政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/49011

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/49011

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题名:	波動度預測與波動度交易—以台灣選擇權市場為實證 Forecasting volatility and volatility trading—evidence from Taiwan options market
作者:	林政聲
贡献者:	廖四郎林政聲
关键词:	波動度預測波動度交易 GARCH 隱含波動度選擇權與期貨組合跨式策略勒式策略
日期:	2009
上传时间:	2010-12-08 01:56:56 (UTC+8)
摘要:	本研究主要探討幾個廣受市場投資人所使用的波動度預測模型，如歷史波動度法、指數加權移動平均法、GARCH、EGARCH以及隱含波度，另外再考慮近年才被學者提出的RLS模型與A-RLS模型，一同比較它們對於台灣市場波動度的預估能力，並擇一最優者，作為從事波動度交易的訊號依據。本文在進行波動度交易之實證，主要是利用選擇權與期貨組合、選擇權與delta期貨組合、跨式交易策略與勒式交易策略等四種廣為波動度交易者使用之波動度交易策略，進而比較它們在樣本外的交易績效。本波動度預測的實證發現，樣本內的預測能力，是以GARCH和RLS模型最佳，而樣本外的預估能力，則是GARCH表現最好。另外，波動度交易的驗證結果顯示，若持有至次一交易日即平倉，勒式交易策略於買進波動度時會有最高的績效，而當放空波動度時，則是跨式交易策略會有最佳的表現。
參考文獻:	英文部分 [1] Alexander, C. (2008). “Pricing, Hedging, and Financial Instruments”, Market Risk Analysis,Vol. 3, John Wiley & Sons, Inc. . [2] Alexander, C. (2001). “Principles of the Skew”, Equity Risk Special Report, Jan. 2001, 14-17. [3] Bartels, H.J. and Lu, J. (1998). “Volatility Forecasting and Delta-Neural Volatility Trading for DTB Options on the DAX”. [4] Bollerslev, T. (1986). “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Financial Econometrics, 1981, 307-327. [5] Bollerslev, T., Litvinova, J. and Tauchen, G. (2006). “Leverage and Volatility Feedback Effects in High-Frequency Data”, Journal of Financial Econometrics, 2006, Vol. 4, No. 3, 353-384. [6] Chang, C.C., Hsieh, P.F. and Lai, H.N. (2010). “Information Content of Options Trading Volume for Future Volatility：Evidence From the Taiwan Options Market”, Journal of Banking & Finance, 2010, Vol. 34, 174-183. [7] Chang, C.C., Hsieh, P.F. and Lai, H.N. (2009). “Do Informed Option Investors Predict Stock Returns？ Evidence From the Taiwan Stock Exchange”, Journal of Banking & Finance, 2009, Vol. 33, 757-764. [8] Chaput, J.S. and Ederington, L.H. (2004). “Volatility Trade Design”, The Journal of Futures Markets, 2005, Vol. 25, No.3, 243-279. [9] Carr, P. and Madan, D. (2001). “Towards a theory of Volatility Trading”, Handbooks in Mathematical Finance：Option Pricing, Interest Rates and Risk Mangement, eds. Cvitanic, J., Jouini, E. and Musiela, M., Cambridge University Press, 2001, 458-476. [10] Christensen, B.J. and Prabhala, N.R. (1998). “The Relation Between Implied and Realized Volatility”, Journal of Financial Econometrics, 1998, Vol. 51, 125-150. [11] Chung, S. L. and Shackleton, M.K. (2005). “On The Errors and Comparison of Vega Estimation Methods”, The Journal of Futures Markets, 2005, Vol. 25, No. 1, 21-38. [12] Dawson, P. and Staikouras, S. (2009). “The Impact of Volatility Derivatives on S&P 500 Volatility”, The Journal of Futures Markets, 2009, Vol. 29, No. 2, 1190-1213. [13] Ederington, L.H. and Guan W. (2005). “Forecasting Volatility”, The Journal of Futures Markets, 2005, Vol. 25, No. 5, 465-490. [14] Figlewski, S. (1989). “Options Arbitrage in Imperfect Markets”, The Journal of Finance, Dec. 1989, Vol. 44, No. 5, 1289-1311. [15] Giot, P. (2005). “Relationships Between Implied Volatility Indexes and Stock Index Returns”, The Journal of Portfolio Management, Spring 2005, 92-100. [16] Hung, C.H., Tzang, S.W. and Hsyu, S.D. (2009). “Forecasting Efficiency of Implied Volatility and the Intraday High-Low Price Ranger in Taiwan Stock Market”, International Research Journal of Financial and Economics, 2009, Issue 27, 192-202. [17] Lai, H.L. (2009). “A Study of Volatility Trading Strategies：Evidence From Taiwan Index Options”, Working Paper. [18] Ni, S.X., Pan, J. and Poteshman, A.M. (2006). “Volatility Information Trading in the Option Market”, The Journal of Financial, June 2006, Vol. 63, No. 3, 1059-1091. [19] Poon, S.H. and Granger, C.W.J. (2003). “Forecasting Volatility in Financial Markets：A Review”, Journal of Economic Literature, June 2003, Vol. 41, 478-539. [20] Sinclair, E. (2008). “Volatility Trading”, John Wiley & Sons, Inc., 2008. [21] Wang, Y.H., Keswani, A. and Taylor, S.J. (2006). “The Relationships Between Sentiment, Returns and Volatility”, International Journal of Forecasting, 2006, Vol. 22, 109-123. 中文部分 [1] 江木偉 (2004). “台指選擇權隱含波動率指標之資訊內涵－新編VIX指標之實證”。 [2] 伍尚文 (2005). “台指選擇權及期貨操作之研究－運用買賣權結構比例即週技術指標”。 [3] 李茂華 (2009). “運用基因演算法於建立最佳隱含波動率估計值之研究－以台指選擇權為例”。 [4] 陳仕庭 (2007). “香港恆生指數未來真實波動度之預測－多因子波動度模型與門檻模型之應用”。 [5] 莊益源、張鍾霖與王祝三 (2003). “波動度模型預測能力的比較－以台指選擇權為例”。 [6] 張富達 (2003). “國內選擇權市場波動率指數(VIX)之建構與分析”。 [7] 盧佳鈺 (2003). “台指選擇權隱含波動率指標之資訊內涵”。
描述:	碩士國立政治大學金融研究所 97352018 98
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0097352018
数据类型:	thesis
显示于类别:	[金融學系] 學位論文

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