政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/137060
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 113656/144643 (79%)
造访人次 : 51740043      在线人数 : 567
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/137060


    题名: 比特幣：投機與避險？
    Bitcoin:Speculative Trading or Hedging?
    作者: 黃騵禾
    Huang, Yuan-Ho
    贡献者: 徐士勛
    邱惠玉
    Hsu, Shih­-Hsun
    Chiu, Huei­-Yu
    黃騵禾
    Huang, Yuan-Ho
    关键词: 比特幣
    投機
    避險
    LMSW模型
    馬可夫轉換模型
    Bitcoin
    Speculative trading
    Hedging
    LMSW model
    Markov switching model
    日期: 2021
    上传时间: 2021-09-02 17:41:40 (UTC+8)
    摘要: 本研究探討比特幣在新冠肺炎疫情(Covid19)經濟衰退下，比特幣市場主要是由是投機性交易或是避險性交易為主導。有別於研究比特幣與其他資產的動態關聯性去檢驗其避險或投機性，本文從投資者的動機與交易目的作為切入，採用Llorente et al. (2002) 有關市場投資人因訊息不對稱(Information asymmetry)，不同動機產生的報酬與交易量的動態關係，來檢驗市場是由投機或避險交易主導。並採用Hamilton(1989) 馬可夫轉換模型(Markov Switching Model) 捕捉繁複的動態與狀態改變的行為。

    實證結果顯示比特幣市場大多時候充斥著投機交易。知情投資者在訊息不對稱的情況下，擁有對比特幣的未來私人信息，並會在訊息尚未公布之前，出於投機動機而交易比特幣。隨著私人信息的公開，原本預期的價格會逐漸實現，最終反映知情投資者對未來報酬的預期。而在市場情緒動盪高波動的時刻，投資者出於避險動機，改變持有權重來降低其非交易資產的風險，可能造成市場大量拋售或是看空的情況，並在該期間產生負報酬。此類價格變化並不包含未來報酬的信息，價格最終會相應調整，並吸引其他投資者進入市場，導致下一期預期報酬的增加，因此避險交易產生的報酬容易出現反轉的現象。
    This research explores whether Bitcoin market is dominated by speculative trading or hedging during the Covid19 recession. Instead of studying the relation between Bitcoin and other assets, we based on Llorente et al.(2002)s’information asymmetry theory, which observes the specific pattern of the returns and volume generated by different motivations, to test whether the market is dominated by speculation or hedging transactions. Also, we employ Markov switching model to capture the state-changing behaviors.

    The empirical result shows that Bitcoin market is mostly dominated by speculative trading during the time. Informed investors have private information about the future Bitcoin return, and they would speculate in Bitcoin. With the disclosure of private information, the price would gradually be realized and eventually reflect their expectations. When the market is turbulent, investors would change their holding out of risk-averse motives. The price would adjust by attracting other investors to enter the market and the returns generated by risk-sharing trades tend to reverse themselves.
    參考文獻: Andrews, D. W. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica: Journal of the Econometric Society, 821–856.
    Andrews, D. W., & Ploberger, W. (1994). Optimal tests when a nuisance parameter is present only under the alternative. Econometrica: Journal of the Econometric Society, 1383–1414.
    Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 47–78.
    Baur, D. G., & Dimpfl, T. (2017). Realized bitcoin volatility. SSRN, 2949754, 1–26.
    Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions and Money, 54, 177–189.
    Baur, D. G., & Lucey, B. M. (2010). Is gold a hedge or a safe haven? an analysis of stocks, bonds and gold. Financial Review, 45(2), 217–229.
    Biais, B., Bisiere, C., Bouvard, M., Casamatta, C., & Menkveld, A. J. (2020). Equilibrium bitcoin pricing. Available at SSRN 3261063.
    Blau, B. M. (2018). Price dynamics and speculative trading in bitcoin. Research in International Business and Finance, 43, 15–21.
    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.
    Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
    Buchholz, M., Delaney, J., Warren, J., & Parker, J. (2012). Bits and bets, information,price volatility, and demand for itcoin. Economics, 312, 2–48.
    Campbell, J. Y., Grossman, S. J., & Wang, J. (1993). Trading volume and serial correlation in stock returns. The Quarterly Journal of Economics, 108(4), 905–939. Cerra, V., & Saxena, S. C. (2005). Did output recover from the asian crisis? IMF Staff Papers, 52(1), 1–23.
    Cheung, A., Roca, E., & Su, J.J. (2015). Cryptocurrency bubbles: an application of the Phillips–Shi–Yu(2013) methodology on mt. gox bitcoin prices. Applied Economics, 47(23), 2348–2358.
    Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions.
    Econometrica: Journal of the Econometric Society, 591–605.
    Ciaian, P., Rajcaniova, M., & Kancs, d. (2016). The economics of bitcoin price formation. Applied Economics, 48(19), 1799–1815.
    Cong, L. W., Li, Y., & Wang, N. (2021). Tokenomics: Dynamic adoption and valuation. The Review of Financial Studies, 34(3), 1105–1155.
    Conlon, T., & McGee, R. (2020). Safe haven or risky hazard? bitcoin during the covid19 bear market. Finance Research Letters, 35, 101607.
    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 copulabased granger causality test. Finance Research Letters, 28, 160–164. Davig, T. (2004). Regimeswitching
    debt and taxation. Journal of Monetary Economics, 51(4).
    Drobetz, W., Momtaz, P. P., & Schröder, H. (2019). Investor sentiment and initial coin offerings. The Journal of Alternative Investments, 21(4), 41–55.
    Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar–a garch volatility analysis. Finance Research Letters, 16, 85–92.
    Gallant, A. R., Rossi, P. E., & Tauchen, G. (1992). Stock prices and volume. The Review of Financial Studies, 5(2), 199–242.
    Garcia, R., Luger, R., & Renault, E. (2003). Empirical assessment of an intertemporal option pricing model with latent variables. Journal of Econometrics, 116(12), 49–83.
    Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D., & Giaglis, G. M. (2015). Using timeseries and sentiment analysis to detect the determinants of bitcoin prices. Available at SSRN 2607167.
    Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the econometric society, 357–384.
    Hamilton, J. D. (1990). Analysis of time series subject to changes in regime. Journal of econometrics, 45(12), 39–70.
    Hamilton, J. D. (1994). Time series analysis. Princeton university press.
    Hamilton, J. D., & PerezQuiros, G. (1996). What do the leading indicators lead? Journal of Business, 27–49.
    Hayes, A. S. (2019). Bitcoin price and its marginal cost of production: support for a fundamental value. Applied Economics Letters, 26(7), 554–560.
    Karalevicius, V., Degrande, N., & De Weerdt, J. (2018). Using sentiment analysis to predict interday bitcoin price movements. The Journal of Risk Finance.
    Karpoff, J. M. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and quantitative Analysis, 109–126.
    Kim, C.J. (1994). Dynamic linear models with markovswitching. Journal of Econometrics, 60(12), 1–22.
    Kim, C.J., & Nelson, C. R. (2017). Statespace models with regime switching: Classical and gibbssampling approaches with applications. MIT press.
    Koutmos, D. (2018). Bitcoin returns and transaction activity. Economics Letters, 167, 81–85.
    Kristoufek, L. (2013). Bitcoin meets google trends and wikipedia: Quantifying the relationship between phenomena of the internet era. Scientific reports, 3(1), 1–7.
    Kristoufek, L. (2015). What are the main drivers of the bitcoin price? evidence from wavelet coherence analysis. PloS one, 10(4), e0123923.
    Lee, T.H., & Yang, W. (2014). Grangercausality in quantiles between financial markets: Using copula approach. International Review of Financial Analysis, 33, 70–78.
    Li, J., & Yi, G. (2019). Toward a factor structure in crypto asset returns. The Journal of Alternative Investments, 21(4), 56–66.
    Liu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. The Review of Financial Studies, 34(6), 2689–2727.
    Llorente, G., Michaely, R., Saar, G., & Wang, J. (2002). Dynamic volumereturn relation of individual stocks. The Review of financial studies, 15(4), 1005–1047.
    Lo, A. W., & Wang, J. (2000). Trading volume: definitions, data analysis, and implications of portfolio theory. The Review of Financial Studies, 13(2), 257–300.
    Lucas Jr, R. E. (1978). Asset prices in an exchange economy. Econometrica: Journal of the Econometric Society, 1429–1445.
    MacDonell, A. (2014). Popping the bitcoin bubble: An application of logperiodic power law modeling to digital currency. University of Notre Dame working paper, 1–33.
    Markov, A. (1907). Extension of the limit theorems of probability theory to a sum of variables connected in a chain, the notes of the imperial academy of sciences of st. Petersburg VIII Series, PhysioMathematical College, 22(9).
    Mensi, W., AlYahyaee, K. H., & Kang, S. H. (2019). Structural breaks and double long memory of cryptocurrency prices: A comparative analysis from bitcoin and
    ethereum. Finance Research Letters, 29, 222–230.
    Nakamoto, S. (2008). Bitcoin: A peertopeer electronic cash system. Decentralized Business Review, 21260.
    Pel, A. (2015). Money for nothing and bits for free: the geographies of bitcoin. Department of Geography and Planning University of Toronto.
    Phillips, P. C., Shi, S., & Yu, J. (2015). Testing for multiple bubbles: Limit theory of real-time detectors. International Economic Review, 56(4), 1079–1134.
    Pindyck, R. S., Rubinfeld, D. L., & Rabasco, E. (2013). Microeconomia. Pearson Italia.
    Quandt, R. E. (1960). Tests of the hypothesis that a linear regression system obeys two separate regimes. Journal of the American statistical Association, 55(290), 324–330.
    Schwert, G. W. (1989). Why does stock market volatility change over time? The journal of finance, 44(5), 1115–1153.
    Shiller, R. J., Fischer, S., & Friedman, B. M. (1984). Stock prices and social dynamics.Brookings papers on economic activity, 1984(2), 457–510.
    Sims, C. A., & Zha, T. (2006). Were there regime switches in us monetary policy? American Economic Review, 96(1), 54–81.
    Smaniotto, E. N., & Neto, G. B. (2020). Speculative trading in bitcoin: A brazilian market evidence. The Quarterly Review of Economics and Finance.
    Thies, S., & Molnár, P. (2018). Bayesian change point analysis of bitcoin returns. Finance Research Letters, 27, 223–227.
    Tirole, J. (1985). Asset bubbles and overlapping generations. Econometrica: Journal of the Econometric Society, 1499–1528.
    Wheatley, S., Sornette, D., Huber, T., Reppen, M., & Gantner, R. N. (2019). Are bitcoin bubbles predictable? combining a generalized metcalfe’s law and the logperiodic
    power law singularity model. Royal Society open science, 6(6), 180538.
    Wöckl, I. (2019). Bubble detection in financial marketsa survey of theoretical bubble models and empirical bubble detection tests. Available at SSRN 3460430.
    描述: 碩士
    國立政治大學
    經濟學系
    106258038
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0106258038
    数据类型: thesis
    DOI: 10.6814/NCCU202101182
    显示于类别:[經濟學系] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    803801.pdf1530KbAdobe PDF20检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈