參數充份表現出來。這些網絡參數提供有趣的經濟意涵包括資訊不對稱、從眾效應以及資訊的流動,它們比傳統的 VAR 分析在市場傳染性的研究中提供更多豐富洞見。例如,我們可以回應的問題有 : 在跨市場中,那支股票或是金融商品在資訊傳遞中是最重要的? 或是在跨產業中,哪個產業在資訊中是最重要的? 在特定事件發生前後,整個相關性網絡是如何變化? 以及更重要的一點,網絡參數與全球市場報酬率的分配之間的關係為何?本研究將建構每日的股票及金融商品相關性網絡,進而去估計與報酬率特性之間的關係。

本研究是首篇將相關性網絡分析應用在跨股票、跨商品市場的研究,本文的目的主要有三方面:

1. 估計每檔股票或金融商品在跨市場訊息傳遞的網絡"重要性"。
2. 透過將資料動態視覺化,觀察在重要的事件發生時,網絡結構的變化,並據以探討發生此變化背後的原因。
3. 實證分析跨市場網絡結構參數與報酬率特性之間的關係。
Abstract
In this study we construct return correlation network for cross market listed stocks, commodities and foreign exchanges. Unlike conventional studies of market contagion, this study attempt to analyze the information flow among stocks using social network topologies. The change in return correlation network can be manifested by network parameters such as eigenvector centrality, degree, betweenness, closeness, modularity and density. These parameters have interesting economic connotation in terms of information asymmetry, herding, and information flow, and they can offer far more rich observation than traditional VAR analysis in market contagion studies. For example, we may be able to answer question such as which stock is the most important stock in information transmission across-markets or across industries, or how the correlation structure changes before and after certain events. More importantly, we can also identify the relationship between network topologies and global markets’ return distribution.

This is the first study to apply network analysis to cross market securities and cross products. The purposes are threefold:

1. To estimate the ‘importance’ of each stock in cross-market information transmission.
2. To observe changes in topologies at important market events using dynamic data visualization, and investigate the underlying causes.
3. To empirically test the relationship between return topologies and return attributes.
目次 Table of Contents
論文審定書...................................................................................................i
摘要..............................................................................................................ii
ABSTRACT...................................................................................................iii
I. INTRODUCTION...................................................................................1
1.1 BACKGROUND INFORMATION...........................................................1
1.2 RESEARCH PURPOSE........................................................................4
1.3 RESEARCH STRUCTURE....................................................................7
1.4 RESEARCH CONTRIBUTION...............................................................9
II. LITERATURE REVIEW.........................................................................10
2.1 SOCIAL NETWORK IN FINANCIAL FIELD..........................................10
2.2 CROSS MARKETS RESEARCH..........................................................12
III. METHODOLOGY...................................................................................17
3.1 DATA DESCRIPTION............................................................................17
3.2 RETURN CORRELATION NETWORK.................................................19
3.3 PANEL REGRESSION..........................................................................21
3.4 CROSS MARKETS NETWORK VISUALIZATION................................25
3.5 Z-SCORE FOR DEGREE.....................................................................26
IV. EMPIRICAL RESULTS.........................................................................28
4.1 DESCRIPTIVE STATISTICS............................................................... .28
4.2 NETWORK TOPOLOGY AND RETURN ATTRIBUTIONS....................51
4.3 Z-SCORE ANALYSIS.............................................................................60
4.4 MODULARITY VISUALIZATION............................................................62
V. CONCLUSION........................................................................................64
5.1 CONCLUSION........................................................................................64
5.2 SUGGESTIONS FOR FUTURE RESEARCH.........................................66
REFERENCES................................................................................................67
APPENDIX......................................................................................................69
參考文獻 References
Baskaran, T., Blöchl, F., Brück, T., & Theis, F. J. (2011). The Heckscher-Ohlin model and the network structure of international trade. International Review of Economics & Finance, 20(2), 135-145.
Colla, P., & Mele, A. (2010). Information linkages and correlated trading. Review of Financial Studies, 23(1), 203-246.
Diebold, F. X., & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets*. The Economic Journal, 119(534), 158-171.
Diebold, F. X., & Yilmaz, K. (2010).Equity Market Spillovers in the Americas, In: Alfaro, R. (ed.), Financial Stability. Monetary Policy, and Central Banking, Bank of Chile Central Banking Series, Vol 15, 199-214.
Dimitrios, K., & Vasileios, O. (2015). A Network Analysis of the Greek Stock Market. Procedia Economics and Finance, 33, 340-349.
Fagiolo, G., Reyes, J., & Schiavo, S. (2009). Dynamics and Evolution of the International Trade Network. In S. Fortunato, G. Mangioni, R. Menezes, & V. Nicosia (Eds.), Complex Networks: Results of the 2009 International Workshop on Complex Networks (CompleNet 2009) (pp. 1-13). Berlin, Heidelberg: Springer Berlin Heidelberg.
Garlaschelli, D., & Loffredo, M. I. (2004). Fitness-Dependent Topological Properties of the World Trade Web. Physical Review Letters, 93(18), 188701.
Grubel, H. G. (1968). Internationally Diversified Portfolios: Welfare Gains and Capital Flows. The American Economic Review, 58(5), 1299-1314.
Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American economic review, 70(3), 393-408.
Hamilton, J. D. (1983). Oil and the Macroeconomy since World War II. Journal of Political Economy, 91(2), 228-248.
Hedi Arouri, M. E., & Khuong Nguyen, D. (2010). Oil prices, stock markets and portfolio investment: Evidence from sector analysis in Europe over the last decade. Energy Policy, 38(8), 4528-4539.
Hellwig, M. F. (1980). On the aggregation of information in competitive markets.Journal of economic theory, 22(3), 477-498.
Heiberger, R. H. (2014). Stock network stability in times of crisis. Physica A: Statistical Mechanics and its Applications, 393, 376-381.
Kuzubaş, T. U., Ömercikoğlu, I., & Saltoğlu, B. (2014). Network centrality measures and systemic risk: An application to the Turkish financial crisis. Physica A: Statistical Mechanics and its Applications, 405, 203-215.
Kyriakopoulos, F., Thurner, S., Puhr, C., & Schmitz, S. (2009). Network and eigenvalue analysis of financial transaction networks. The European Physical Journal B: Condensed Matter and Complex Systems, 71(4), 523-531.
Lessard, D. R. (1974). WORLD, NATIONAL, AND INDUSTRY FACTORS IN EQUITY RETURNS. The Journal of Finance, 29(2), 379-391.
Liu, Y. A., Pan, M.-S., & Shieh, J. C. P. (1998). International transmission of stock price movements: Evidence from the U.S. and five Asian-Pacific markets. Journal of Economics and Finance, 22(1), 59-69.
Nobi, A., Lee, S., Kim, D. H., & Lee, J. W. (2014). Correlation and network topologies in global and local stock indices. Physics Letters A, 378(34), 2482-2489.
Sun, X.-Q., Shen, H.-W., & Cheng, X.-Q. (2014). Trading Network Predicts Stock Price. Scientific Reports, 4, 3711.
Tabak, B. M., Serra, T. R., & Cajueiro, D. O. (2010). Topological properties of stock market networks: The case of Brazil. Physica A: Statistical Mechanics and its Applications, 389(16), 3240-3249. doi:http://dx.doi.org/10.1016/j.physa.2010.04.002
Tse, C. K., Liu, J., & Lau, F. C. M. (2010). A network perspective of the stock market. Journal of Empirical Finance, 17(4), 659-667.
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