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姓名 陳柏諺(Po-Yen Chen)  查詢紙本館藏   畢業系所 產業經濟研究所
論文名稱 股票報酬率波動度之記憶性質及其定價效果
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摘要(中) 本研究探討台灣市場中,股票報酬率波動度之記憶性質。基於資本資產定價模型隱含資產期望報酬率與風險之間存在正向關係,然而資產的風險與波動度持續性具有關聯性,亦即波動度持續越久,其風險越小。因此,波動度持續性較短的資產相較於波動持續性較長的資產,應有較高的期望報酬率。本文實證結果顯示,記憶程度較低(波動度持續較短)的股票相較於記憶程度較高(波動持續較長)的股票,多出3.1016%的股票月超額報酬率。此外,本文進一步探討股票報酬率波動度的記憶程度與公司特徵之關聯性,以及其分別對股票超額報酬率與已實現波動率之影響。本研究的主要結果與Nguyen et al. (2020)在美國市場中的研究結果具一致性。
摘要(英) We examine long memory volatility in the cross-section of stock daily returns. We show that long memory volatility is widespread in the Taiwan market and that the degree of memory can be related to firm characteristics, such as market capitalization, book-to-market ratio, prior performance, and price jumps.
Based on the capital asset pricing model (CAPM), there is a positive relationship between the expected return on assets and the risk. Therefore, assets with shorter volatility persistence should generate higher expected return than assets with longer volatility persistence. The empirical result shows that stocks with lower memory generates significant excess returns of 3.1016% per month. This result is consistent with that of Nguyen et al. (2020).
關鍵字(中) ★ 長記憶性
★ 廠商特徵
★ 資本資產定價模型
★ 已實現波動率
★ GPH
關鍵字(英)
論文目次 摘要 i
Abatract ii
謝辭 iii
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的及研究範圍 2
第三節 研究架構 4
第二章 文獻回顧 5
第一節 各國金融市場中存在長記憶性文獻 5
第二節 記憶參數與公司特徵相關聯文獻 6
第三章 研究資料來源、變數說明與模型設定 10
第一節 樣本與資料來源 10
第二節 變數定義以及說明 10
第三節 研究方法與模型 17
第四章 實證結果與分析 20
第一節 股票報酬率波動度之記憶性與公司特徵之關聯 20
第二節 股票報酬率波動度之記憶性與股票超額報酬率之關聯性 26
第三節 股票報酬率波動度之記憶性對股票報酬率之影響 29
第四節 股票報酬率波動度記憶性對已實現波動率預測性之分析 33
第五章 結論與建議 35
一、研究結論 35
二、研究限制與建議 35
附錄 37
單一產業實證結果: 以電子業為例 37
參考文獻 42
參考文獻 [1] Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets 5, 31-56.
[2] Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross‐section of volatility and expected returns. Journal of Finance 61(1), 259-299.
[3] Areal, N. M., & Taylor, S. J. (2002). The realized volatility of FTSE‐100 futures prices. Journal of Futures Markets 22(7), 627-648.
[4] Barndorff-Nielsen, O. E., Hansen, P. R., Lunde, A., & Shephard, N. (2009). Realized kernels in practice: trades and quotes. Econometrics Journal 12, 1-33.
[5] Barndorff-Nielsen, O. E., & Shephard, N. (2006). Econometrics of testing for jumps in financial economics using bipower variation. Journal of Financial Econometrics 4, 1-30.
[6] Becker, J., Hollstein, F., Prokopczuk, M., & Sibbertsen, P. (2020). The Memory of Beta Factors. Mimeo, Leibniz University Hannover, Germany.
[7] Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31(3), 307-327.
[8] Cheung, Y.-W. & Lai, K. S. (1995). A search for long memory in international stock market returns. Journal of International Money and Finance 14(4), 597-615.
[9] Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7, 174-196.
[10] Cremers, M., Halling, M., & Weinbaum, D. (2015). Aggregate jump and volatility risk in the cross-section of stock returns. Journal of Finance 70, 577-614.
[11] Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. Journal of Finance 47, 427-465.
[12] Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3-56.
[13] Fama, E. F., & French, K. R. (2008). Dissecting anomalies. Journal of Finance 63, 1653-1678.
[14] Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics 116, 1-22.
[15] Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: empirical tests. Journal of Political Economy 81, 607-636.
[16] Geweke, J., & Porter-Hudak, S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis 4, 221-238.
[17] Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. Journal of Finance 45, 881-898.
[18] Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance 48, 65-91.
[19] Jiang, G. J., & Oomen, R. C. (2008). Testing for jumps when asset prices are observed with noise: a swap variance approach. Journal of Econometrics 144, 352-370.
[20] Jiang, G. J., & Yao, T. (2013). Stock price jumps and cross-sectional return predictability. Journal of Financial and Quantitative Analysis 48, 1519-1544.
[21] Jubinski, D. (2005). Observable firm characteristics and individual equity volatility persistence. Mimeo, Saint Joseph’s University, United States.
[22] Kasman, A., Kasman, S., & Torun, E. (2009). Dual long memory property in returns and volatility: Evidence from the CEE countries′ stock markets. Emerging Markets Review 10, 122-139.
[23] Kelly, B., & Jiang, H. (2014). Tail risk and asset prices. Review of Financial Studies 27, 2841-2871.
[24] Lobato, I. N., & Savin, N. E. (1998). Real and spurious long-memory properties of stock-market data. Journal of Business & Economic Statistics 16, 261-268.
[25] Mandelbrot, B. (1967). The variation of some other speculative prices. Journal of Business 40, 393-413.
[26] Nguyen, D. B. B., Prokopczuk, M., & Sibbertsen, P. (2020). The memory of stock return volatility: Asset pricing implications. Journal of Financial Markets 47, 100487.
[27] Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. Journal of Business 53, 61-65.
[28] Sadique, S., & Silvapulle, P. (2001). Long‐term memory in stock market returns: International evidence. International Journal of Finance & Economics 6, 59-67.
[29] Turkyilmaz, S. & Balibey, M. (2014). Long memory behavior in the returns of Pakistan stock market: ARFIMA-FIGARCH models. International Journal of Economics and Financial Issues 4, 400.
[30] 龐淑娟、劉向麗與汪壽陽 (2011),「中國期貨市場高頻波動率的長記憶性」,系統工程理論與實踐,31,1039-1044。
[31] 施紅俊、馬玉林與陳偉忠 (2004),「中國股市長記憶性實証研究」,同濟大學學報(自然科學版),32,416-420.
[32] 陳鈺雯 (2011),Firm Attributes and Long Memory in Volatility,中央大學財務金融學系碩士學位論文。
[33] 葉宗穎 (2000),國際資本資產定價模型:多變量FIGARCH-in-Mean模型的應用,國立臺灣大學經濟學系碩士學位論文。
指導教授 蔡明宏 劉錦龍 審核日期 2021-7-27
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