臺灣博碩士論文加值系統

虛擬貨幣近幾年快速崛起，由於虛擬貨幣漲跌幅不受限制、無時無刻都在交易、去中心化、無監管機構等特性，使得虛擬貨幣的交易具有較高風險。虛擬貨幣市場的流動性尤其重要，當市場缺乏流動性會導致價格下跌或需要等待較長時間才能完成交易，將更進一步加深虛擬貨幣市場的投資風險。本研究著重在探討虛擬貨幣市場發生重大事件時流動性外溢效果的變化。應用Diebold & Yilmaz (2012) 模型，以市值前五大的虛擬貨幣進行分析。實證結果顯示，當虛擬貨幣市場發生重大事件時，市場的總流動性外溢效果明顯提高，表示在危機時期，虛擬貨幣的流動性外溢效果會比平穩時期更強。另外，本研究也發現，在市值前五大的虛擬貨幣中，泰達幣(USDT)為流動性外溢效果的淨接收者，乙太幣(ETH)則是流動性外溢效果的淨傳遞者，而比特幣(BTC)、瑞波幣(XRP)以及幣安幣(BNB)這三個虛擬貨幣與其他虛擬貨幣間並不存在明確的流動性傳遞以及流動性外溢效果。

The cryptocurrency market rises rapidly in recent years. Due to the characteristics of unrestricted price limit, trading all day, decentralization, and no institutional supervision, investing in cryptocurrencies is perceived to be a risky investment. Therefore, the liquidity of cryptocurrency market is particularly important. When the illiquidity of cryptocurrency market arises, the transaction cannot be executed immediately at the current market which brings more risk. This study focuses on exploring the changes in liquidity spillover effects when major events occur in the cryptocurrency market. By applying the DY model (Diebold & Yilmaz, 2012) to analyze the big five cryptocurrencies, our empirical results show that when a major event occurs in cryptocurrency market, the total liquidity spillover effect is significantly increase, indicating that in a crisis period, the liquidity spillover effect of cryptocurrency may be stronger than that in a stable period. In addition, the result also show that among the top five cryptocurrencies, USDT is the net receiver of the liquidity spillover effect, ETH is the net transmitter of the liquidity spillover effect. However, there is no clear liquidity transfer and liquidity spillover effect between the other three cryptocurrencies in our sample, namely BTC, XRP and BNB.

摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
一、 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 論文架構 3
二、 文獻探討 5
2.1 名詞解釋 5
2.1.1 虛擬貨幣定義 5
2.1.2 流動性定義 5
2.1.3 外溢效果定義 6
2.2 虛擬貨幣之相關文獻與簡介 6
2.2.1 虛擬貨幣價格 6
2.2.2 虛擬貨幣與其他金融資產之間的關係 7
2.2.3 虛擬貨幣近年大事記 8
2.2.4 虛擬貨幣標的簡介 11
2.3 流動性外溢效果 12
三、 研究方法 13
3.1 樣本資料介紹與研究期間 13
3.2 研究變數定義以及計算方式 14
3.3 衡量流動性外溢效果 16
四、 實驗結果與分析 18
4.1 敘述統計 18
4.2 實證結果分析 19
4.2.1 總流動性外溢效果 19
4.2.2 給別人的外溢效果(to) 20
4.2.3 來自別人的外溢效果(from) 23
4.2.4 淨外溢效果(NET to-from) 26
4.2.5 成對總外溢效果 29
4.2.6 成對淨外溢效果 35
4.2.7 虛擬貨幣流動性外溢效果數值 41
五、 結論與未來展望 43
5.1 結論 43
5.2 未來展望 44
參考文獻 45
中文 45
英文 45

中文
[1]林姿婷. (2010). 頻道收視率與景氣關聯性：VAR模型之應用. 世新大學管理學院財務金融學系碩士學位論文.
[2]陳廷婕. (2016). 亞洲國家間的貿易關係研究：VAR模型分析. 國立高雄大學金融管理學系碩士論文.
[3]張子溥, 吳孟道, & 林建甫. (2014). 台灣證券市場流動性的跨國比較分析. 兩岸金融季刊, 2(2), 45-68.
[4]劉凱騏. (2020). 虛擬貨幣之避險績效-以比特幣(BTC)、乙太幣(ETH)及萊特幣為例. 東吳大學商學院資訊管理學系碩士論文.
英文
[1]Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of financial markets, 5(1), 31-56.
[2]Cespa, G., & Foucault, T. (2014). Illiquidity contagion and liquidity crashes. The Review of Financial Studies, 27(6), 1615-1660.
[3]Chang, Y. T., Gau, Y. F., & Hsu, C. C. (2022). Liquidity spillover in foreign exchange markets. Finance Research Letters, 44, 102105.
[4]Chen, Y., Chiu, J., & Chung, H. (2022). Givers or Receivers? Return and volatility spillovers between Fintech and the Traditional Financial Industry. Finance Research Letters, 46, 102458.
[5]Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28-34.
[6]Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 1057-1072.
[7]Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.
[8]Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of forecasting, 28(1), 57-66.
[9]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.
[10]Elliott, G., Rothenberg, T. J., & Stock, J. H. (1992). Efficient tests for an autoregressive unit root.
[11]Gil-Alana, L. A., Abakah, E. J. A., & Rojo, M. F. R. (2020). Cryptocurrencies and stock market indices. Are they related? Research in International Business and Finance, 51, 101063.
[12]Guégan, D., & Renault, T. (2021). Does investor sentiment on social media provide robust information for Bitcoin returns predictability? Finance Research Letters, 38, 101494.
[13]Handika, R., Soepriyanto, G., & Havidz, S. A. H. (2019). Are cryptocurrencies contagious to Asian financial markets? Research in International Business and Finance, 50, 416-429.
[14]Hasan, M., Naeem, M. A., Arif, M., Shahzad, S. J. H., & Vo, X. V. (2022). Liquidity connectedness in cryptocurrency market. Financial Innovation, 8, 1-25.
[15]Kumah, S. P., & Odei-Mensah, J. (2021). Are Cryptocurrencies and African stock markets integrated? The Quarterly Review of Economics and Finance, 81, 330-341.
[16]Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of econometrics, 54(1-3), 159-178.
[17]Liu, M., Li, G., Li, J., Zhu, X., & Yao, Y. (2021). Forecasting the price of Bitcoin using deep learning. Finance research letters, 40, 101755.
[18]Ng, S., & Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69(6), 1519-1554.
[19]Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
[20]gabauer, D. (2023, February 15). Online Estimation Platform. https://sites.google.com/view/davidgabauer/contact-details?authuser=0