English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 109952/140903 (78%)
Visitors : 46035060      Online Users : 700
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 財務管理學系 > 學位論文 >  Item 140.119/146285
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/146285


    Title: 非系統風險與加密貨幣市場橫斷面報酬關係
    Idiosyncratic risk and the cross-section of expected cryptocurrency returns
    Authors: 林秉陞
    Lin, Ping-Sheng
    Contributors: 岳夢蘭
    Yueh, Meng-Lan
    林秉陞
    Lin, Ping-Sheng
    Keywords: 加密貨幣
    波動度
    VIX 指數
    非系統風險
    Cryptocurrency
    Volatility
    VIX index
    Idiosyncratic volatility
    Date: 2023
    Issue Date: 2023-08-02 12:59:22 (UTC+8)
    Abstract: 本文闡述了波動度類別、非系統風險類別、股票市場波動度、比特幣恐懼貪婪指數和加密貨幣波動指數,與加密貨幣橫斷面報酬的關係。在單變量投資組合分析下發現,波動度類別和非系統風險類別皆有統計顯著性。在股票市場波動度VIX和比特幣恐懼貪婪指數,其多空策略的結果皆不顯著,代表股票市場波動度高低,並不影響加密貨幣的報酬率。而加密貨幣波動指數在多空策略中僅有單因子模型所估計的多空策略中有些微顯著的負向,代表當加密貨幣市場波動變大時,投資人會將資金投入加密貨幣波動指數Beta係數更高的加密貨幣進行避險,造成價格變高,報酬率變低的情形。
    本文也針對在單變量投資組合分析中有顯著的6個特徵,進行單因子、雙因子和三因子模型的研究,以了解該特徵是否能被因子模型所解釋。結果發現,單因子跟雙因子模型就能解釋非系統風險特徵,波動度類別中僅有報酬率的波動度在包含動量因子的雙因子模型中能夠被解釋,其餘的兩個特徵則是用三因子模型也無法完全解釋其報酬率。
    This article discusses volatility, non-systematic risk, volatility, Bitcoin fear and greed index, and cryptocurrency volatility index, and their relationship with cross-sectional returns of cryptocurrencies. In the analysis of univariate portfolio, both volatility categories and non-systematic risk categories were found to be statistically significant. Regarding stock market volatility (VIX) and Bitcoin fear and greed index, the results of long-short strategies were not significant, indicating that the volatility of the stock market does not affect the returns of cryptocurrencies. However, the cryptocurrency volatility index showed a slightly significant negative relationship in the long-short strategy estimated by the single-factor model, suggesting that when the cryptocurrency market becomes more volatile, investors tend to invest in cryptocurrencies with higher beta coefficients in the cryptocurrency volatility index as a hedge, causing higher prices and lower returns.
    This article also examines six significant features identified in the univariate portfolio analysis using single-factor, two-factor, and three-factor models to determine if these features can be explained by factor models. The results show that the non-systematic risk feature can be explained by both single-factor and two-factor models. Among the volatility categories, only the volatility of returns can be explained by the two-factor model that includes the momentum factor, while the other two features cannot be fully explained even by the three-factor model in terms of their returns.
    Reference: Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets, 5(1), 31-56.
    Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross‐section of volatility and expected returns. The Journal of Finance, 61(1), 259-299.
    Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
    Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680.
    Bali, T. G., & Cakici, N. (2008). Idiosyncratic volatility and the cross section of expected returns. Journal of Financial and Quantitative Analysis, 43(1), 29-58.
    Bai, J., Bali, T. G., & Wen, Q. (2021). Is there a risk-return tradeoff in the corporate bond market? Time-series and cross-sectional evidence. Journal of Financial Economics, 142(3), 1017-1037.
    Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57-82.
    Chordia, T., Subrahmanyam, A., & Anshuman, V. R. (2001). Trading activity and expected stock returns. Journal of Financial Economics, 59(1), 3-32.
    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.
    Chung, K. H., Wang, J., & Wu, C. (2019). Volatility and the cross-section of corporate bond returns. Journal of Financial Economics, 133(2), 397-417.
    Daniel, K., & Titman, S. (1997). Evidence on the characteristics of cross-sectional variation in stock returns. Journal of Finance, 52(1), 1-33.
    Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
    Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
    Fu, F. (2009). Idiosyncratic risk and the cross-section of expected stock returns. Journal of Financial Economics, 91(1), 24-37.
    Liu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. The Review of Financial Studies, 34(6), 2689-2727.
    Liu, Y., Tsyvinski, A., & Wu, X. (2022). Common risk factors in cryptocurrency. The Journal of Finance, 77(2), 1133-1177.
    Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
    Stambaugh, R. F., Yu, J., & Yuan, Y. (2015). Arbitrage asymmetry and the idiosyncratic volatility puzzle. The Journal of Finance, 70(5), 1903-1948.
    Description: 碩士
    國立政治大學
    財務管理學系
    110357023
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110357023
    Data Type: thesis
    Appears in Collections:[財務管理學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    702301.pdf1308KbAdobe PDF266View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    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 ©   - Feedback