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研究生:許溥淳
研究生(外文):Pu-Chun Hsu
論文名稱:低波動度效果與市場預警之指標
論文名稱(外文):Low Volatility Effect and Early Warning Indicator for Stock Market
指導教授:吳庭斌吳庭斌引用關係
指導教授(外文):Ting‑Pin Wu
學位類別:碩士
校院名稱:國立中央大學
系所名稱:財務金融學系
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:58
中文關鍵詞:低波動度效果避風港效應市場預警
外文關鍵詞:low volatility effectlow volatility anomalysafe haven flowsearly warning for stock market
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過去的財務理論中,風險與報酬之間存在抵換關係,當投資人承擔較高的風險時,必能賺取較高的報酬,反之如此;然而,Ang, Hodrick, Xing, and Zhang (2006) 發現,獨有風險較高的股票,反而擁有較低的預期報酬,此現象稱為「低波動度效果」。本文試圖以 Kaul and Sapp (2006) 所提之避風港效應 (save haven flows) 解釋低波動度效果,並藉由此效果建構市場預警之指標。

實證結果顯示,在景氣緊縮時期是確實存在低波動度效果,雖然低波動度效果與任一市場風險之指標沒有一個明顯的領先、落後或同步的關係,但是,同時也突顯出投資人行為與市場指標之間的複雜關係;本文嘗試建構低波動度效果之指標的布林通道,並搭配市場大盤走勢,藉由觀察這些原始資料,發現在重大事件發生之前,低波動度效果之指標具有不錯的預警能力,然而,因為在其他時期有發生誤報的情況,本文利用觀察歷史數據訂定臨界值,結果證明,布林通道搭配臨界值能有效排除錯誤的警報,進而提升該預警指標的準確率。
In the traditional financial theory, there is a trade-off relation between risks and returns. When investors take higher risks, they will earn more money, and vice versa. However, Ang, Hodrick, Xing, and Zhang (2006) found that stocks with higher idiosyncratic risk have lower expected return instead. This phenomenon is called as low volatility effect or low volatility anomaly. This paper tries to explain this phenomenon by save haven flows proposed by Kaul and Sapp (2006) and build an indicator with early warning ability for stock market.

The result shows that there is indeed a low volatility effect during recessions. Although low volatility effect doesn’t have a specific leading, lagging or coincident relation with any indicators of market risk, the complicated association between investors’ behavior and indicators of market risk is highlighted. We use indicators of low volatility effect to build Bollinger bands. With the Bollinger band and S&P 500 historical chart, we find out that the indicator of low volatility effect has great ability of early warning before a crisis happened. Although there are some false alarms in other time, we use a threshold to screen out those false alarms. The result proves that the threshold is helpful for eliminating those false alarms and improves the accuracy of the early warning indicator.
摘要 i
Abstract ii
致謝 iii
目錄 iv
表目錄 v
圖目錄 vi
一、 緒論 1
二、 資料與研究方法 5
2-1 資料選取 5
2-2 研究方法 5
三、 研究結果 8
3-1 檢驗低波動度效果 8
3-2 低波動度效果與市場風險的關係 18
3-3 低波動度效果之指標與市場大盤走勢的關係 26
四、 結論 36
參考文獻 38
附錄一 40
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.
Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2009). High idiosyncratic volatility and low returns: International and further U.S. evidence. Journal of Financial Economics, 91(1), 1-23.
Baker, M., Bradley, B., & Wurgler, J. (2011). Benchmarks as limits to arbitrage: Understanding the low-volatility anomaly. Financial Analysts Journal, 67(1), 40-54.
Baker, N. L., & Haugen, R. A. (2012). Low risk stocks outperform within all observable markets of the world. Working Paper, .
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.
Blitz, D. C., & van Vliet, P. (2007). The Volatility Effect. The Journal of Portfolio Management, 34(1), 102-113.
Blitz, D., Pang, J., & Van Vliet, P. (2013). The volatility effect in emerging markets. Emerging Markets Review, 16, 31-45.
Clarke, R. G., De Silva, H., & Thorley, S. (2006). Minimum-variance portfolios in the US equity market. The Journal of Portfolio Management, 33(1), 10-24.
Clarke, R. G., De Silva, H., & Thorley, S. (2010). Know your VMS exposure. The Journal of Portfolio Management, 36(2), 52-59.
Dutt, T., & Humphery-Jenner, M. (2013). Stock return volatility, operating performance and stock returns: International evidence on drivers of the ‘low volatility’ anomaly. Journal of Banking & Finance, 37(3), 999-1017.
Frazzini, A., & Pedersen, L. H. (2014). Betting against beta. Journal of Financial Economics, 111(1), 1-25.
Fu, F. (2009). Idiosyncratic risk and the cross-section of expected stock returns. Journal of Financial Economics, 91(1), 24-37.
Garcia-Feijóo, L., Kochard, L., Sullivan, R. N., & Wang, P. (2015). Low-Volatility Cycles: The Influence of Valuation and Momentum on Low-Volatility Portfolios. Financial Analysts Journal, 71(3), 47-60.
Guo, H., & Savickas, R. (2010). Relation between time-series and cross-sectional effects of idiosyncratic variance on stock returns. Journal of Banking & Finance, 34(7), 1637-1649.
Haugen, R. A., & Heins, A. J. (1975). Risk and the rate of return on financial assets: Some old wine in new bottles. Journal of Financial and Quantitative Analysis, 10(5), 775-784.
Huang, W., Liu, Q., Rhee, S. G., & Zhang, L. (2009). Return reversals, idiosyncratic risk, and expected returns. The Review of Financial Studies, 23(1), 147-168.
Kaul, A., & Sapp, S. (2006). Y2K fears and safe haven trading of the US dollar. Journal of international money and finance, 25(5), 760-779.
Malkiel, B. G., & Xu, Y. (2002). Idiosyncratic risk and security returns. University of Texas at Dallas (November 2002).
Spiegel, M. I., & Wang, X. (2005). Cross-sectional variation in stock returns: Liquidity and idiosyncratic risk. Yale ICF Working Paper, 05-13.
吳冠緯,「波動異常現象及其預測能力」,國立中央大學,碩士論文,民國106年6月。
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