Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/23661
標題: The Relationship of Realized Volatility, Implied Volatility and Long Memory : Evidence from S&P 100 Stock Option Market
以S&P100個股選擇權市場探討實現波動度、隱含波動度與緩長記憶關係之研究
作者: 洪培勛
Hong, Pei-Syun
關鍵字: long memory
緩長記憶
fractional cointegration
implied volatility
realized volatility
部份共整合
隱含波動度
實現波動度
出版社: 財務金融系所
引用: Ammann, M., Skovmand, D. and Verhofen M. (2009). “Implied and Realized Volatility in the Cross-Section of Equity Option.” Working Papers Andersen, T. G., T. Bollerslev, and F. X. Fiebold, (2006) “Parametric and Nonparametric Volatility Measurement.” Handbook of Financial Econometrics. Andrews, D. W. K. and P. Guggenberger, (2003) “A Bias-Reduced Log-Periodogram Regression Estimator for the Long Memory Parameter.” Econometrica 71, 675-712. Andrews, D. W. K. and Y. Sun, (2004). “Adaptive Local Polynomial Whittle Estimation of Long-Range Dependence.” Econometrica 72, 569-614. Bent, J.C., Charlotte, S.H. (2002). “New Evidence on The Implied-Realized Volatility Relation.” The European Journal of Finance 8, 187-205. Baillie, R., T. Bollerslev and H.O. Mikkelsen, (1996) “Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity.” Journal of Econometrics, 74, 1996, 3-30. Bakshi, G. & Kapadia, N. (2003). “Volatility risk premiums embedded in individual equity options: Some new insights.”, Journal of Derivatives 11, 45.54. Bollerslev, T. (1986). “Generalized autoregressive conditional heteroskedasticity.”, Journal of Econometrics 31, 307-327. Bollerslev, T. and H. O. Mikkelsen, (1996). “Modeling and Pricing Long-Memory in Stock Market Volatility.” Journal of Econometrics 73, 151-184. Box,G.E.P. and Jenkins G.M. (1970).“Time series analysis: forecasting and control.” Chan, K. and Fong, W. M. (2000). “Trade Size, Order Imbalance, and the Volatility-Volume Relation.” Journal of Financial Economics 57, 247-273. Christensen, B. J., Prabhala, N.R. (1998). “The Relation Between Implied and Realized Volatility.” Journal of Financial Economics 50, 125-150. Christensen, B. J. and Nielsen M. O. (2006). “Asymptotic Normality of Narrow-Band Squares in the Stationary Fractional Cointegration Model and Volatility Forecasting.” Journal of Econometrics 133, 343-371. Charlotte, S.H., (2001). “The Relationship Between Implied and Realized Volatility in the Danish Option and Equity Markets.” Accounting and Finance 41, 197-228. Chernov, M., (2004). “Implied Volatilities as Forecasts of Future Volatility, the Market Risk Premia, and Returns Variability.” Mimeo. Canina, L. and S. Figlewski, (1993). “The Informational Content of Implied Volatility.”, The Review of Financial Studies, 6, 659-681. Christensen, B.J. and N.R. Prabhala, (1998). “The Relation Between Implied and Realized Volatility.”, Journal of Financial Economics, 50, 125-150. Comte, F. and E. Renalut, (1996). “Long-Memory Continuous-Time Models.”, Journal of Econometrics, 73, 101-149. Cheung, Y. W. and Lai, S. L. (1993). “A Fractional Cointegration Analysis of Purchasing Power Parity.”, Journal of Business and Economic Statistics, 11, 103-112. Chiras, D. P. and Manaster, S. (1978). “The information content of option prices and a test of market efficiency.”, Journal of Financial Economics 6, 213-234. Day, T. E. and Lewis, C. M.(1993). “Forecasting Futures Market Volatility.”, Journal of Derivatives, 1, 33-50. Ding, Z., C. W. K. Granger and R. F. Engle, (1993). “A Long Memory Property of Stock Returns and a New Model.”, Journal of Empirical Finance, 1, 83-106. Dolado, J. J. and Marmol, F. (1996). “Efficient Estimation of Cointegrating Relationships Among Higher Order and Fractionally Integrated Processes.” Dittmann, I. and C. W. J. Granger, (2002). “Properties of Nonlinear Transformations of Fractionally Integrated Processes.”, Journal of Econometrics, 110, 113-133. Dueker, M. and Startz, R. (1998). “Maximum-likelihood estimation of fractional cointegration with an application to US and Canadian bond rates.”, Review of Economics and Statistics, 80, 420-426. Engle, R. F. (1982). “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation.”, Econometrica 50, 987-1007. Federico, M.B., Benoit, P. (2006). “Long Memory and Relation between Implied and Realized Volatility.” Journal of Financial Economics 4, 636-670. Geske, R. (1979). “The Valuation of Compound Option.” Journal of Financial Economics 7, 63-81. Geweke, J. and S. Porter-Hudak, (1983). “The Estimation and Application of Long Memory Time Series Models.”, Journal of Time Series Analysis 4, 221-238. Granger, C. J. and Joyeux, R. (1980).“An Introduction to Long-Memory Time Series Models and Fractional Differencing.” Journal of Time Series Analysis 1, 15-29. Harvey, C. R. and Whaley, R. E. (1992). “Market Volatility Prediction and the Efficiency of the S&P 100 Index Option Markey.”, Journal of Financial Economics 31, 43-73. Hurvich, C. M. and B. K. Ray, (2003). “The Local Whittle Estimator of Long-Memory Stochastic Volatility.”, Journal of Financial Econometrics 1, 445-470. Jorion, P. (1995), “Predicting Volatility in the Foreign Exchange Market.”, Journal of Finance 50, 507-528. Jackwerth, J. C. and Rubinstein, M. (1996). “Recovering Probability from Option Prices.”, Journal of Finance 51, 1611-1631. Kunsch, H. R. (1987). “Statistical Aspects of Self-Similar Processes.” Proceedings of the first World Congress of the Bernoulli Society, Utrecht: VNU Science Press, 67-74. Latané, H. A. and Rendleman, R. J. (1976). “Standard deviations of stock price ratios in implied option prices.”, Journal of Finance 76, 369-381. Lamoureux, C. G. and Lastrapes, W. D. (1993). “Forecasting Stock-Return Variance: Toward an Under-standing of Stochastic Implied Volatilities.”, Review of Financial Studies, 6, 293-326. Poteshman, A. M., (2000). “Forecasting Future Volatility from Option Prices.” Mimeo. Poon, S.-H. & Granger, C. (2005). “Practical issues in forecasting volatility.”, Financial Analyst Journal 61, 45-56. Poterba, J. M. and Summers, L. H. (1986). “The Persistence of Volatility and Stock Market Fluctuations.” American Economic Review 76, 1142-1151. P. Dennis, and S. Mayhew, (2002). “Risk-Neutral Skewness: Evidence from Stock Options.”, Journal of Financial and Quantitative Analysis 37, 471-493. Robinson, P. M. and D. Marinucci, (2001). “Narrow-band Analysis of Nonstationary Processes.”, Mimeo, LSE. Robinson, P. M. (1995). “Gaussian Semiparametric Estimation of Long Range Dependence.”, Annals of Statistics 23, 1630-1661. Robinson, P. M., (1995). “Log-Periodogram Regression of Time Series with Long Range Dependence.”, Annals of Statistics 23, 1048-1072. Robinson, P. M., (1994). “Semiparametric Analysis of Long-Memory Time Series.”, Annals of Statistics 22, 515-539. Robinson, P. M. and D. Marinucci, (2003). “Semiparametric Frequency Domain Analysis of Fractional Cointegration.”, Time Series With Long Memory. Robinson, P. M. and Hualde,J. (2003). ”Cointegration in fractional systems with unknown integration orders.”, Econometrica 71, 1727-1766. Schwert, G. W. (1989). “Why does Stock Market Volatility Change over Time.”, The Journal of Finance 5, 1115-1153. Sheikh, A. M. (1989). “Stock Splits, Volatility Increases, and Implied Volatilities.”, Journal of Finance 44, 1361-1372. Tauchen, G., Zhang, H. and Liu, M. (1996).“Volume, Volatility, and Leverage: A Dynamic analysis.”, Journal of Econometrics 74, 177-208. Yule, G. U. and Greenwood, M. (1920). “An Inquiry into the Nature of Frequency Distributions Representative of Multiple Happenings with Particular Reference to the Occurrence of Multiple Attacks of Disease or of Repeated Accidents.” Journal of the Royal Statistical Society 83, 255-279. Velasco, C. (1999). “Non-Stationary Log-Periodogram Regression.” Journal of Econometrics 91, 325-371.
摘要: We argue that the predictive regression between implied volatility and realized volatility is likely to be a fractional cointegration relation. Then we use narrow band spectral least squares methods of semiparametric frequency domain analysis to estimate the relation between implied volatility and realized volatility. We find that Size, Beta and Option Volume have a positive relation with the slope estimators in the regression between the implied volatility and the realized volatility. As Size, Beta and Option Volume increase, the sensitivity of the implied volatility to the realized volatility would increase. In addition, Beta and Option Volume have a positive relation with the absolute value of intercept estimators. When Beta and Option Volume higher represent the risk bigger, the market investors will tend to pay a higher volatility risk premium to avoid risk. Cause the phenomenon that when Beta and Option Volume increase, the absolute value of intercept estimators will be higher. However, we consider that the implied volatility is an unbiased predictor of the realized volatility in this paper.
我們認為在隱含波動度和實現波動度常見的預測迴歸之中可能存在一個部份共整合的關係,而我們利用半參數頻域(semiparametric frequency domain)分析之NBLS方法來估計隱含波動度與實現波動度之關係,且加入公司特性探討是否會對隱含波動度與實現波動度之間的關係造成影響。我們發現公司規模(Size)、Beta、選擇權交易量(Option Volume)在隱含波動度和實現波動度的迴歸關係中與斜率估計值 (β ̂) 皆有正向的關係,隨著公司規模(Size)、Beta、選擇權交易量(Option Volume)越大,隱含波動度對實現波動度的敏感度也越高。此外,Beta、選擇權交易量(Option Volume)則與截距項估計值 (α ̂) 的絕對值有正向的關係,當Beta、選擇權交易量(Option Volume)越高的時候代表此時風險越大,市場投資人會傾向付出一筆較大的波動度風險貼水來規避風險,造成Beta、選擇權交易量(Option Volume)越高時,截距項估計值 (α ̂) 的絕對值也越高的現象。然而在本文中我們認為隱含波動度為實現波動度的一個不偏估計值。
URI: http://hdl.handle.net/11455/23661
其他識別: U0005-0207201015164700
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0207201015164700
Appears in Collections:財務金融學系所

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.