請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95132完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 管中閔 | zh_TW |
| dc.contributor.advisor | Chung-Ming Kuan | en |
| dc.contributor.author | 黃筠凱 | zh_TW |
| dc.contributor.author | Yun-Kai Huang | en |
| dc.date.accessioned | 2024-08-29T16:13:56Z | - |
| dc.date.available | 2024-12-27 | - |
| dc.date.copyright | 2024-08-29 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-14 | - |
| dc.identifier.citation | Ahn, Y., & Kim, D. (2020). Sentiment disagreement and Bitcoin price fluctuations: A psycholinguistic approach. Applied Economics Letters, 27(5), 412–416. https://doi.org/10.1080/13504851.2019.1619013
Assaf, A., Demir, E., & Ersan, O. (2024). Detecting and date-stamping bubbles in fan tokens. International Review of Economics Finance, 92, 98–113. https://doi.org/10.1016/j.iref.2024.01.039 Azariadis, C. (1981). Self-fulfilling prophecies. Journal of Economic Theory, 25(3), 380–396. https://doi.org/10.1016/0022-0531(81)90038-7 Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307–343. https://doi.org/10.1016/S0304-405X(98)00027-0 Bashchenko, O., & Marchal, A. (2020). Deep learning for asset bubbles detection (Research Paper No. 20-08). Swiss Finance Institute. https://doi.org/10.2139/ssrn.3531154 Black, F. (1986). Noise. The Journal of Finance, 41(3), 528–543. https://doi.org/10.1111/j.1540-6261.1986.tb04513.x Blanchard, O. J. (1979). Speculative bubbles, crashes and rational expectations. Economics Letters, 3(4), 387–389. https://doi.org/10.1016/0165-1765(79)90017-X Blanchard, O. J., & Watson, M. W. (1982). Bubbles, rational expectations and financial markets. National Bureau of Economic Research Working Paper Series, No. 945. https://doi.org/10.3386/w0945 Bouri, E., Gupta, R., & Roubaud, D. (2019). Herding behaviour in cryptocurrencies. Finance Research Letters, 29, 216–221. https://doi.org/10.1016/j.frl.2018.07.008 Bouri, E., Shahzad, S. J. H., & Roubaud, D. (2019). Co-explosivity in the cryptocurrency market. Finance Research Letters, 29, 178–183. https://doi.org/10.1016/j.frl.2018.07.005 Bouteska, A., Mefteh-Wali, S., & Dang, T. (2022). Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic. Technological Forecasting and Social Change, 184, 121999. https://doi.org/10.1016/j.techfore.2022.121999 Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press. https://doi.org/10.2307/j.ctt7skm5 Cass, D., & Shell, K. (1983). Do sunspots matter? Journal of Political Economy, 91(2), 193–227. http://www.jstor.org/stable/1832054 Cheung, A., Roca, E., & Su, J.-J. (2015). Crypto-currency bubbles: An application of the Phillips–Shi–Yu (2013) methodology on Mt. Gox Bitcoin prices. Applied Economics, 47(23), 2348–2358. https://doi.org/10.1080/00036846.2015.1005827 Chowdhury, M. S. R., Damianov, D. S., & Elsayed, A. H. (2022). Bubbles and crashes in cryptocurrencies: Interdependence, contagion, or asset rotation? Finance Research Letters, 46, 102494. https://doi.org/10.1016/j.frl.2021.102494 Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81–88. https://doi.org/10.1016/j.frl.2017.12.006 Crépellière, T., Pelster, M., & Zeisberger, S. (2023). Arbitrage in the market for cryptocurrencies. Journal of Financial Markets, 64, 100817. https://doi.org/10.1016/j.finmar.2023.100817 Daniel, K., Hirshleifer, D., & Teoh, S. H. (2002). Investor psychology in capital markets: Evidence and policy implications. Journal of Monetary Economics, 49(1), 139–209. https://doi.org/10.1016/S0304-3932(01)00091-5 Dastgir, S., Demir, E., Downing, G., Gozgor, G., & Lau, C. K. M. (2019). The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the Copula- based Granger causality test. Finance Research Letters, 28, 160–164. https://doi.org/10.1016/j.frl.2018.04.019 De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703–738. http://www.jstor.org/stable/2937765 Diba, B. T., & Grossman, H. I. (1988). Explosive rational bubbles in stock prices? The American Economic Review, 78(3), 520–530. http://www.jstor.org/stable/1809149 Dickey, D., & Fuller, W. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348 Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. https://doi.org/10.2307/1913236 Enoksen, F. A., Landsnes, C. J., Lučivjanská, K., & Molnár, P. (2020). Understanding risk of bubbles in cryptocurrencies. Journal of Economic Behavior & Organization, 176, 129–144. https://doi.org/10.1016/j.jebo.2020.05.005 Evans, G. W. (1991). Pitfalls in testing for explosive bubbles in asset prices. The American Economic Review, 81(4), 922–930. http://www.jstor.org/stable/2006651 Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486 Fama, E. F. (2014). Two pillars of asset pricing. American Economic Review, 104(6), 1467–1485. https://doi.org/10.1257/aer.104.6.1467 Filardo, A. (2004). Should monetary policy respond to asset price bubbles? Some experimental results (BIS Working Paper). Bank for International Settlements. https://doi.org/10.2139/ssrn.285413 Flood, R. P., & Garber, P. M. (1980). Market fundamentals versus price-level bubbles: The first tests. Journal of Political Economy, 88(4), 745–770. http://www.jstor.org/stable/1837311 Flood, R. P., & Garber, P. M. (1991). The linkage between speculative attack and target zone models of exchange rates. The Quarterly Journal of Economics, 106(4), 1367–1372. https://doi.org/10.2307/2937968 Foley, S., Karlsen, J. R., & Putniņš, T. J. (2019). Sex, drugs, and Bitcoin: How much illegal activity is financed through cryptocurrencies? The Review of Financial Studies, 32(5), 1798–1853. https://doi.org/10.1093/rfs/hhz015 Froot, K. A., & Obstfeld, M. (1991). Intrinsic bubbles: The case of stock prices. The American Economic Review, 81(5), 1189–1214. http://www.jstor.org/stable/2006913 Gandal, N., Hamrick, J. T., Moore, T., & Oberman, T. (2018). Price manipulation in the Bitcoin ecosystem. Journal of Monetary Economics, 95, 86–96. https://doi.org/10.1016/j.jmoneco.2017.12.004 Garber, P. M. (1989). Tulipmania. Journal of Political Economy, 97(3), 535–560. http://www.jstor.org/stable/1830454 Garber, P. M. (1990). Famous first bubbles. The Journal of Economic Perspectives, 4(2), 35–54. http://www.jstor.org/stable/1942889 Granger, C. W. J. (1981). Some properties of time series data and their use in econometric model specification. Journal of Econometrics, 16(1), 121–130. https://doi.org/10.1016/0304-4076(81)90079-8 Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111–120. https://doi.org/10.1016/0304-4076(74)90034-7 Griffin, J. M., & Shams, A. (2020). Is Bitcoin really untethered? The Journal of Finance, 75(4), 1913–1964. https://doi.org/10.1111/jofi.12903 Haber, S., & Stornetta, W. S. (1991). How to time-stamp a digital document. Journal of Cryptology, 3(2), 99–111. https://doi.org/10.1007/BF00196791 Hamilton, J. D. (1986). On testing for self-fulfilling speculative price bubbles. International Economic Review, 27(3), 545–552. https://doi.org/10.2307/2526680 Harvey, D. I., Leybourne, S. J., Sollis, R., & Taylor, A. M. R. (2016). Tests for explosive financial bubbles in the presence of non-stationary volatility. Journal of Empirical Finance, 38, 548–574. https://doi.org/10.1016/j.jempfin.2015.09.002 Kindleberger & Aliber. (2005). Manias, panics and crashes: A history of financial crises (5thed.,Vol.7). Palgrave Macmillan UK. https://doi.org/10.1057/9780230628045 Kristoufek, L. (2013). Bitcoin meets Google trends and Wikipedia: Quantifying the relationship between phenomena of the internet era. Scientific Reports, 3(1), 3415. https://doi.org/10.1038/srep03415 Lewis, A. (2021). The basics of Bitcoins and blockchains: An introduction to cryptocurrencies and the technology that powers them. Mango Media. https://books.google.com.tw/books?id=C8YpzgEACAAJ Lux, T. (1995). Herd behaviour, bubbles and crashes. The Economic Journal, 105(431), 881–896. https://doi.org/10.2307/2235156 M’Bakob, G. B. (2024). Bubbles in Bitcoin and Ethereum: The role of halving in the formation of super cycles. Sustainable Futures, 7, 100178. https://doi.org/10.1016/j.sftr.2024.100178 Meese, R. A. (1986). Testing for bubbles in exchange markets: A case of sparkling rates? Journal of Political Economy, 94(2), 345–373. http://www.jstor.org/stable/1837408 Merton, R. C. (1995). A functional perspective of financial intermediation. Financial Management, 24(2), 23–41. https://doi.org/10.2307/3665532 Minsky, H. P. (1986). Stabilizing an unstable economy (Vol. 1). Yale University Press. Montasser, G. E., Charfeddine, L., & Benhamed, A. (2022). COVID-19, cryptocurrencies bubbles and digital market efficiency: Sensitivity and similarity analysis. Finance Research Letters, 46, 102362. https://doi.org/10.1016/j.frl.2021.102362 Muth, J. F. (1961). Rational expectations and the theory of price movements. Econometrica, 29(3), 315–335. https://doi.org/10.2307/1909635 Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Nelson, C. R., & Plosser, C. R. (1982). Trends and random walks in macroeconmic time series: Some evidence and implications. Journal of Monetary Economics, 10(2), 139–162. https://doi.org/10.1016/0304-3932(82)90012-5 Ng, S., & Perron, P. (1995). Unit root tests in ARMA models with data-dependent methods for the selection of the truncation lag. Journal of the American Statistical Association, 90(429), 268–281. https://doi.org/10.1080/01621459.1995.10476510 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. https://doi.org/10.1111/1468-0262.00256 Obstfeld, M., & Rogoff, K. (1983). Speculative hyperinflations in maximizing models: Can we rule them out? Journal of Political Economy, 91(4), 675–687. http://www.jstor.org/stable/1831073 Obstfeld, M., & Rogoff, K. (1986). Ruling out divergent speculative bubbles. Journal of Monetary Economics, 17(3), 349–362. https://doi.org/10.1016/0304-3932(86)90062-0 Pedersen, T. Q., & Schütte, E. C. M. (2020). Testing for explosive bubbles in the presence of autocorrelated innovations. Journal of Empirical Finance, 58, 207–225. https://doi.org/10.1016/j.jempfin.2020.06.002 Phillips, P. C. B. (1987). Time series regression with a unit root. Econometrica, 55(2), 277–301. https://doi.org/10.2307/1913237 Phillips, P. C. B., & Shi, S. (2020). Real time monitoring of asset markets: Bubbles and crises. In H. D. Vinod & C. R. Rao (Eds.), Handbook of statistics (pp. 61–80). Elsevier. https://doi.org/10.1016/bs.host.2018.12.002 Phillips, P. C. B., Shi, S., & Yu, J. (2014). Specification sensitivity in right-tailed unit root testing for explosive behaviour. Oxford Bulletin of Economics and Statistics, 76(3), 315–333. https://doi.org/10.1111/obes.12026 Phillips, P. C. B., Shi, S., & Yu, J. (2015a). Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500. International Economic Review, 56(4), 1043–1078. https://doi.org/10.1111/iere.12132 Phillips, P. C. B., Shi, S., & Yu, J. (2015b). Testing for multiple bubbles: Limit theory of real-time detectors. International Economic Review, 56(4), 1079–1134. https://doi.org/10.1111/iere.12131 Phillips, P. C. B., Wu, Y., & Yu, J. (2011). Explosive behavior in the 1990s NASDAQ: When did exuberance escalate asset values? International Economic Review, 52(1), 201–226. https://doi.org/10.1111/j.1468-2354.2010.00625.x Said, E. S., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607. https://doi.org/10.2307/2336570 Samuelson, P. A. (1965). Proof that properly anticipated prices fluctuate randomly. IMR; Industrial Management Review (pre-1986), 6(2), 41. Shahzad, S. J. H., Anas, M., & Bouri, E. (2022). Price explosiveness in cryptocurrencies and Elon Musk’s tweets. Finance Research Letters, 47, 102695. https://doi.org/ 10.1016/j.frl.2022.102695 Shen, D., Urquhart, A., & Wang, P. (2019). Does twitter predict Bitcoin? Economics Letters, 174, 118–122. https://doi.org/10.1016/j.econlet.2018.11.007 Shiller, R. J. (2015). Irrational exuberance revised and expanded third edition (REV - Revised, 3). Princeton University Press. https://doi.org/10.2307/j.ctt1287kz5 Skrobotov, A. (2023). Testing for explosive bubbles: A review. Dependence Modeling, 11(1). https://doi.org/10.1515/demo-2022-0152 Smith, V. L., Suchanek, G. L., & Williams, A. W. (1988). Bubbles, crashes, and endoge- nous expectations in experimental spot asset markets. Econometrica, 56(5), 1119–1151. https://doi.org/10.2307/1911361 Summers, B. J. (1994). The payment system: Design, management, and supervision. International Monetary Fund. https://doi.org/10.5089/9781557753861.071 Thomas, W. I. (1928). The child in America: Behavior problems and programs. Knopf. Tsay, R. (2010). Linear time series analysis and its applications. In Analysis of financial time series (3rd ed., p. 33). John Wiley Sons, Ltd. https://doi.org/10.1002/9780470644560.ch2 Turk, Ž., & Klinc, R. (2017). Potentials of blockchain technology for construction management. Procedia Engineering, 196, 638–645. https://doi.org/10.1016/j.proeng.2017.08.052 Vidal-Tomás, D. (2022). Which cryptocurrency data sources should scholars use? International Review of Financial Analysis, 81, 102061. https://doi.org/10.1016/j.irfa.2022.102061 West, K. D. (1987). A specification test for speculative bubbles. The Quarterly Journal of Economics, 102(3), 553–580. https://doi.org/10.2307/1884217 Xu, Y. (2023). Behavioral finance: An introduction of herd effect - Take the dotcom bubble in 2000s as an example. Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023), 216–224. https://doi.org/10.2991/978-94-6463-142-5_25 Yao, C.-Z., & Li, H.-Y. (2021). A study on the bursting point of Bitcoin based on the BSADF and LPPLS methods. The North American Journal of Economics and Finance, 55, 101280. https://doi.org/10.1016/j.najef.2020.101280 Zeira, J. (1999). Informational overshooting, booms, and crashes. Journal of Monetary Economics, 43(1), 237–257. https://doi.org/10.1016/S0304-3932(98)00042-7 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95132 | - |
| dc.description.abstract | 新冠疫情的爆發促進了加密貨幣市場的顯著增長，市場呈現高度波動與頻繁劇烈的價格震盪，識別和分析加密貨幣市場中的泡沫現象儼然成為投資者、研究者與監管單位的關注焦點。本研究選取市值排名前三的加密貨幣：比特幣、以太幣與幣安幣作為研究對象，並應用Phillips, Wu 與 Yu (2011, PWY) 提出的 supremum augmented Dickey–Fuller (SADF) 檢定與 Phillips, Shi與Yu (2015a, PSY) 提出 generalized SADF (GSADF) 檢定方法進行泡沫檢測，並透過backward SADF (BSADF) 檢定標記出泡沫發生日期。研究結果顯示，這些加密貨幣在樣本期間內均被檢測出多次泡沫。儘管這些檢定能有效識別部分泡沫，但在面對加密貨幣價格複雜且變化劇烈的序列特徵時，PWY(2011) 與 PSY(2015a) 所提出的泡沫偵測模式的應用價值仍有所局限，這一缺失有待進一步改進。然而，這些模型仍然為投資者提供了具有價值的預警資訊。此外，本研究亦發現，加密貨幣價格易受市場情緒、新聞事件以及政策變動的影響。 | zh_TW |
| dc.description.abstract | The COVID-19 pandemic has significantly accelerated the growth of the cryptocurrency market, characterized by high volatility and frequent dramatic price fluctuations. Identifying and analyzing bubbles in this market has become a critical concern for investors, researchers, and regulatory authorities. This study focuses on the top three cryptocurrencies by market capitalization: Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB). We employ the supremum augmented Dickey-Fuller (SADF) test proposed by Phillips, Wu, and Yu (2011, PWY), the generalized SADF (GSADF) test by Phillips, Shi, and Yu (2015a, PSY), and the backward SADF (BSADF) test to date the occurrence of bubbles. Our findings reveal multiple bubbles in these cryptocurrencies during the sample period. Although these tests effectively identify some bubbles, the complex and highly volatile nature of cryptocurrency price sequences limits their utility, indicating a need for further investigation. Despite these limitations, these models still provide valuable early warning signals for investors. Additionally, our study finds that cryptocurrency prices are influenced by market sentiment, news events, and policy changes, highlighting the multifaceted dynamics of this emerging market. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-29T16:13:56Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-29T16:13:56Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii 英文摘要 iii 目次 iv 圖次 vi 表次 vii 第一章 緒論 1 第二章 加密貨幣發展背景 3 2.1 加密貨幣緣起 3 2.2 區塊鏈技術 4 2.3 加密貨幣技術與發展 6 第三章 資產價格泡沫 12 3.1 泡沫之定義與概念 12 第四章 自我迴歸棋型與單根檢定 18 4.1 自我迴歸模型定態概念 19 4.2 時間序列單根性質 24 4.3 單根檢定 26 第五章 泡沫檢測模型 29 5.1 單根檢定在泡沫研究之應用 29 5.2 右尾單根模型一般式設定 31 5.3 SADF檢定 33 5.4 GSADF檢定 34 5.5 泡沫時間戳記 35 5.6 PWY與PSY模型應用與發展 38 第六章 研究方法與結果 41 6.1 樣本與研究方法 41 6.2 研究結果 44 第七章 結論 55 參考文獻 58 附錄 — 密碼學技術 65 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | BSADF | zh_TW |
| dc.subject | GSADF | zh_TW |
| dc.subject | 泡沫 | zh_TW |
| dc.subject | 加密貨幣 | zh_TW |
| dc.subject | SADF | zh_TW |
| dc.subject | Bubbles | en |
| dc.subject | Cryptocurrency | en |
| dc.subject | SADF | en |
| dc.subject | BSADF | en |
| dc.subject | GSADF | en |
| dc.title | 加密貨幣市場泡沫特徵與檢測方法研究 | zh_TW |
| dc.title | A Study on the Characteristics and Detection Methods of Cryptocurrency Market Bubbles | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 何耕宇;黃裕烈 | zh_TW |
| dc.contributor.oralexamcommittee | Keng-Yu Ho;Yue-Lieh Huang | en |
| dc.subject.keyword | 加密貨幣,泡沫,SADF,GSADF,BSADF, | zh_TW |
| dc.subject.keyword | Cryptocurrency,Bubbles,SADF,GSADF,BSADF, | en |
| dc.relation.page | 68 | - |
| dc.identifier.doi | 10.6342/NTU202404297 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-14 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 財務金融學系 | - |
| 顯示於系所單位： | 財務金融學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf | 10.61 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。