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題名 特質性波動率於加密貨幣市場中之風險探討
Idiosyncratic Volatility Risk in the Cryptocurrency Market
作者 童思謙
Tung, Szu-Chien
貢獻者 岳夢蘭
Yueh, Meng-Lan
童思謙
Tung, Szu-Chien
關鍵詞 加密貨幣
異質性波動
樂透型偏好
Cryptocurrency
Idiosyncratic Volatility
Lottery-like Preference
日期 2025
上傳時間 4-Aug-2025 14:08:24 (UTC+8)
摘要 受到 Ang et al. (2006)的啟發，本文旨在探討異質性波動(Idiosyncratic Volatility, IVOL)於加密貨幣市場中對橫斷面報酬的影響。本文採用單變量投資組合分析、雙變量投資組合分析以及Fama-MacBeth橫斷面迴歸等多元實證方法進行檢驗。實證結果發現，IVOL較高的加密貨幣具有顯著偏低的未來報酬，IVOL在加密貨幣市場展現出的定價效應與Ang et al. (2006)在美國股市中的發現相同。 此外，本文進一步研究MAX(一個月內前五大單日報酬的平均值)，發現MAX變數同樣會對加密貨幣報酬造成負向影響，且MAX與IVOL高度相關。將MAX納入橫斷面迴歸後，IVOL與未來報酬的負向關係不再顯著，此結果在控制同樣為極端報酬的 MIN(一個月內最小單日報酬)後依然穩健。最終，當以 IVOL 對MAX迴歸的殘差項作為分析變數時，亦無統計顯著影響。 本文研究結果指出，IVOL的定價效應係由MAX所驅動，異質性波動在加密貨幣市場的定價效應可由Bali et al. (2011)提出的樂透型偏好理論所解釋。
Motivated by Ang et al. (2006), this study aims to investigate the impact of idiosyncratic volatility (IVOL) on the cross-sectional returns in the cryptocurrency market. Using a combination of univariate portfolio analysis, bivariate portfolio analysis, and Fama-MacBeth cross-sectional regressions, we find that cryptocurrencies with higher IVOL tend to have significantly lower future returns, the pricing effect of IVOL in the cryptocurrency market is consistent with Ang et al. (2006) findings in the U.S. stock market. Furthermore, this paper explores the role of MAX (the maximum daily return) and finds that MAX also has a negative impact on cryptocurrency returns and is highly correlated with IVOL. After including MAX in the cross-sectional regressions, the previously observed negative relationship between IVOL and future returns disappears, and this result remains robust even when controlling for MIN (the minimum daily return), which is another measure of extreme returns. Finally, when using the residuals from the regression of IVOL on MAX as an explanatory variable, we also find no statistically significant effect. These results suggest that the pricing effect of IVOL in the cryptocurrency market is driven by MAX, and the apparent anomaly in IVOL can be explained by the lottery like preference theory proposed by Bali et al. (2011).
參考文獻 Ahmed, M. S., El-Masry, A. A., Al-Maghyereh, A. I., & Kumar, S. (2024). Cryptocurrency Volatility: A Review, Synthesis, and Research Agenda. Research in International Business and Finance, 102472. Almeida, J., & Gonçalves, T. C. (2023). A Systematic Literature Review of Investor Behavior in the Cryptocurrency Markets. Journal of Behavioral and Experimental Finance, 37, 100785. 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. Bali, T. G., Cakici, N., & Whitelaw, R. F. (2011). Maxing Out: Stocks as Lotteries and the Cross-Section of Expected Returns. Journal of Financial Economics, 99(2), 427-446. Bali, T. G., Demirtas, K. O., & Levy, H. (2009). Is There an Intertemporal Relation between Downside Risk and Expected Returns? Journal of Financial and Quantitative Analysis, 44(4), 883-909. Barberis, N., & Huang, M. (2008). Stocks as Lotteries: The Implications of Probability Weighting for Security Prices. American Economic Review, 98(5), 2066-2100. 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. Brunnermeier, M. K., Gollier, C., & Parker, J. A. (2007). Optimal Beliefs, Asset Prices, and the Preference for Skewed Returns. American Economic Review, 97(2), 159-165. Fama, E. F., & French, K. R. (1992). The Cross‐Section of Expected Stock Returns. The Journal of Finance, 47(2), 427-465. 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. Fama, E. F., & French, K. R. (2015). A Five-Factor Asset Pricing Model. Journal of Financial Economics, 116(1), 1-22. Fama, E. F., & MacBeth, J. D. (1973). Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy, 81(3), 607-636. Grobys, K., & Junttila, J. (2021). Speculation and Lottery-Like Demand in Cryptocurrency Markets. Journal of International Financial Markets, Institutions and Money, 71, 101289. Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics. McGraw-hill. Lammer, D. M., Hanspal, T., & Hackethal, A. (2020). Who Are the Bitcoin Investors? Evidence from Indirect Cryptocurrency Investments. Lintner, J. (1965). Security Prices, Risk, and Maximal Gains from Diversification. The Journal of Finance, 20(4), 587-615. 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. Mossin, J. (1966). Equilibrium in a Capital Asset Market. Econometrica: Journal of the Econometric Society, 768-783. 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. Tversky, A., & Kahneman, D. (1992). Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty, 5, 297-323. Urquhart, A. (2016). The Inefficiency of Bitcoin. Economics Letters, 148, 80-82. 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
112357038
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112357038
資料類型 thesis
dc.contributor.advisor 岳夢蘭zh_TW
dc.contributor.advisor Yueh, Meng-Lanen_US
dc.contributor.author (Authors) 童思謙zh_TW
dc.contributor.author (Authors) Tung, Szu-Chienen_US
dc.creator (作者) 童思謙zh_TW
dc.creator (作者) Tung, Szu-Chienen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 14:08:24 (UTC+8)-
dc.date.available 4-Aug-2025 14:08:24 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 14:08:24 (UTC+8)-
dc.identifier (Other Identifiers) G0112357038en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158511-
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) 112357038zh_TW
dc.description.abstract (摘要) 受到 Ang et al. (2006)的啟發，本文旨在探討異質性波動(Idiosyncratic Volatility, IVOL)於加密貨幣市場中對橫斷面報酬的影響。本文採用單變量投資組合分析、雙變量投資組合分析以及Fama-MacBeth橫斷面迴歸等多元實證方法進行檢驗。實證結果發現，IVOL較高的加密貨幣具有顯著偏低的未來報酬，IVOL在加密貨幣市場展現出的定價效應與Ang et al. (2006)在美國股市中的發現相同。 此外，本文進一步研究MAX(一個月內前五大單日報酬的平均值)，發現MAX變數同樣會對加密貨幣報酬造成負向影響，且MAX與IVOL高度相關。將MAX納入橫斷面迴歸後，IVOL與未來報酬的負向關係不再顯著，此結果在控制同樣為極端報酬的 MIN(一個月內最小單日報酬)後依然穩健。最終，當以 IVOL 對MAX迴歸的殘差項作為分析變數時，亦無統計顯著影響。 本文研究結果指出，IVOL的定價效應係由MAX所驅動，異質性波動在加密貨幣市場的定價效應可由Bali et al. (2011)提出的樂透型偏好理論所解釋。zh_TW
dc.description.abstract (摘要) Motivated by Ang et al. (2006), this study aims to investigate the impact of idiosyncratic volatility (IVOL) on the cross-sectional returns in the cryptocurrency market. Using a combination of univariate portfolio analysis, bivariate portfolio analysis, and Fama-MacBeth cross-sectional regressions, we find that cryptocurrencies with higher IVOL tend to have significantly lower future returns, the pricing effect of IVOL in the cryptocurrency market is consistent with Ang et al. (2006) findings in the U.S. stock market. Furthermore, this paper explores the role of MAX (the maximum daily return) and finds that MAX also has a negative impact on cryptocurrency returns and is highly correlated with IVOL. After including MAX in the cross-sectional regressions, the previously observed negative relationship between IVOL and future returns disappears, and this result remains robust even when controlling for MIN (the minimum daily return), which is another measure of extreme returns. Finally, when using the residuals from the regression of IVOL on MAX as an explanatory variable, we also find no statistically significant effect. These results suggest that the pricing effect of IVOL in the cryptocurrency market is driven by MAX, and the apparent anomaly in IVOL can be explained by the lottery like preference theory proposed by Bali et al. (2011).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 5 第一節 樣本來源與篩選 5 第二節 變數定義與計算 6 第四章 實證分析 12 第一節 單變量投資組合分析 12 第二節 雙變量投資組合分析 15 第三節 FamaMacBeth迴歸分析 19 第四節 穩健性檢定 23 第五節 樂透型需求假說 25 第六節 異質性風險與極端報酬 29 第五章 結論 35 參考文獻 36zh_TW
dc.format.extent 1929281 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112357038en_US
dc.subject (關鍵詞) 加密貨幣zh_TW
dc.subject (關鍵詞) 異質性波動zh_TW
dc.subject (關鍵詞) 樂透型偏好zh_TW
dc.subject (關鍵詞) Cryptocurrencyen_US
dc.subject (關鍵詞) Idiosyncratic Volatilityen_US
dc.subject (關鍵詞) Lottery-like Preferenceen_US
dc.title (題名) 特質性波動率於加密貨幣市場中之風險探討zh_TW
dc.title (題名) Idiosyncratic Volatility Risk in the Cryptocurrency Marketen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Ahmed, M. S., El-Masry, A. A., Al-Maghyereh, A. I., & Kumar, S. (2024). Cryptocurrency Volatility: A Review, Synthesis, and Research Agenda. Research in International Business and Finance, 102472. Almeida, J., & Gonçalves, T. C. (2023). A Systematic Literature Review of Investor Behavior in the Cryptocurrency Markets. Journal of Behavioral and Experimental Finance, 37, 100785. 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. Bali, T. G., Cakici, N., & Whitelaw, R. F. (2011). Maxing Out: Stocks as Lotteries and the Cross-Section of Expected Returns. Journal of Financial Economics, 99(2), 427-446. Bali, T. G., Demirtas, K. O., & Levy, H. (2009). Is There an Intertemporal Relation between Downside Risk and Expected Returns? Journal of Financial and Quantitative Analysis, 44(4), 883-909. Barberis, N., & Huang, M. (2008). Stocks as Lotteries: The Implications of Probability Weighting for Security Prices. American Economic Review, 98(5), 2066-2100. 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. Brunnermeier, M. K., Gollier, C., & Parker, J. A. (2007). Optimal Beliefs, Asset Prices, and the Preference for Skewed Returns. American Economic Review, 97(2), 159-165. Fama, E. F., & French, K. R. (1992). The Cross‐Section of Expected Stock Returns. The Journal of Finance, 47(2), 427-465. 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. Fama, E. F., & French, K. R. (2015). A Five-Factor Asset Pricing Model. Journal of Financial Economics, 116(1), 1-22. Fama, E. F., & MacBeth, J. D. (1973). Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy, 81(3), 607-636. Grobys, K., & Junttila, J. (2021). Speculation and Lottery-Like Demand in Cryptocurrency Markets. Journal of International Financial Markets, Institutions and Money, 71, 101289. Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics. McGraw-hill. Lammer, D. M., Hanspal, T., & Hackethal, A. (2020). Who Are the Bitcoin Investors? Evidence from Indirect Cryptocurrency Investments. Lintner, J. (1965). Security Prices, Risk, and Maximal Gains from Diversification. The Journal of Finance, 20(4), 587-615. 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. Mossin, J. (1966). Equilibrium in a Capital Asset Market. Econometrica: Journal of the Econometric Society, 768-783. 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. Tversky, A., & Kahneman, D. (1992). Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty, 5, 297-323. Urquhart, A. (2016). The Inefficiency of Bitcoin. Economics Letters, 148, 80-82. 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