(3.92.96.236) 您好!臺灣時間:2021/05/06 23:02
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

: 
twitterline
研究生:呂惠琪
研究生(外文):Huie-Chi Lu
論文名稱:選擇權內含資訊對股價指數波動率預測之績效表現-以臺指選擇權為例
論文名稱(外文):The performance of forecasting stock index return volatility from the information content of stock index options - an empirical research of TXO
指導教授:程言信程言信引用關係
指導教授(外文):Yen-Shin Cheng
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:金融資訊研究所
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:2009
畢業學年度:97
語文別:中文
論文頁數:59
中文關鍵詞:臺指選擇權隱含波動率臺指選擇權波動率指數波動率預測交易量選擇權交易量成交量效果誤差
外文關鍵詞:TXO(TAIEX Options)Implied VolatilityVIX of TXOVolatility ForecastTrading VolumeAND
相關次數:
  • 被引用被引用:3
  • 點閱點閱:2306
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:255
  • 收藏至我的研究室書目清單書目收藏:2
一般傳統都以歷史資料為基礎的時間序列模型來估計股價波動率,本文進一步將選擇權所隱含的資訊模型納入考量並進行比較分析,檢測各波動率模型的內含資訊及對未來股價真實波動的預測能力。實證顯示,隱含波動率與波動率指數提供相對於時間序列模型不同的資訊,並有額外的解釋能力;在不同的誤差衡量指標與迴歸分析結果發現,價平買權隱含波動率為最佳波動預測模型。此外,在時間序列模型中,股價指數成交量與未來真實波動有顯著的正向關係;隱含波動率與波動率指數模型中,除價平買權隱含波動率與選擇權成交量呈正向關係之外,其他模型加入選擇權交易量資訊則無法顯著提升其模型的預測能力。最後,本文以歷史波動、價平買權和賣權隱含波動率及波動率指數等模型評價樣本外價平選擇權。在買權及賣權的評價結果中,買權以迴歸式計算的波動率評價能力較佳,賣權則相反;分別又以迴歸價平買權隱含波動率和原始波動率指數的評價效果最好,顯示選擇權所隱含的資訊不僅對未來股價波動有較好的解釋能力,用以評價未來選擇權的價格也會得到較小的誤差,而內含資訊較豐富的模型其評價績效也相對較好。
In traditional, we use time series model calculated from past return information to estimate the stock index volatility. This paper, further, compares the information implied by stock index options with time series representations of stock return volatility, and examines the information content and the forecast ability to future realized volatility of all models. Our empirical results show that implied volatility and VIX models contain different information and provide incremental explanatory power from time series models. According to different error measurement indices and regression analysis, the implied volatility of ATM call option is the best volatility forecast model. Moreover, we add volume of underlying asset, volume and open interest of index options to the forecast model separately to examine whether trading volume information can improve the forecast performance. We find there is a significantly positive relationship between volume of underlying and future realized volatility in time series models. However, implied volatility and VIX models by adding volume and open interest of index options as exogenous variable respectively can not significantly improve forecast ability except adding option volume of ATM call. Finally, we use historical, implied volatility of ATM call and put, and VIX models to price the out-of-sample ATM options. In order to examine whether the linear relationship between forecasting models and realized volatility can improve option pricing, we differentiate original from regression volatility. In our pricing results, the regression volatility has better pricing ability for call options. On the contrary, the original type is superior to regression in pricing put option. In addiction, the regression implied volatility of ATM call and original VIX models have the best pricing performance while pricing call and put options separately, and show that using the trading information of options to price the future options may get less error. Besides, models contain relative high information content also having better pricing ability.
目錄

誌謝 I
中文摘要 II
英文摘要 III
目錄 IV
表目錄 VI

第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 3
第三節 研究限制 4
第二章 文獻探討 5
第一節 隱含波動率相關文獻 5
第二節 波動率指數相關文獻 8
第三節 交易量相關文獻 9
第三章 研究方法 11
第一節 波動率模型 11
第二節 波動率模型的簡單迴歸分析 21
第三節 波動率模型的複迴歸分析 24
第四節 樣本外選擇權評價 26
第四章 實證分析與結果 29
第一節 資料來源與選取 29
第二節 波動率模型的基本敘述統計量及時間序列走勢 30
第三節 波動率模型預測能力之比較分析 32
第四節 加入交易量資訊的波動率模型 34
第五節 波動率模型的相對預測能力之比較分析 37
第六節 樣本外選擇權評價 41
第五章 結論 46
參考文獻 48
附錄 51
參考文獻
1.莊益源、張鐘霖、王祝三(2003),波動率模型預測能力的比較-以台指選擇權為例, 台灣金融季刊,第四輯第二期,41-63。
2.Akgiray, V. (1989), “Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts,” Journal of Business, 62, 55-80.
3.Anthony, J. (1988), “The Interrelation of Stock and Options Market Trading-Volume Data,” Journal of Finance, 43, 949-964.
4.Beckers, S. (1981), “Standard Deviations Implied in Option Prices as Predictors of Future Stock Price Variability,” Journal of Banking and Finance, 5, 363-381.
5.Becker, R., A.E. Clements, and S.I. White (2006) , “On the Information Efficiency of S&P 500 Implied Volatility,” North American Journal of Economics and Finance, 17, 139-153.
6.Becker, R., A.E. Clements, and S.I. White (2007) , “Does Implied Volatility Provide Any Information beyond that Captured in Model-Based Volatility Forecasts? ,” Journal of Banking and Finance, 31, 2535-2549.
7.Bessembinder, H. and P. Seguin (1992), “Futures-Trading Activity and Stock Price Volatility,” Journal of Finance, 47, 2015-2034.
8.Black, F. and M. Scholes (1973), “The Pricing of Options and Corporate Liabilities,” Journal of Political Economy, 3, 637-659.
9.Blair, B.J., S-H. Poon, and S.J. Taylor (2001), “Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities and High Frequency Index Returns,” Journal of Econometrics, 105, 5-26.
10.Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroscedasticity,” Journal of Econometrics, 31, 307-327.
11.Canina, L. and S. Figlewski (1993), “The Informational Content of Implied Volatility,” Review of Financial Studies, 6, 659-681.
12.Carr, P. and L. Wu (2006), “A Tale of Two Indices,” The Journal of Derivatives, 13, 13-29.
13.Chiras, D.P. and S. Manaster (1978). “The Information Content of Option Prices and a Test of Market Efficiency,” Journal of Financial Economics, 6, 213-234.
14.Christensen, B.J. and N.R. Prabhala (1998), “The Relation between Implied and Realized Volatility,” Journal of Financial Economics, 50, 125-150.
15.Chu, S.H. and S. Freund (1996), “Volatility Estimation for Stock Index Option: A GARCH Approach”, Quarterly Review of Economics and Finance, 36, 431-450.
16.Cornell, B. (1981), “The Relationship between Volume and Price Variability in Futures Markets,” Journal of Futures Markets, 1, 303-316.
17.Corrado, C.J. and Miller, T.W. (2005), “The Forecast Quality of CBOE Implied Volatility Indexes,” The Journal of Futures Markets, 25, 339-373.
18.Cox, J.C., S.A. Ross, and M. Rubinstein (1979), “Option Pricing: A Simplified Approach,” Journal of Financial Economics, 7, 229-263.
19.Day, T.E. and C.M. Lewis (1992), “Stock Market Volatility and the Information Content of Stock Index Options,” Journal of Econometrics, 52, 267-287.
20.Demeterfi, K., E. Derman, M. Kamal, and J. Zou (1999), “More than You Ever Wanted to Know about Volatility Swap,” Quantitative Strategies Research Notes, Goldman Sachs.
21.Engle, R. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of UK Inflation,” Econometrica, 50, 987-1008.
22.Fair, R.C. and R.J. Shiller (1990), “Comparing Information in Forecasts from Econometric Models,” American Economic Review, 80, 375-389.
23.Fleming, J. and R.E. Whaley (1994), “The Value of Wildcard Options,” Journal of Finance, 49, 215-236.
24.Fleming, J. (1998), “The Quality of Market Volatility Forecasts Implied by S&P 100 Index Option Prices,” Journal of Empirical Finance, 5, 317-345.
25.Fung, Joseph K.W. (2007), “The Information Content of Option Implied Volatility Surrounding the 1997 Hong Kong Stock Market Crash,” Journal of Futures Markets, 27, 555-574
26.Giot, P. and S. Laurent (2007), “The Information Content of Implied Volatility in Light of the Jump/Continuous Decomposition of Realized Volatility,” Journal of Futures Markets, 27, 337-359.
27.Gwilym, O.A. and M. Buckle (1999), “Volatility Forecasting in the Framework of the Option Expiry Circle,” The European Journal of Finance, 5, 73-94.
28.Hansen, L. (1982), “Large Sample Properties of Generalized Method of Moments Estimators,” Econometrica, 50, 1029-1054.
29.Harvey, C.R. and R.E. Whaley (1991), “S&P 100 Index Option Volatility,” Journal of Finance, 46, 1551-1561.
30.Hull, J. and A. White (1987), “The Pricing of Options on Assets with Stochastic Volatility,” Journal of Finance, 42, 281-300.
31.Jorion, P. (1995), “Predicting Volatility in the Foreign Exchange Market,” Journal of Finance, 50, 507-528.
32.Karpoff, J. (1987), “The Relation between Price Changes and Trading Volume: A Survey,” Journal of Financial and Quantitative Analysis, 22, 109-123.
33.Lamoureux, C.G. and W.D. Lastrapes (1993), “Forecasting Stock Return Variance: Toward an Understanding of Stochastic Implied Volatilities,” Review of Financial Studies, 6, 293-326.
34.Latane, H. and R. Rendleman (1976), “Standard Deviation of Stock Price Ratios Implied in Option Prices,” Journal of Finance, 31, 369-382.
35.Nelson, D.B. (1991), “Conditional Heteroskedasticity in Asset Returns: A New Approach,” Econometrica, 59,347-370.
36.Ni, S.X., J. Pan, and A.M. Poteshman (2008), “Volatility Information Trading in the Option Market,” Journal of Finance, LXIII, 1059-1091.
37.Poon S.H. and Clive W.J. Granger (2003), “Forecasting Volatility in Financial Market:A Review,” Journal of Economic Literature, 41, 478-539.
38.Shu, J. and J.E. Zhang (2003), “The Relationship between Implied and Realized Volatility of S&P 500 Index,” Wilmott Magazine, January, 83-91.
39.Whaley, R.E. (1993), “Derivatives on Market Volatility:Hedging Tools Long Overdue,” Journal of Derivatives, 1, 71-84.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊
 
系統版面圖檔 系統版面圖檔