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Student Number 944208026
Author CHIEN-CHIH KUO(郭建志)
Author's Email Address No Public.
Statistics This thesis had been viewed 1759 times. Download 633 times.
Department Finance
Year 2006
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title Bivariate Density Prediction for Financial Asset Prices
Date of Defense 2007-07-03
Page Count 25
Keyword
  • generalized beta distribution
  • lognormal mixture
  • Abstract 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.
    Table of Content 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
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    Advisor
  • Yaw-Huei Wang(王耀輝)
  • Files
  • 944208026.pdf
  • approve in 2 years
    Date of Submission 2007-07-17

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