資產價格之雙變量機率分配預測

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：7

、訪客IP：13.58.252.8

姓名

郭建志(CHIEN-CHIH KUO) 查詢紙本館藏

畢業系所

財務金融學系

論文名稱

資產價格之雙變量機率分配預測
(Bivariate Density Prediction for Financial Asset Prices)

相關論文

★ 歐元對歐洲股票市場整合之影響：產業實證分析	★ 選擇權交易與標的資產報酬及標準差之關係
★ Copula-based GARCH模型於期貨避險之應用	★ GARCH-jump模型預測波動性之準確度
★ 個股選擇權市場的流動性不足貼水與波動度差	★ 一般化自我迴歸條件異質變異數模型在不同分配假設下對波動度與價格分配預測之表現
★ 選擇權市場的資訊內涵	★ Model-Free隱含波動度價差之遠期資訊

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本篇論文利用選擇權價格所提供的資訊，以兩種機率分配模型，混合型常態分配與廣義貝他分配，預估未來風險中立下的資產價格機率分配。在得到風險中立下的機率分配後，利用羃次效用函數將風險中立世界中的預測機率轉換為現實世界的預測機率分配，再使用四種關聯結構函數，高斯結構關聯函數、法蘭克結構關聯函數、干貝爾結構關聯函數與克萊頓結構關聯函數，將兩種資產的預測分配結合，轉換為雙變量的聯合分配。採用S&P 500與IBM在1996到2005年的選擇權價格資料進行模型的估計與關聯結構函數的結合，並以Berkowitz在2001年提出的統計檢定方法測試預測模型的預測能力。根據本論文的實證結果，以廣義貝他函數搭配克萊頓關聯結構函數的模型會有比較好的預測能力。

摘要(英)

Option prices provide a rich source of information for estimating risk-neutral world densities. This paper exploits lognormal mixture distribution and generalized beta distribution to forecast asset price risk-neutral probability distribution when options expire. The power utility function is used to estimate the risk aversion parameter, and transform the risk-neutral world density into the real world density. After completing transformation, four kinds of copula functions, including Gassian copula, Frank copula, Gumbel copula and Clayton copula are used to combined two predictive density. The empirical results are examined with test of Berkowitz (2001). According to the empirical results, the combination of generalized beta distribution and Clayton copula outperforms other models in this paper.

關鍵字(中)

★ 現實世界機率分配
★ 風險中立機率分配
★ 關聯結構函數
★ 廣義貝他分配
★ 混合型對數常態分配

關鍵字(英)

★ lognormal mixture
★ generalized beta distribution

論文目次

第一章、緒論 1
1-1 研究動機與目的 1
1-2 研究架構 1
第二章、文章回顧 2
2-1 無風險世界中的機率模型 2
2-2 風險轉換 3
第三章、風險轉換方法與關聯結購函數 4
3-1 風險中立(risk-neutral world)下的機率分配 4
3-2 現實世界(real world)的機率分配 4
3-3 關聯結構函數(copula) 5
第四章、機率分配估計方法(density estimation methods) 7
4-1 機率測度轉換 7
4-2 混合型對數常態分配(mixtures of lognormal densities, MLN) 7
4-3 廣義貝他分配(generalized beta densities, GB2) 9
4-4 風險中立下機率分配的參數估計 11
4-5 風險中立世界到現實世界轉換過程的參數估計 11
4-6 關聯結構函數選用 12
4-7 關聯結構函數的參數估計 14
第五章、資料來源 15
第六章、實證結果 15
6-1 單變量機率分配預測 15
6-2 雙變量機率分配預測 20
第七章、結論 22
參考文獻 23

參考文獻

Ait-Sahalia, Y. and A.W. Lo, Nonparametric estimation of state-price densities implicit in financial asset prices, Journal of Finance, 53, 499-547, 1998.
Ait-Sahalia, Y. and A.W. Lo, Nonparametric risk management and implied risk aversion, Journal of Econometrics, 94, 9-51, 2000.
Anagnou-Basioudis, I., M. Bedendo, S. D. Hodges and R. Tompkins, Forecasting accuracy of implied and GARCH-based probability density functions, Review of Futures Markets, 11, 41-66, 2005.
Bakshi, G., N. Kapadia and D. B. Madan, Stock return characteristics, skew laws, and the differential pricing of individual equity options, Review of Financial Studies, 16, 101-143, 2003.
Bates, D.S, Post-’87 crash fears in the S&P 500 futures option markets, Journal of Econometrics, 94, 181-238., 2000.
Berkowitz, J. (2001), Testing dnsity forcasts with application to risk Management, Journal of Business and Economic Statistics, 19, 465-74, 2001.
Bliss, R. and N. Panigirtzoglou, Recovering risk aversion from options, Journal of Finance, 59, 2004.
Bookstaber, R. and J. MacDonald, A general distribution for describing security price returns, Journal of Business, 60, 401-424, 1987.
Breeden, D. and R. Litzenberger, Prices of state-contingent claims implicit in options prices, Journal of Business, 51,621-651, 1978.
Brigo, D. and F. Mercurio, Lognormal –mixture dynamics and calibration to market volatility smiles, International Journal of Theoretical and Applied Finance, 5,427-446, 2002.
Buchen, P. and M. Kelly, The maximum entropy distribution of an asset inferred from option prices, Journal of Financial and Quantitative Analysis,31,143-159, 1996.
Chernov, M. and E. Ghysels, A study towards a unified approach to the joint estimation of objective and risk neutral measure for the purpose of options valuation, Journal of Financial Economics, 56, 407-458, 2000.
Cherubini, U. and Luciano, E., Bivariate option pricing with copulas, Appl. Math. Finance, 8, 69-85, 2002a.
Clayton, D. G., A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence, Biometrika, 65, 141-151, 1978.
Cook, R.D. and Johnson M.E., A family of distributions for modeling non-elliptically symmetric multivariate data, J. Roy. Statist. Soc. Ser. B, 43, 210-218,1981.
Cook, R.D. and Johnson M.E., Generalized Burr-Pareto-logistic distributions with applications to a uranium exploration data set, Technometrics, 28, 123-131,1986.
Cox, D.R.and Oakes,D. Analysis of survival data Cahpman &Hall, London, 1984.
Frank, M.J., On the simultaneous associativity of F(x,y)and x+y-F(x,y), Aequationes Mah, 19,194-226, 1979.
Genest, C. Frank’s family of bivariate distributions, Biometrika, 74, 549-555, 1987.
Hull, J.C, Options, futures, and other derivatives, Prentice Hall, New Jersey, 2006.
Jackwerth, J., Recovering risk aversion from option prices and realized returns, Review of Financial studies, 13, 433-451, 2000.
Jackwerth, J. and M. Rubinstein, Recovering probability distribution from option prices, Journal of Finance, 51, 1611-1631, 1996.
Jondeau, E. and M. Rockinger, Reading the smile: the message conveyed by methods which infer risk neutral densities, Journal of International Money and Finance, 19, 885-915, 2000.
Joe, H., Multivariate models and dependence concepts, Chapman & Hall/CRC, New York, 1997.
Liu, X., S.B Shackleton, S. J Taylor and X. Xu,Closed-form transformations from risk neutral to real-world densities, Journal of Banking and Finance, 31, 1501-1520, 2007.
Luciano, U.C.E. and W. Vecchiato, Copula method in Finance, John Wiley & Sons Ltd, England, 2005.
Melick, W.R. and C.P. Thomas, Recovering an asset’s implied PDF from option prices: an application to crude oil during the Gulf crisis, Journal of Financial and Quantitative Analysis, 32, 91-115, 1997.
Oakes, D., A model for association in bivariate survival data, J. Roy. Statist. Soc. Ser. B, 44, 414-422, 1982.
Oakes, D., Semiparametric inference in a model for association in bivariate survival data, Biometrika, 73, 353-361, 1986.
Ritchey, R.J, Call option valuation for discrete normal mixtures, Journal of Financial Research, 13, 285-296., 1990.
Roncalli, T., Gestiondes Risques Multiples.Cours ENSAI de 3e ann’ee. Groupe de Operationelle, Credit Lyonnais, 2002, working paper.
Rosenberger, J. and R.F. Engle, Empirical pricing kernels, Journal of Financial Economics, 64, 341-372, 2002.
Sklar, A., Fonctions de repartition a n dimensions et leurs marges, Pub. Inst. Statist. Univ. Paris, 8, 229-231, 1959.
Taylor, S.J., Asset price Dynamics, Volatility, and Prediction, Princeton University Press, New Jersey, 2005.

指導教授

王耀輝(Yaw-Huei Wang)

審核日期

2007-7-17

推文