政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146285

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146285

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政大典藏 > College of Commerce > Department of Finance > Theses > 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:	[Department of Finance] Theses

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