| 研究生: |
黃鈺辰 Huang, Yu-Chen |
|---|---|
| 論文名稱: |
主要加密貨幣市場之價格互動與波動傳導分析 Price Interaction and Volatility Spillovers in Major Cryptocurrency Markets |
| 指導教授: |
王澤世
Wang, Tse-Shih |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 財務金融研究所 Graduate Institute of Finance |
| 論文出版年: | 2025 |
| 畢業學年度: | 114 |
| 語文別: | 中文 |
| 論文頁數: | 52 |
| 中文關鍵詞: | 加密貨幣 、價格互動 、向量誤差修正模型(VECM) 、共整合關係 、波動傳導 、衝擊反應分析 、Granger 因果關係 、多資產比較 |
| 外文關鍵詞: | Cryptocurrency, VECM, Cointegration, Volatility Spillovers, Price Dynamics |
| 相關次數: | 點閱:2 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
隨著加密貨幣逐漸制度化並成為全球金融市場的重要資產,探討其價格聯動與波動傳導機制,有助於理解其獨特的市場結構與資產功能定位。本研究運用向量誤差修正模型(Vector Error Correction Model, VECM),針對主要加密貨幣市場之價格關係與波動機制進行實證分析,研究期間涵蓋 2020 年 8 月至 2025 年 5 月。模型納入比特幣(BTC)、以太幣(ETH)、幣安幣(BNB)、Solana(SOL)與瑞波幣(XRP)等五種具代表性的主流加密資產作為內生變數,分析期間之長期均衡關係、短期動態調整特性與衝擊反應效果,並進一步探討市場主從結構與資訊傳導方向之不對稱性。
為控制總體經濟與傳統金融市場變數可能帶來的干擾與聯動效果,模型另納入標普 500 指數報酬率作為外生變數,輔助分析宏觀因子對加密資產市場動態之潛在影響力。研究方法包括 Johansen 共整合檢定、VECM 模型估計、Granger 因果檢定、衝擊反應分析與預測誤差變異數分解,以多層次剖析加密資產市場的價格調整機制與波動傳遞路徑。
實證結果顯示,五種加密資產之間存在長期均衡關係,價格走勢呈現共同趨勢。在此架構下,加密市場呈現明確的層級結構:比特幣為核心主導者,具有高度獨立性並對其他資產具有領導效果,而以太幣則為次級領導,具備一定的帶動力,但本身亦容易受到外部衝擊影響,甚至在部分情境下與比特幣呈現替代性的互動。Solana居中,兼具傳導與被動反應的特性;相較之下,幣安幣與瑞波幣主要展現「被動跟隨」的市場行為。
另外,本研究也顯示最關鍵的一點:儘管加密資產間存在上述傳導與層級結構,其價格波動的主要來源仍以各自的內生因素為主,包括基本面發展、技術升級、政策或監管消息、網路活動與市場情緒等。換言之,跨資產的影響雖然重要,但無法取代各項幣種自身特性所帶來的主導作用。整體而言,加密貨幣市場已逐漸展現出類似傳統金融體系的內部秩序與功能分工,其中比特幣與以太幣的市場定位愈趨類似傳統市場中的基準資產,既能代表市場整體動向,又保有其獨特性與領先角色,顯示整體市場的制度化與整合程度正持續提升。
This study examines the long-term equilibrium relationships and short-run dynamics among five major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Solana (SOL), and Ripple (XRP)—over the period from August 2020 to May 2025. Using a Vector Error Correction Model (VECM), the analysis identifies how these assets co-move, transmit shocks, and form a hierarchical structure within an increasingly institutionalized digital asset ecosystem. The S&P 500 Index return is incorporated as an exogenous variable to capture potential macro-financial influences from traditional markets. Empirical results reveal the presence of stable cointegration relationships, indicating that the five cryptocurrencies are tied together by a long-run equilibrium path. Within this structure, BTC emerges as the dominant core asset with strong independence and leading influence, followed by ETH as a secondary leader that both transmits and receives market information. SOL plays an intermediate role, while BNB and XRP primarily act as passive followers. Despite these cross-asset linkages, the major drivers of volatility remain asset-specific fundamentals, technological developments, regulatory news, and network activity. Overall, the findings suggest that the cryptocurrency market is developing into a system with clearer internal order, with BTC and ETH increasingly resembling benchmark assets in traditional finance.
Antonakakis, N., Gabauer, D., & Gupta, R. (2023). Dynamic connectedness of cryptocurrencies to macroeconomic and financial variables. *International Review of Financial Analysis, 85*, 102434.
Barros, F., da Cruz, J., & Silva, F. (2021). Dynamic spillovers in the cryptocurrency market. *Journal of International Financial Markets, Institutions and Money, 75*, 101449.
Baur, D. G., & Dimpfl, T. (2021). The Bitcoin–interest rate nexus. *Applied Economics, 53*(57), 6653–6668.
Bouri, E., & Gupta, R. (2021). Predicting Bitcoin returns: Comparing the roles of newspaper- and internet search-based measures of uncertainty. *North American Journal of Economics and Finance, 56*, 101390.
Ciaian, P., Rajcaniova, M., & Kancs, D. (2018). Virtual relationships: Short- and long-run evidence from Bitcoin and altcoin markets. *Journal of International Financial Markets, Institutions and Money, 52*, 173–195.
Corbet, S., Hou, Y., Hu, Y., Lucey, B., & Oxley, L. (2020). The role of cryptocurrencies in the global financial system: A quantitative analysis. *The European Journal of Finance, 26*(1), 32–47.
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*(366), 427–431.
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. *Econometrica, 55*(2), 251–276.
Gkillas, K., Vougas, D., & Kyriazis, N. (2020). Asymmetric spillovers in cryptocurrency markets. *Finance Research Letters, 36*, 101297.
Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. *Econometrica, 37*(3), 424–438.
Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. *Journal of Econometrics, 2*(2), 111–120.
Ji, Q., Zhang, D., & Roubaud, D. (2019). Measuring the spillovers across Bitcoin and other cryptocurrencies: Evidence from the time and frequency domains. *Energy Economics, 84*, 104523.
Johansen, S. (1988). Statistical analysis of cointegration vectors. *Journal of Economic Dynamics and Control, 12*(2–3), 231–254.
Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. *Econometrica, 59*(6), 1551–1580.
Karau, S. (2023). Monetary policy and Bitcoin. *Journal of International Money and Finance, 137*, 102880.
Kou, G., Lu, H., Wu, Y., Xu, S., & Ma, X. (2022). Dynamic relationship between cryptocurrencies: Evidence from a time-varying connectedness approach. *Technological Forecasting and Social Change, 174*, 121287.
Mangialardi, G., Gigliarano, C., & Marchesi, M. (2021). The long-run and short-run dynamics of the cryptocurrency market: A multifractal analysis. *Chaos, Solitons & Fractals, 146*, 110826.
Nelson, C. R., & Plosser, C. I. (1982). Trends and random walks in macroeconomic time series. *Journal of Monetary Economics, 10*(2), 139–162.
Phillip, A., Chan, J., & Peiris, S. (2019). A new look at cryptocurrencies. *Economics Letters, 174*, 118–122.
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. *Biometrika, 75*(2), 335–346.
Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. *Biometrika, 71*(3), 599–607.
Sims, C. A. (1980). Macroeconomics and reality. *Econometrica, 48*(1), 1–48.
Symitsi, E., & Zopounidis, C. (2021). Spillovers and systemic risk in the cryptocurrency market. *International Review of Financial Analysis, 74*, 101662.
Tiwari, A. K., Shahbaz, M., & Hasim, P. (2020). Asymmetric spillovers between crude oil prices and cryptocurrency returns. *Energy Economics, 88*, 104772.
Yaya, A., Tweneboah, D., & Ofori, G. E. (2020). Long-term comovement among major cryptocurrencies. *Physica A: Statistical Mechanics and its Applications, 547*, 123847.
Zhao, Y., Zhang, M., Pei, Z., & Nan, J. (2023). The effects of quantitative easing on Bitcoin prices. *Finance Research Letters, 57*, 104232.