【摘要】：金融市場充滿風險，，投資者面臨兩個重要的問題：一是當某一金融市場出現(xiàn)巨大波動時，其他金融市場會不會受影響？二是當持有某個投資組合時，投資者所面臨的風險有多大，如何度量投資組合的在險價值VaR？ 考慮到Copula函數(shù)能更有效地捕捉金融資產(chǎn)之間的相關(guān)信息，其在金融市場之間的尾部相關(guān)性分析和投資組合的VaR計量上具有獨特的優(yōu)勢，本文采用Copula函數(shù)分別對這兩個問題進行研究，包括兩個部分的實證分析。 要解決的第一個問題，便是基于Copula函數(shù)導(dǎo)出兩個金融市場之間的尾部相關(guān)性，以滬深300指數(shù)和香港恒生指數(shù)的日收益率序列為研究對象，分析二者之間的相關(guān)結(jié)構(gòu)變動，從秩相關(guān)性和平方歐氏距離兩個評價指標得出在五個Copula函數(shù)族模型中哪一個能更好地擬合觀測數(shù)據(jù)。 其次，要解決的第二個問題，是將Copula技術(shù)應(yīng)用到投資組合的VaR度量中，并和傳統(tǒng)的方法進行了比較。所選取的Copula函數(shù)族包括正態(tài)Copula、t-Copula、Clayton-Copula、Frank-Copula和Gumbel-Copula，結(jié)果表明：基于Copula算法計算的VaR的絕對值在大多數(shù)情況下普遍大于傳統(tǒng)算法計算的結(jié)果，說明傳統(tǒng)的VaR計算方法低估了風險。Copula函數(shù)由于更加充分地考慮了變量之間的相關(guān)性，而且不囿于正態(tài)性假設(shè)，所以能更大程度地考慮風險。
[Abstract]:Financial markets are full of risks, and investors face two important questions: first, will other financial markets be affected when there is great volatility in one financial market? The second is how much risk investors face when holding a certain portfolio, and how to measure the in-risk value VaR? of the portfolio. Considering that the Copula function can capture the relevant information between financial assets more effectively, it has unique advantages in tail correlation analysis between financial markets and VaR measurement of portfolio, In this paper, Copula function is used to study these two problems, including two parts of empirical analysis. The first problem to be solved is to derive the tail correlation between the two financial markets based on the Copula function. Taking the daily return series of the Shanghai and Shenzhen 300 index and the Hang Seng Index of Hong Kong as the research object, the related structural changes between the two are analyzed. From the two evaluation indexes of rank correlation and square Euclidean distance, which of the five Copula function family models can better fit the observed data is obtained. Secondly, the second problem to be solved is to apply Copula technology to the VaR measurement of portfolio and compare it with the traditional method. The selected Copula function family includes normal Copula,t-Copula,Clayton-Copula,Frank-Copula and Gumbel-Copula, results show that the absolute value of VaR calculated based on Copula algorithm is generally larger than that calculated by traditional algorithm in most cases. It is shown that the traditional VaR calculation method underestimates the risk. Copula function can consider the risk to a greater extent because it considers the correlation between variables more fully and is not confined to the normality hypothesis.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F224;F830.9

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