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題名 加密貨幣、穩定幣與金融市場長短期互動關係
Long-term and Short-term Interaction among Cryptocurrencies, Stablecoins and Financial Markets作者 張敦翔
Chang, Tun-Hsiang貢獻者 林建秀
Lin, Chien-Hsiu
張敦翔
Chang, Tun-Hsiang關鍵詞 加密貨幣
穩定幣
ARDL
共整合分析
誤差修正模型
Cryptocurrency
Stablecoin
ARDL
Cointegration Analysis
Error Correction Model日期 2023 上傳時間 6-Jul-2023 16:45:21 (UTC+8) 摘要 從加密貨幣問世以來，透過去中心化與不受政府監管的特點博取廣大市場投資者的興趣，在這波浪潮下，關於加密貨幣的研究也如雨後春筍般產出。然而，近年來，儘管加密貨幣與傳統金融市場的低相關性或對沖風險能力逐漸獲得認可，但其極大的波動幅度也常使眾人卻步。為了改善這項缺點，與具備穩定價格的資產掛勾的穩定幣便應運而生，以加密貨幣資金避風港的角色於2014年開始發行。COVID-19爆發之後，隨著美國重啟量化寬鬆政策（Quantitative easing）與接連降息刺激經濟，大量資金流入市場推升加密貨幣價格，紛紛創下歷史新高。但就在2022年3月美國聯準會宣布恢復升息循環與縮表以打擊居高不下的通貨膨脹後，加密貨幣整體市值出現急遽下滑，也讓穩定幣再度浮上檯面。本文有別於過往文獻，將加密貨幣與穩定幣一同放入比較，觀察兩者對於傳統金融市場、商品以及不同變數間的反應。研究方法則首先以單根檢定確認變數序列有無呈現定態（Stationary），再結合ARDL（Autoregressive Distributed Lag Model）模型與Pesaran and Shin (1999)與Pesaran et al. (2001)所提出的 Bound Testing檢定加密貨幣、穩定幣和解釋變數間是否存在長期共整合關係，同時利用誤差修正模型（Error Correction Model）來識別短期互動方向，最後分別探討COVID-19疫情前後彼此間關聯是否發生變化。實證結果發現，加密貨幣不管在長期下與利率、避險資產黃金，抑或是短期內與恐慌指數VIX的互動，都凸顯加密貨幣帶給投資組合的避險效益有限。然而在穩定幣上，除了可以提供交易者在加密貨幣市況不佳時暫避風頭，本文更發現穩定幣短期下能夠在熊市或市場情緒恐慌之際，吸引資金流入，甚至在疫情後與利率的連結產生轉變，被投資者視為避風港。不過，由於加密貨幣與穩定幣屬於新興領域，市場信心度受負面消息影響甚巨，購買者仍須時刻注意。
Since the emergence of cryptocurrency, its decentralized and unregulated features have attracted the interest of many investors, leading to a surge of research on the topic. Despite the low correlation or hedging ability of cryptocurrencies with traditional financial markets, their high volatility has often made investors cautious. To address this issue, stablecoins linked to assets with stable prices were introduced in 2014 to serve as a safe haven for cryptocurrency. After the outbreak of COVID-19, the injection of massive amounts of funds into the market by the US government`s quantitative easing policy and consecutive rate cuts pushed up the prices of cryptocurrencies, setting new historical highs one after another. But in March 2022, the Federal Reserve announced the resumption of interest rate hikes and balance sheet reduction to combat persistent inflation. This caused a sharp decline in the overall market capitalization of cryptocurrencies, and brought stablecoins back into the spotlight.Unlike previous literature, this paper compares both cryptocurrencies and stablecoins, examining their reactions to traditional financial markets, common trading assets, and different variables. The research method first confirms the stationarity of variable sequences using the unit root test, then combining the ARDL (Autoregressive Distributed Lag Model) model with the Bound Testing proposed by Pesaran and Shin (1999) and Pesaran et al. (2001) to investigate the existence of long-term cointegration relationships among cryptocurrencies, stablecoins, and explanatory variables. The Error Correction Model is used to identify short-term interactive directions, and the changes in their correlation before and after the COVID-19 pandemic are separately explored.Empirical results show that cryptocurrencies have limited hedging benefits for investment portfolios in the long run, regardless of their interactions with interest rates, hedge assets such as gold, or the short-term interaction with the VIX index. On the other hand, stablecoins not only provide traders with a safe haven during adverse cryptocurrency market conditions, but also attract capital inflows during bear markets or market panic in the short term. Moreover, the link between stablecoins and interest rates underwent a change after the pandemic, making them more appealing to investors as a safe haven. However, as cryptocurrencies and stablecoins are still emerging fields, their market confidence is highly affected by negative news, and buyers need to remain vigilant.參考文獻 Baur, D. G., & Hoang, L. T. (2021). A crypto safe haven against Bitcoin. Finance Research Letters, 38, 101431.Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192-198.Charfeddine, L., Benlagha, N., & Maouchi, Y. (2020). Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors. Economic Modelling, 85, 198-217.Conlon, T., Corbet, S., & McGee, R. J. (2021). Inflation and cryptocurrencies revisited: A time-scale analysis. Economics Letters, 206, 109996.Conlon, T., & McGee, R. (2020). Safe haven or risky hazard? Bitcoin during the COVID-19 bear market. Finance Research Letters, 35, 101607.Corbet, S., Hou, Y. G., Hu, Y., Larkin, C., & Oxley, L. (2020). Any port in a storm: Cryptocurrency safe-havens during the COVID-19 pandemic. Economics Letters, 194, 109377.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.Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251-276.Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of econometrics, 2(2), 111-120.Hasan, M. B., Hassan, M. K., Rashid, M. M., & Alhenawi, Y. (2021). Are safe haven assets really safe during the 2008 global financial crisis and COVID-19 pandemic? Global Finance Journal, 50, 100668.Jareño, F., González, M. d. l. O., López, R., & Ramos, A. R. (2021). Cryptocurrencies and oil price shocks: A NARDL analysis in the COVID-19 pandemic. Resources Policy, 74, 102281.Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254.Lyócsa, Š., Molnár, P., Plíhal, T., & Širaňová, M. (2020). Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin. Journal of Economic Dynamics and Control, 119, 103980.Mnif, E., Jarboui, A., & Mouakhar, K. (2020). How the cryptocurrency market has performed during COVID 19? A multifractal analysis. Finance Research Letters, 36, 101647.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.Nguyen, T. V., Nguyen, T. V. H., Nguyen, T. C., Pham, T. T. A., & Nguyen, Q. M. (2022). Stablecoins versus traditional cryptocurrencies in response to interbank rates. Finance Research Letters, 47, 102744.Pesaran, M. H., & Shin, Y. (1999). An Autoregressive Distributed-Lag Modelling Approach to Cointegration Analysis. In S. Strøm (Ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium (pp. 371-413). Cambridge University Press. https://doi.org/DOI: 10.1017/CCOL521633230.011Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289-326. http://www.jstor.org/stable/2678547Poyser, O. (2017). Exploring the determinants of Bitcoin`s price: an application of Bayesian Structural Time Series. arXiv preprint arXiv:1706.01437.Selmi, R., Mensi, W., Hammoudeh, S., & Bouoiyour, J. (2018). Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold. Energy Economics, 74, 787-801.Shaikh, I. (2020). Policy uncertainty and Bitcoin returns. Borsa Istanbul Review, 20(3), 257-268.Sovbetov, Y. (2018). Factors influencing cryptocurrency prices: Evidence from bitcoin, ethereum, dash, litcoin, and monero. Journal of Economics and Financial Analysis, 2(2), 1-27.Wang, G.-J., Ma, X.-y., & Wu, H.-y. (2020). Are stablecoins truly diversifiers, hedges, or safe havens against traditional cryptocurrencies as their name suggests? Research in International Business and Finance, 54, 101225.Xie, Y., Kang, S. B., & Zhao, J. (2021). Are stablecoins safe havens for traditional cryptocurrencies? An empirical study during the COVID-19 pandemic. Applied Finance Letters, 10, 2-9. 描述 碩士
國立政治大學
金融學系
110352001資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110352001 資料類型 thesis dc.contributor.advisor 林建秀 zh_TW dc.contributor.advisor Lin, Chien-Hsiu en_US dc.contributor.author (Authors) 張敦翔 zh_TW dc.contributor.author (Authors) Chang, Tun-Hsiang en_US dc.creator (作者) 張敦翔 zh_TW dc.creator (作者) Chang, Tun-Hsiang en_US dc.date (日期) 2023 en_US dc.date.accessioned 6-Jul-2023 16:45:21 (UTC+8) - dc.date.available 6-Jul-2023 16:45:21 (UTC+8) - dc.date.issued (上傳時間) 6-Jul-2023 16:45:21 (UTC+8) - dc.identifier (Other Identifiers) G0110352001 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/145853 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 110352001 zh_TW dc.description.abstract (摘要) 從加密貨幣問世以來，透過去中心化與不受政府監管的特點博取廣大市場投資者的興趣，在這波浪潮下，關於加密貨幣的研究也如雨後春筍般產出。然而，近年來，儘管加密貨幣與傳統金融市場的低相關性或對沖風險能力逐漸獲得認可，但其極大的波動幅度也常使眾人卻步。為了改善這項缺點，與具備穩定價格的資產掛勾的穩定幣便應運而生，以加密貨幣資金避風港的角色於2014年開始發行。COVID-19爆發之後，隨著美國重啟量化寬鬆政策（Quantitative easing）與接連降息刺激經濟，大量資金流入市場推升加密貨幣價格，紛紛創下歷史新高。但就在2022年3月美國聯準會宣布恢復升息循環與縮表以打擊居高不下的通貨膨脹後，加密貨幣整體市值出現急遽下滑，也讓穩定幣再度浮上檯面。本文有別於過往文獻，將加密貨幣與穩定幣一同放入比較，觀察兩者對於傳統金融市場、商品以及不同變數間的反應。研究方法則首先以單根檢定確認變數序列有無呈現定態（Stationary），再結合ARDL（Autoregressive Distributed Lag Model）模型與Pesaran and Shin (1999)與Pesaran et al. (2001)所提出的 Bound Testing檢定加密貨幣、穩定幣和解釋變數間是否存在長期共整合關係，同時利用誤差修正模型（Error Correction Model）來識別短期互動方向，最後分別探討COVID-19疫情前後彼此間關聯是否發生變化。實證結果發現，加密貨幣不管在長期下與利率、避險資產黃金，抑或是短期內與恐慌指數VIX的互動，都凸顯加密貨幣帶給投資組合的避險效益有限。然而在穩定幣上，除了可以提供交易者在加密貨幣市況不佳時暫避風頭，本文更發現穩定幣短期下能夠在熊市或市場情緒恐慌之際，吸引資金流入，甚至在疫情後與利率的連結產生轉變，被投資者視為避風港。不過，由於加密貨幣與穩定幣屬於新興領域，市場信心度受負面消息影響甚巨，購買者仍須時刻注意。 zh_TW dc.description.abstract (摘要) Since the emergence of cryptocurrency, its decentralized and unregulated features have attracted the interest of many investors, leading to a surge of research on the topic. Despite the low correlation or hedging ability of cryptocurrencies with traditional financial markets, their high volatility has often made investors cautious. To address this issue, stablecoins linked to assets with stable prices were introduced in 2014 to serve as a safe haven for cryptocurrency. After the outbreak of COVID-19, the injection of massive amounts of funds into the market by the US government`s quantitative easing policy and consecutive rate cuts pushed up the prices of cryptocurrencies, setting new historical highs one after another. But in March 2022, the Federal Reserve announced the resumption of interest rate hikes and balance sheet reduction to combat persistent inflation. This caused a sharp decline in the overall market capitalization of cryptocurrencies, and brought stablecoins back into the spotlight.Unlike previous literature, this paper compares both cryptocurrencies and stablecoins, examining their reactions to traditional financial markets, common trading assets, and different variables. The research method first confirms the stationarity of variable sequences using the unit root test, then combining the ARDL (Autoregressive Distributed Lag Model) model with the Bound Testing proposed by Pesaran and Shin (1999) and Pesaran et al. (2001) to investigate the existence of long-term cointegration relationships among cryptocurrencies, stablecoins, and explanatory variables. The Error Correction Model is used to identify short-term interactive directions, and the changes in their correlation before and after the COVID-19 pandemic are separately explored.Empirical results show that cryptocurrencies have limited hedging benefits for investment portfolios in the long run, regardless of their interactions with interest rates, hedge assets such as gold, or the short-term interaction with the VIX index. On the other hand, stablecoins not only provide traders with a safe haven during adverse cryptocurrency market conditions, but also attract capital inflows during bear markets or market panic in the short term. Moreover, the link between stablecoins and interest rates underwent a change after the pandemic, making them more appealing to investors as a safe haven. However, as cryptocurrencies and stablecoins are still emerging fields, their market confidence is highly affected by negative news, and buyers need to remain vigilant. en_US dc.description.tableofcontents 摘要 iiAbstract iii目次 v表次 vi圖次 viii第一章 緒論 1第一節 研究背景 1第二節 研究動機與目的 4第三節 研究架構 5第二章 文獻回顧 7第三章 研究資料與方法 10第一節 資料選擇 10第二節 變數間預期方向 11第三節 單根檢定 13第四節 ARDL/Bound Testing 15第四章 實證分析與結果 20第一節 敘述統計 20第二節 單根檢定結果 23第三節 ARDL/Bound Testing實證結果 29第四節 短期ARDL結果 41第五章 結論 46參考文獻 48 zh_TW dc.format.extent 2143463 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110352001 en_US dc.subject (關鍵詞) 加密貨幣 zh_TW dc.subject (關鍵詞) 穩定幣 zh_TW dc.subject (關鍵詞) ARDL zh_TW dc.subject (關鍵詞) 共整合分析 zh_TW dc.subject (關鍵詞) 誤差修正模型 zh_TW dc.subject (關鍵詞) Cryptocurrency en_US dc.subject (關鍵詞) Stablecoin en_US dc.subject (關鍵詞) ARDL en_US dc.subject (關鍵詞) Cointegration Analysis en_US dc.subject (關鍵詞) Error Correction Model en_US dc.title (題名) 加密貨幣、穩定幣與金融市場長短期互動關係 zh_TW dc.title (題名) Long-term and Short-term Interaction among Cryptocurrencies, Stablecoins and Financial Markets en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Baur, D. G., & Hoang, L. T. (2021). A crypto safe haven against Bitcoin. Finance Research Letters, 38, 101431.Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192-198.Charfeddine, L., Benlagha, N., & Maouchi, Y. (2020). Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors. Economic Modelling, 85, 198-217.Conlon, T., Corbet, S., & McGee, R. J. (2021). Inflation and cryptocurrencies revisited: A time-scale analysis. Economics Letters, 206, 109996.Conlon, T., & McGee, R. (2020). Safe haven or risky hazard? Bitcoin during the COVID-19 bear market. Finance Research Letters, 35, 101607.Corbet, S., Hou, Y. G., Hu, Y., Larkin, C., & Oxley, L. (2020). Any port in a storm: Cryptocurrency safe-havens during the COVID-19 pandemic. Economics Letters, 194, 109377.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.Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251-276.Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of econometrics, 2(2), 111-120.Hasan, M. B., Hassan, M. K., Rashid, M. M., & Alhenawi, Y. (2021). Are safe haven assets really safe during the 2008 global financial crisis and COVID-19 pandemic? Global Finance Journal, 50, 100668.Jareño, F., González, M. d. l. O., López, R., & Ramos, A. R. (2021). Cryptocurrencies and oil price shocks: A NARDL analysis in the COVID-19 pandemic. Resources Policy, 74, 102281.Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254.Lyócsa, Š., Molnár, P., Plíhal, T., & Širaňová, M. (2020). Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin. Journal of Economic Dynamics and Control, 119, 103980.Mnif, E., Jarboui, A., & Mouakhar, K. (2020). How the cryptocurrency market has performed during COVID 19? A multifractal analysis. Finance Research Letters, 36, 101647.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.Nguyen, T. V., Nguyen, T. V. H., Nguyen, T. C., Pham, T. T. A., & Nguyen, Q. M. (2022). Stablecoins versus traditional cryptocurrencies in response to interbank rates. Finance Research Letters, 47, 102744.Pesaran, M. H., & Shin, Y. (1999). An Autoregressive Distributed-Lag Modelling Approach to Cointegration Analysis. In S. Strøm (Ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium (pp. 371-413). Cambridge University Press. https://doi.org/DOI: 10.1017/CCOL521633230.011Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289-326. http://www.jstor.org/stable/2678547Poyser, O. (2017). Exploring the determinants of Bitcoin`s price: an application of Bayesian Structural Time Series. arXiv preprint arXiv:1706.01437.Selmi, R., Mensi, W., Hammoudeh, S., & Bouoiyour, J. (2018). Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold. Energy Economics, 74, 787-801.Shaikh, I. (2020). Policy uncertainty and Bitcoin returns. Borsa Istanbul Review, 20(3), 257-268.Sovbetov, Y. (2018). Factors influencing cryptocurrency prices: Evidence from bitcoin, ethereum, dash, litcoin, and monero. Journal of Economics and Financial Analysis, 2(2), 1-27.Wang, G.-J., Ma, X.-y., & Wu, H.-y. (2020). Are stablecoins truly diversifiers, hedges, or safe havens against traditional cryptocurrencies as their name suggests? Research in International Business and Finance, 54, 101225.Xie, Y., Kang, S. B., & Zhao, J. (2021). Are stablecoins safe havens for traditional cryptocurrencies? An empirical study during the COVID-19 pandemic. Applied Finance Letters, 10, 2-9. zh_TW
