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題名 下行風險是否能解釋橫斷面報酬：以加密貨幣市場為例
Can downside risk explain the cross-section of cryptocurrency returns?
作者 林冠宇
Lin, Kuan-Yu
貢獻者 岳夢蘭
Yueh, Meng-Lan
林冠宇
Lin, Kuan-Yu
關鍵詞 加密貨幣
下行風險
情緒
關注度
Cryptocyrrency
Downside risk
Sentiment
Attention
日期 2023
上傳時間 2-Aug-2023 13:01:17 (UTC+8)
摘要 本文使用風險價值(Value at Risk)衡量個別加密貨幣的下行風險，並闡述了下行風險與橫斷面報酬的正向關係，在單變量投資組合分析、雙變量投資組合分析下的長短策略皆顯著為正，並且在使用三因子模型(Liu et al., 2022)進行風險調整後依然顯著為正。此外，本文使用Fama and MacBeth (1973)迴歸發現在個別貨幣的下行風險特徵與未來報酬具有顯著橫斷面關係。研究結果顯示橫斷面上，下行風險會被加密貨幣市場定價。另外，本文使用全樣本資料並控制貨幣、時間效果後，亦發現下行風險與未來報酬存在跨期關係(Intertemporal relation)。
本文的額外研究發現股市情緒、加密貨幣關注度對於下行風險具有解釋能力，並且加密貨幣關注度在短、中長期的影響方向相反。本文分別使用Barber and Odean (2007)的關注度理論、Baker and Wurgler (2006)的情緒研究結果進行解釋，並且發現情緒假說能在中長期解釋本文的實證結果，而關注度理論則皆能解釋。
In this paper, we use Value at Risk as a measure to downside risk, and discuss the positive relationship between downside risk and cross-sectional returns. We find that in both univariate and bivariate portfolio analyses, long and short strategies exhibit a significant positive relationship. Even after adjusting for risk using the three-factor model(Liu et al., 2022), this positive relationship remains significant. Furthermore, employing the Fama and MacBeth (1973) regression, we discover a significant cross-sectional relationship between the downside risk characteristics of individual currencies and future returns. The research results suggest that downside risk is priced in the cryptocurrency market at the cross-sectional level. Additionally, when we analyze the full sample data while controlling for entity and time effects, we also find a cross-period relationship (intertemporal relation) between downside risk and future returns.
Moreover, our additional research reveals that stock market sentiment and cryptocurrency attention have explanatory power for downside risk. Interestingly, the impact of cryptocurrency attention on downside risk differs in the short and medium to long term. We employ the attention theory proposed by Barber and Odean (2007) and the sentiment research findings of Baker and Wurgler (2006) to explain these results. We find that the sentiment hypothesis can explain the empirical findings in the medium to long term, while the attention theory is able to explain them consistently.
參考文獻 Al Guindy, M. (2021). Cryptocurrency price volatility and investor attention. International Review of Economics & Finance, 76, 556-570.
Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets, 5(1), 31-56.
Anamika, Chakraborty, M., & Subramaniam, S. (2023). Does Sentiment Impact Cryptocurrency? Journal of Behavioral Finance, 24(2), 202-218.
Ang, A., Chen, J., & Xing, Y. (2006). Downside Risk. The Review of Financial Studies, 19(4), 1191-1239.
Atilgan, Y., Bali, T. G., Demirtas, K. O., & Gunaydin, A. D. (2020). Left-tail momentum: Underreaction to bad news, costly arbitrage and equity returns. Journal of financial economics, 135(3), 725-753.
Bai, J., Bali, T. G., & Wen, Q. (2019). Common risk factors in the cross-section of corporate bond returns. Journal of financial economics, 131(3), 619-642.
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., Demirtas, K. O., & Levy, H. (2009). Is There an Intertemporal Relation between Downside Risk and Expected Returns? The Journal of Financial and Quantitative Analysis, 44(4), 883-909.
Barber, B. M., & Odean, T. (2007). All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors. The Review of Financial Studies, 21(2), 785-818.
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.
Borri, N. (2019). Conditional tail-risk in cryptocurrency markets. Journal of Empirical Finance, 50, 1-19.
Conlon, T., & McGee, R. J. (2020). Betting on Bitcoin: Does gambling volume on the blockchain explain Bitcoin price changes? Economics Letters, 191, 108727.
Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199.
Da, Z., Engelberg, J., & Gao, P. (2011). In Search of Attention. The Journal of Finance, 66(5), 1461-1499.
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.
Detzel, A., Liu, H., Strauss, J., Zhou, G., & Zhu, Y. (2021). Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard-to-value fundamentals. Financial Management, 50(1), 107-137.
Fama, E. F., & MacBeth, J. D. (1973). Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy, 81(3), 607-636.
Harvey, C. R., & Siddique, A. (2000). Conditional Skewness in Asset Pricing Tests. The Journal of Finance, 55(3), 1263-1295.
Li, Y., Urquhart, A., Wang, P., & Zhang, W. (2021). MAX momentum in cryptocurrency markets. International Review of Financial Analysis, 77, 101829.
Liu, Y., & Tsyvinski, A. (2020). 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.
Pelster, M., Breitmayer, B., & Hasso, T. (2019). Are cryptocurrency traders pioneers or just risk-seekers? Evidence from brokerage accounts. Economics Letters, 182, 98-100.
Zhang, W., & Li, Y. (2020). Is idiosyncratic volatility priced in cryptocurrency markets? Research in International Business and Finance, 54, 101252.
Zhang, W., & Li, Y. (2023). Liquidity risk and expected cryptocurrency returns. International Journal of Finance & Economics, 28(1), 472-492.
Zhang, W., Li, Y., Xiong, X., & Wang, P. (2021). Downside risk and the cross-section of cryptocurrency returns. Journal of Banking & Finance, 133, 106246.
描述 碩士
國立政治大學
財務管理學系
110357036
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110357036
資料類型 thesis
dc.contributor.advisor 岳夢蘭zh_TW
dc.contributor.advisor Yueh, Meng-Lanen_US
dc.contributor.author (Authors) 林冠宇zh_TW
dc.contributor.author (Authors) Lin, Kuan-Yuen_US
dc.creator (作者) 林冠宇zh_TW
dc.creator (作者) Lin, Kuan-Yuen_US
dc.date (日期) 2023en_US
dc.date.accessioned 2-Aug-2023 13:01:17 (UTC+8)-
dc.date.available 2-Aug-2023 13:01:17 (UTC+8)-
dc.date.issued (上傳時間) 2-Aug-2023 13:01:17 (UTC+8)-
dc.identifier (Other Identifiers) G0110357036en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146294-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 財務管理學系zh_TW
dc.description (描述) 110357036zh_TW
dc.description.abstract (摘要) 本文使用風險價值(Value at Risk)衡量個別加密貨幣的下行風險，並闡述了下行風險與橫斷面報酬的正向關係，在單變量投資組合分析、雙變量投資組合分析下的長短策略皆顯著為正，並且在使用三因子模型(Liu et al., 2022)進行風險調整後依然顯著為正。此外，本文使用Fama and MacBeth (1973)迴歸發現在個別貨幣的下行風險特徵與未來報酬具有顯著橫斷面關係。研究結果顯示橫斷面上，下行風險會被加密貨幣市場定價。另外，本文使用全樣本資料並控制貨幣、時間效果後，亦發現下行風險與未來報酬存在跨期關係(Intertemporal relation)。
本文的額外研究發現股市情緒、加密貨幣關注度對於下行風險具有解釋能力，並且加密貨幣關注度在短、中長期的影響方向相反。本文分別使用Barber and Odean (2007)的關注度理論、Baker and Wurgler (2006)的情緒研究結果進行解釋，並且發現情緒假說能在中長期解釋本文的實證結果，而關注度理論則皆能解釋。		zh_TW
dc.description.abstract (摘要) In this paper, we use Value at Risk as a measure to downside risk, and discuss the positive relationship between downside risk and cross-sectional returns. We find that in both univariate and bivariate portfolio analyses, long and short strategies exhibit a significant positive relationship. Even after adjusting for risk using the three-factor model(Liu et al., 2022), this positive relationship remains significant. Furthermore, employing the Fama and MacBeth (1973) regression, we discover a significant cross-sectional relationship between the downside risk characteristics of individual currencies and future returns. The research results suggest that downside risk is priced in the cryptocurrency market at the cross-sectional level. Additionally, when we analyze the full sample data while controlling for entity and time effects, we also find a cross-period relationship (intertemporal relation) between downside risk and future returns.
Moreover, our additional research reveals that stock market sentiment and cryptocurrency attention have explanatory power for downside risk. Interestingly, the impact of cryptocurrency attention on downside risk differs in the short and medium to long term. We employ the attention theory proposed by Barber and Odean (2007) and the sentiment research findings of Baker and Wurgler (2006) to explain these results. We find that the sentiment hypothesis can explain the empirical findings in the medium to long term, while the attention theory is able to explain them consistently.		en_US
dc.description.tableofcontents 第一章 緒論 1
第二章 文獻回顧 2
第三章 研究方法 4
第一節 樣本來源與篩選 4
第二節 變數計算 5
第四章 實證分析與結果 11
第一節 單變量投資組合分析 11
第二節 雙變量投資組合分析 13
第三節 橫截面分析 17
第四節 跨期相關性 19
第五節 下行風險與情緒 21
第六節 情緒延遲效果 26
第五章 結論與建議 29
參考文獻 29		zh_TW
dc.format.extent 1566199 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110357036en_US
dc.subject (關鍵詞) 加密貨幣zh_TW
dc.subject (關鍵詞) 下行風險zh_TW
dc.subject (關鍵詞) 情緒zh_TW
dc.subject (關鍵詞) 關注度zh_TW
dc.subject (關鍵詞) Cryptocyrrencyen_US
dc.subject (關鍵詞) Downside risken_US
dc.subject (關鍵詞) Sentimenten_US
dc.subject (關鍵詞) Attentionen_US
dc.title (題名) 下行風險是否能解釋橫斷面報酬：以加密貨幣市場為例zh_TW
dc.title (題名) Can downside risk explain the cross-section of cryptocurrency returns?en_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Al Guindy, M. (2021). Cryptocurrency price volatility and investor attention. International Review of Economics & Finance, 76, 556-570.
Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets, 5(1), 31-56.
Anamika, Chakraborty, M., & Subramaniam, S. (2023). Does Sentiment Impact Cryptocurrency? Journal of Behavioral Finance, 24(2), 202-218.
Ang, A., Chen, J., & Xing, Y. (2006). Downside Risk. The Review of Financial Studies, 19(4), 1191-1239.
Atilgan, Y., Bali, T. G., Demirtas, K. O., & Gunaydin, A. D. (2020). Left-tail momentum: Underreaction to bad news, costly arbitrage and equity returns. Journal of financial economics, 135(3), 725-753.
Bai, J., Bali, T. G., & Wen, Q. (2019). Common risk factors in the cross-section of corporate bond returns. Journal of financial economics, 131(3), 619-642.
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., Demirtas, K. O., & Levy, H. (2009). Is There an Intertemporal Relation between Downside Risk and Expected Returns? The Journal of Financial and Quantitative Analysis, 44(4), 883-909.
Barber, B. M., & Odean, T. (2007). All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors. The Review of Financial Studies, 21(2), 785-818.
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.
Borri, N. (2019). Conditional tail-risk in cryptocurrency markets. Journal of Empirical Finance, 50, 1-19.
Conlon, T., & McGee, R. J. (2020). Betting on Bitcoin: Does gambling volume on the blockchain explain Bitcoin price changes? Economics Letters, 191, 108727.
Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199.
Da, Z., Engelberg, J., & Gao, P. (2011). In Search of Attention. The Journal of Finance, 66(5), 1461-1499.
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.
Detzel, A., Liu, H., Strauss, J., Zhou, G., & Zhu, Y. (2021). Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard-to-value fundamentals. Financial Management, 50(1), 107-137.
Fama, E. F., & MacBeth, J. D. (1973). Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy, 81(3), 607-636.
Harvey, C. R., & Siddique, A. (2000). Conditional Skewness in Asset Pricing Tests. The Journal of Finance, 55(3), 1263-1295.
Li, Y., Urquhart, A., Wang, P., & Zhang, W. (2021). MAX momentum in cryptocurrency markets. International Review of Financial Analysis, 77, 101829.
Liu, Y., & Tsyvinski, A. (2020). 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.
Pelster, M., Breitmayer, B., & Hasso, T. (2019). Are cryptocurrency traders pioneers or just risk-seekers? Evidence from brokerage accounts. Economics Letters, 182, 98-100.
Zhang, W., & Li, Y. (2020). Is idiosyncratic volatility priced in cryptocurrency markets? Research in International Business and Finance, 54, 101252.
Zhang, W., & Li, Y. (2023). Liquidity risk and expected cryptocurrency returns. International Journal of Finance & Economics, 28(1), 472-492.
Zhang, W., Li, Y., Xiong, X., & Wang, P. (2021). Downside risk and the cross-section of cryptocurrency returns. Journal of Banking & Finance, 133, 106246.		zh_TW