簡易檢索 / 詳目顯示
| 研究生： |
李政剛 Lee,Jonathan K. |
|---|---|
| 論文名稱： |
交易量對於隱含波動度預測誤差之對偶效果-Panel Data的分析 The Dual Effect of Volume and Volatility Forecasting Error-Panel Data analysis |
| 指導教授： |
杜化宇
Tu,Anthony H. |
| 學位類別： |
碩士
Master |
| 系所名稱： |
商學院 - 財務管理學系 Department of Finance |
| 論文出版年： | 2005 |
| 畢業學年度： | 93 |
| 語文別： | 中文 |
| 論文頁數： | 87 |
| 中文關鍵詞： | 對偶效果 、交易量 、隱含波動度 、波動度預測 、異質性 、固定效果模型 、隨機效果模型 |
| 外文關鍵詞： | dual effect, volume, implied volatility, volatility forecasting, panel data, heterogeneity, fixed effects model, random effects model |
| 相關次數： | 點閱：101 下載：35 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
本研究探討選擇權交易量之大小對於波動度預測之效率性所造成之對偶效果(dual effect)，驗證〝正常的高交易量〞與〝異常的高交易量〞對於波動度預測能力是否有不同的影響。本研究採用panel data之資料型態，以LIFFE上市的個股買權為對象，資料長度為三年左右。主要欲探討之假說為： 1.一般而言，交易量大的選擇權，其波動度估計誤差較交易量小的選擇權來得小。 2.相對於平日水準而言，某日交易量異常高的選擇權將有較大的波動度估計誤差。
本研究所使用的波動度預測模型為隱含波動度(ISD)，採用的是最接近到期月份及最接近價平的合約。實證以組合迴歸、固定效果模型、隨機效果模型分別估計之，加以比較。結果發現固定效果模型為較佳之解釋模型，然而結果顯示交易量的對偶效果並不明確影響波動度預測誤差，故推測有某種影響公司間差異的因素，即公司間之異質性，比相對交易量更容易影響波動度預測之誤差。另外，透過組間與組內效果之分析，發現不論是長期還是短期，由於公司間的異質性存在，使得相對交易量對於波動度預測誤差均無明顯影響。
The purpose of this research is to study the dual effect on the efficiency of volatility forecasting which is caused by the volume of option market, with the intent to test whether〝normal high volume〞and〝abcdrmal high volume〞cause different results on the ability of volatility forecasting. The data used is in the form of panel data. It is drawn from LIFFE, and has a length of about three years. The hypotheses to be examined in this study are：1. High-average-volume options have smaller volatility forecasting errors than low-average-volume options; 2. Options have larger volatility forecasting errors on abcdrmally-high-volume days than on normal-volume days.
In this research, volatility is forecasted by implied standard deviation (ISD) which is implied in the at-the-money and the nearest expiry month options. Pooled regression、fixed effect model、and random effect model methods were applied. The results show that the fixed effect model made the best analysis amongst the three models. However, the result does not support the hypotheses made above, which means that volume does not have much influence on volatility forecasting error. It is inferred that there exists some other factors which could cause the difference between firms, namely heterogeneity, and these factors have much more powerful influence over volatility forecasting error than volume. Finally, it was found that no matter for long run or short run, because of the existence of heterogeneity, relative volume doesn’t have obvious influence on volatility forecasting errors when analyzing the difference between the between-individual effect and the within-individual effect.
第壹章 緒論...................................................1
第一節、研究動機與目的....................................1
第二節、研究架構與流程....................................5
第貳章 文獻探討...............................................7
第一節、交易量的對偶效果..................................7
第二節、隱含波動度預測未來波動度.........................10
第三節、交易量與波動度預測之關聯.........................18
第四節、整理與比較.......................................27
第參章 研究方法..............................................29
第一節、交易量對於波動度預測誤差的對偶效果...............29
第二節、處理Panel Data的相關模型.........................31
第三節、各模型間之關係及取捨.............................44
第肆章 實證分析與結果........................................47
第一節、資料來源與處理...................................47
第二節、交易量對於波動度預測誤差的對偶效果—一般迴歸模型.52
第三節、交易量對於波動度預測誤差的對偶效果—組合迴歸模型.53
第四節、交易量對於波動度預測誤差的對偶效果—固定效果模型.56
第五節、交易量對於波動度預測誤差的對偶效果—隨機效果模型.59
第六節、各模型估計結果之比較與選擇.......................62
第七節、組內與組間效果之實證.............................65
第伍章 結論..................................................70
第一節、結論.............................................70
第二節、研究限制與建議...................................72
參考文獻.....................................................73
附錄.........................................................76
Baltagi, B. H., 1995, Econometric Analysis of Panel Data, John Wiley & Sons Ltd, England.
Baltagi, B. H., 2001, Econometric Analysis of Panel Data, 2nd edn., John Wiley & Sons Ltd, England.
Baltagi, B. H., and J. M. Griffin, 1984, Short and Long Run Effects in Pooled Models, International Economic Review, Vol. 25, No. 3., 631-645.
Barron, O. E., and J. M. Karpoff, 2004, Information Precision, Transaction Costs, and Trading Volume, Journal of Banking & Finance 28, 1207-1223.
Bergman, Y. Z., B. D. Grundy, and Z. Wiener, 1996, General Properties of Option Prices, Journal of Finance, Vol. LI, No. 5., 1573-1610.
Brooks, C., 1998, Predicting Stock Index Volatility：Can Market Volume Help？, Journal of Forecasting, Vol. 17, 59-80.
Canina, L., and S. Figlewski, 1993, The Informational Content of Implied Volatility, Review of Financial Studies, Vol. 6, No. 3, 659-681.
Christensen, B. J., and N. R. Prabhala, 1998, the Relation Between Implied and Realized Volatility, Journal of Financial Economics, 50, 125-150.
Copeland, T. E., 1976, A Model of Asset Trading Under the Assumption of Sequential Information Arrival, Journal of Finance, Vol. XXXI, No. 4., 1149-1168.
Cornell, B., 1990, Volume and R2：A First Look, Journal of Financial Research, Vol. XIII, No 1., 1-6.
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.
Donaldson, G., and M. Kamstra, 2004, Volatility Forecasts, Trading Volume, and the ARCH versus Option-Implied Volatility Trade-off, Federal Reserve Bank of Atlanta, Working Paper Series, 1-41.
Ederington, L. H., and W. Guan, 2002, Measuring Implied Volatility：Is an Average Better？Which Average？, Journal of Futures Markets, Vol. 22, No. 9, 811-837.
Freund, S., and G. P. Webb, 1999, Recent Growth in Nasdaq Trading Volume and Its Relation to Market Volatility, Journal of Financial Research, vol. XXII, No. 4, p. 489-501.
Greene, W. H., 2000, Econometric Analysis, 4th edn., Prentice Hall International, New Jersey.
Greene, W. H., 2003, Econometric Analysis, 5th edn., Prentice Hall International, New Jersey.
Hull, J. C., 2003, Options, Futures, and Other Derivatives, 5th edn., Pearson Education, New Jersey.
Hauthakker, H. S., 1965, New Evidence on Demand Elasticities, Econometrica, Vol. 33, No. 2., 277-288.
Jorion, P., 1995, Predicting Volatility in the Foreign Exchange Market, Journal of Finance, Vol. 50, No. 2, 507-528.
Judge, G., W. E. Griffiths, R. C. Hill, H. Lutkepohl, and T. C. Lee, 1985, The Theory and Practice of Econometrics, 2nd edn., John Wiley and Sons, New York.
Karpoff, J. M., 1987, the Relation Between Price Change and Trading Volume：A Survey, Journal of Financial and Quantitative Analysis, Vol. 22, No. 1, 109-26.
Lamoureux, C. G., and W. D. Lastrapes, 1990, Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects, Journal of Finance, Vol. XLV, No. 1., 221-229.
Lamoureux, C. G., and W. D. Lastrapes, 1993, Forecasting Stock-Return Variances: Toward an Understanding of Stochastic Implied Volatilities, Review of Financial Studies, 6, 293-326.
Latane, H. A., and J. R. Rendleman, Jr., 1976, Standard Deviations of Stock Price Ratios Implied in Option Prices, Journal of Finance, Vol. 31, No. 2, 369-381.
Long, D. M., and D. T. Officer, 1997, the Relation Between Option Mispricing and Volume in the Black-Scholes Option Model, Journal of Financial Research, Vol. XX, No. 1, p. 1-12.
Mayhew, S., and C. Stivers, 2003, Stock Return Dynamics, Option Volume, and the Information Content of Implied Volatility, Journal of Futures Markets, Vol. 23, No. 7, 615-646.
Mixon, S., 2001, Volume and Volatility: News or Noise？, Financial Review 36, p. 99-118.
Ncube, M., 1996, Modelling Implied Volatility with OLS and Panel Data Models, Journal of Banking & Finance, 20, 71-84.
Pindyck, R. S., and D. L. Rubinfeld, 1998, Econometric Models and Economic Forecasts, 4th edn., McGraw-Hill.
Tauchen, G. E., and M. Pitts, 1983, the Price Variability-Volume Relationship on Speculative Markets, Econometrica, Vol. 51, No. 2, 485-505.
Roll, R., 1988, R2, Journal of Finance, Vol. XLIII, No. 2, 541-66.
Robin, A. J., 1993, On Improving the Performance of the Market Model, Journal of Financial Research, Vol. XVI, No. 4, 367-76.