跳到主要內容

臺灣博碩士論文加值系統

(35.175.191.36) 您好!臺灣時間:2021/07/31 02:06
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

我願授權國圖
: 
twitterline
研究生:林育民
研究生(外文):Yu-Ming Lin
論文名稱:無模型設定隱含波動度於風險值之應用:台指選擇權之實證分析
論文名稱(外文):Application of the Model-Free Implied Volatility to Value at Risk: Evidence from the TAIEX Options
指導教授:陳煒朋陳煒朋引用關係
指導教授(外文):Wei-Peng.Chen
學位類別:碩士
校院名稱:世新大學
系所名稱:財務金融學研究所(含碩專班)
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:45
中文關鍵詞:無模型設定隱含波動度風險值
外文關鍵詞:Model-Free implied volatilityValue at Risk
相關次數:
  • 被引用被引用:0
  • 點閱點閱:140
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
波動度的估計一直都是學界與實務界研究的重點之一,但是至今仍沒有一個估計模型能被各界所完全接受。Britten-Jonse and Neuberger(2000)推導出無模型設定隱含波動度模型(Model-Free implied volatility model),此模型特點為它沒有根據任何特定的選擇權訂價模型,完全以無套利模型的基礎出發,其模型說明未來標的資產的報酬變異,完全取決於期間內的選擇權價格集合決定。Jiang and Tian(2005)則將上述波動度估計模型進一步的推廣到在標的資產價格服從的是跳躍-擴散過程(jump-diffusion process)假設下的無模型設定隱含波動度,並用S&P 500的指數選擇權作實證研究,發現無模型設定隱含波動度能有較其它波動度更好的訊息包含力,且對未來真實波動度的預測上有較佳的結果。
本文參考Jiang and Tian(2005)並利用台指選擇權在台灣市場中做應用,去估計無模型設定隱含波動度。在估計出無模型設定隱含波動度後,運用於風險值模型,去實際探討此波動度在台灣市場中的表現。本研究主要參考Hull & White(1998)所提出的修正歷史模擬法,結合無模型設定隱含波動度去做實證研究,結果發現此波動度在風險值上的結果皆與預期失敗比率無顯著差異,表示無模型設定隱含波動度是可以應運於風險值上的一個波動度估計方法。
There is a lot of research on the forcasting ability and information content of volatility. People always try to find the best one. Britten-Jones and Neuberger(2000) derived the model-free implied volatility under the assumption that the price of underlying asset follows diffusion process. Unlike the traditional concept of implied volatility, their model-free implied volatility is not based on any specific option pricing model. Instead, it is derived entirely from no-arbitrage conditions. In particular, Britten-Jones and Neuberger (2000) showed that the risk-neutral integrated return variance between the current date and a future date is fully specified by the set of prices of options expiring on the future date. Jiang and Tian(2005) further extend the above model-free implied volatility to asset price process with jumps and develop a simple method for implementing the model-free implied volatility to transfer the formula to a computing instrument using European option prices on the market, and use the model with Standard and Poor’s 500 index options, the result suggest that the model-free implied volatility is a better volatility for forecasting future realized volatility and information content. According to Jiang and Tian(2005), the empirical study in this paper use the data of TXO to compute the model-free implied volatility to use in VaR (Value at Risk) model. No matter before or after the cut of tax, we found that model-free implied volatility is a good input in VaR model.
第一章 緒論- 7 -
第一節 研究動機與背景 - 7 -
第二節 研究目的 - 8 -
第三節 研究架構 - 10 -
第二章 文獻探討 - 11 -
第一節 波動度文獻 - 11 -
第二節 風險值(VALUE AT RISK) - 17 -
第三章 研究方法 - 23 -
第一節 無模型設定隱含波動度 - 23 -
第二節 其它波動度模型 - 27 -
第三節 風險值模型(VAR MODEL) - 31 -
第四節 VAR檢定模型 - 32 -
第四章 實證結果與分析 - 34 -
第一節 實證資料 - 34 -
第二節 實證結果 - 36 -
第五章 結論與建議 - 40 -
第一節 研究結論 - 40 -
第二節 研究限制與建議 - 41 -
參考文獻 - 42 -
一、國外文獻

1.Alexander, C. O., & Liegh, C. T. 1997. On the covariance matrices used in value at risk models, The Journal of Derivatives, 4(3), 50-62.

2.A��t-Sahalia, Y., and A. W. Lo, 1998, “Nonparametric Estimation of State-price
Densities Implicit in Financial Asset Prices,” Journal of Finance, 53, 499–547.

3.Bates, D., 1991, “The Crash of ’87: Was it Expected? The Evidence from
Options Markets,” Journal of Finance, 46, 1009–1044.

4.Beckers, S.,1981, “Standard Deviations Implied In Option Prices as Predictors of
Futures Stock Price Variability”, Journal of Banking and Finance, Vol. 5,
363-382.

5.Beder, Tanya Styblo 1995. ”VAR:Seductive but Dangerous.” Financial Analysis Journal, September-October

6.Black, F., and M. Scholes, 1973, “The Pricing of Options and Corporate
Liabilities,” Journal of Political Economy, 81, 637–659

7. Blair, B. J., S. Poon, and S. J. Taylor, 2001, “Forecasting S&P100 Volatility: The Incremental Information Content of Implied Volatilities and High-frequency
Index Retruns,” Journal of Econometrics, 105, 5-26.

8.Bollerslev, Tim, 1986, Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics , April, vol 31,pp307-327.

9.Britten-Jones, M., and A. Neuberger, 2000, “Option Prices, Implied Price
Processes, and Stochastic Volatility,” Journal of Finance, 55, 839–866.

10.Campa, J. M., K. P. Chang, and R. L. Reider, 1998, “Implied Exchange Rate
Distributions: Evidence from OTC Option Markets,” Journal of International
Money and Finance, 17, 117–160.

11.Canina, L., and S. Figlewski, 1993, “The Informational Content of Implied
Volatility,” Review of Financial Studies, 6, 659–681.

12.Chiras, D. P. and Manaster, S., 1978, The information content of option
prices and a test of market efficiency, Journal of Financial Economics ,
June-Sep, pp213-234.

13.Christensen, B. J., and N. R. Prabhala, 1998, “The Relation between Implied and
Realized Volatility,” Journal of Financial Economics, 50, 125–150.

14.Day, T. E., and C. M. Lewis, 1988, “The Behavior of the Volatility Implicity in
the Price of Stock Index Option,” Journal of Finance Economics, 22, 103-122.

15.Derman, E., and I. Kani, 1998, ‘‘Stochastic Implied Trees: Arbitrage Pricing with Stochastic Term and trike Structure of Volatility,’’ International Journal of Theoretical and Applied Finance, 1, 61–110.

16.Engle, R. F., 1982, “Autoregressive conditional Heteroskedasticity with
Estimates of The Variance of UK Inflation,” Econometrica, 50, 987-1008.

17.Fleming, J., 1998, “The Quality of Market Volatility Forecast Implied by S&P
100 Index Option Prices,” Journal of Empirical Finance, 5, 317–345.

18.Giot, P., 2005, “Implied volatility indexes and daily Value-at-Risk models,” Journal of Derivatives, 12, 54-64.

19.Glosten, L., R. Jagannathan, and D. E. Runkle, 1993, “On the Relation between the Expected Value and The Volatility of the Nominal Excess Return on Stocks,” The Journal of Finance, Vol. 48, pp. 1779-1801.

20.Hull, J. and A. White, 1998, Incorporating Volatility Updating into the Historical Simulation Method for Value-at-Risk, Journal of Risk, 1, 5-19.

21.Jiang, G.. J. and Y. S. Tian, 2005, “The Model-Free Implied Volatility and Its
Informaion Content,” The Review of Financial Studies, 18, 1306-1342.

22.Jorion, Philippe 1996 “VALUE AT RISK: The New Benchmark for Controlling Market Risk.” IRWIN publishing.

23.J.P. Morgan & Reuters 1996. “RISKMETRICS TECHNICAL DOCUMENT.” 4th edtion.

24.Kupiec, Paul H. 1995. ”Techniques for Verifying the Accuracy of Risk
Measurement Models.” The Journal of Derivatives, winter, pp. 73-84.

25.Nelson, D. B., 1991, “Conditional Heteroskedasticity in Asset Returns: A New Approach,” Econometrica, Vol. 59, pp. 347-370.

26.Poon S. H. and C. W. J. Granger, 2003, “Forecasting Volatility in Financial
Market : A Review,” Journal of Economic Literature, 41, 478-539.

27.Rubinstein, M., 1994, ‘‘Implied Binomial Trees,’’ Journal of Finance, 49, 771–818.

28.Shimko, D., 1993, “Bounds of Probability,” Risk, 6, 33–37.


29.Trippi, Robert R., 1977, A test of option market efficiency using a
random walk valuation model, Journal of Economics and Business , winter, pp93-98.


二、國內文獻

1.陳玉菁 (2007),「台指選擇權隱含波動度之資訊含量」,淡江大學財務金融研究所碩士論文。

2.莊明霖 (2007),「選擇權隱含波動率對未來波動率之資訊內涵」,國立台灣大學財務金融研究所碩士論文。

3. 黃崇銓 (2007),「Model-Free隱含波動度價差之遠期資訊」,國立中央大學財務金融研究所碩士論文。

4.黃雯卿 (2007),「無模型設定隱含波動度之實證研究-以台灣股價指數選擇權為例」,國立東華大學國際經濟研究所碩士論文。
5.鄭智謙 (2006),「無模型設定隱含波動率-S&P500指數期貨選擇權的隱含波動率之實證研究」,國立台灣大學財務金融研究所碩士論文。
電子全文 電子全文(本篇電子全文限研究生所屬學校校內系統及IP範圍內開放)
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top