本文關(guān)鍵詞： Copula函數(shù) GARCH模型 投資組合VaR GED分布　出處：《天津財經(jīng)大學》2013年碩士論文　論文類型：學位論文

【摘要】：金融風險測度一直以來都是現(xiàn)代金融風險管理的重要內(nèi)容。量化金融資產(chǎn)的風險,對金融風險控制具有很重要的意義。鑒于金融資產(chǎn)數(shù)據(jù)大多具有尖峰、厚尾以及波動集聚性等特征,在對其進行建模量化資產(chǎn)風險時,就需要根據(jù)資產(chǎn)數(shù)據(jù)特征來選定合適的模型和分布假定。現(xiàn)階段主要使用VaR方法量化金融資產(chǎn)或者資產(chǎn)組合風險值,使用的模型大多是GARCH族模型、Copula函數(shù)以及兩者的結(jié)合,并在不同的分布假定下,求解VaR值。尤其是在分布假定方面,學者們在進行深入研究的基礎(chǔ)上,使用了厚尾的t分布、GED分布以及極值分布等分布形式來更好的擬合數(shù)據(jù),構(gòu)建模型。在資產(chǎn)組合中,每一資產(chǎn)數(shù)據(jù)都可能擁有各自不同的特征,因而需要對每一資產(chǎn)收益數(shù)據(jù)進行分布擬合,確定其所更接近的分布模型,最終由Copula函數(shù)進行組合得到聯(lián)合分布函數(shù)。由此可以得到更加充分考慮資產(chǎn)組合中各個資產(chǎn)數(shù)據(jù)特征的GARCH-Copula模型,進而得到的在險價值VaR將更加有效。 針對金融資產(chǎn)組合中各個資產(chǎn)數(shù)據(jù)特征,論文使用具有厚尾特性的t分布和GED分布來對樣本數(shù)據(jù)進行擬合,之后構(gòu)建基于t分布和GED分布的GARCH-Copula模型,得到資產(chǎn)組合模擬收益率數(shù)據(jù)的VaR；作為對比,論文同時分別使用基于t分布的GARCH-Copula模型和基于GED分布的GARCH-Copula模型,用于測度資產(chǎn)組合的VaR；論文也引入基于GARCH模型和不同分布假定的計算VaR的簡單方法。 論文正是在較為充分考慮資產(chǎn)組合中單個數(shù)據(jù)特征的前提下,選用更適合資產(chǎn)分布假定以及結(jié)合GARCH模型,描述數(shù)據(jù)所特有的波動集聚性的同時,構(gòu)建一個穩(wěn)健的模型,用于測算資產(chǎn)組合的VaR。同時結(jié)合每一資產(chǎn)數(shù)據(jù)服從的分布,使用合適的Copula函數(shù)對其進行組合,得到基于t分布和GED分布組合的GARCH-Copula模型,以測度資產(chǎn)組合的VaR。
[Abstract]:Financial risk measurement has always been an important part of modern financial risk management. Quantifying the risk of financial assets is of great significance to financial risk control. When modeling and quantifying asset risk, the characteristics of thick tail and volatility agglomeration, It is necessary to select appropriate models and distribution assumptions according to the characteristics of asset data. At present, the VaR method is mainly used to quantify the risk value of financial assets or asset combinations. Most of the models used are Copula functions of GARCH family model and their combination. Under different distribution assumptions, the VaR value is solved, especially in the distribution assumption. On the basis of in-depth research, the authors use the t distribution and extreme value distribution of the thick tail to fit the data better. Build models. In a portfolio, each asset data may have its own different characteristics, so it is necessary to fit each asset income data in order to determine the distribution model that is closer to each asset income data. Finally, the Copula function is combined to get the joint distribution function, which can get the GARCH-Copula model which fully considers the characteristics of each asset data in the asset portfolio, and then the riskier value VaR will be more effective. Aiming at the characteristics of each asset data in the financial asset portfolio, this paper uses the t distribution and GED distribution with thick tail to fit the sample data, and then constructs the GARCH-Copula model based on t distribution and GED distribution. As a comparison, GARCH-Copula model based on t distribution and GARCH-Copula model based on GED distribution are used in this paper. This paper also introduces a simple method to calculate GARCH based on GARCH model and different distribution assumptions. On the premise of fully considering the characteristics of individual data in the asset portfolio, this paper chooses a more suitable asset distribution assumption and a GARCH model to describe the unique volatility agglomeration of the data, and constructs a robust model at the same time. At the same time, combined with the distribution of each asset data, the Copula function is used to combine it, and the GARCH-Copula model based on the combination of t distribution and GED distribution is obtained to measure the value of the portfolio.
【學位授予單位】：天津財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F830.59;F224

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本文編號：1507544