【摘要】：隨著金融市場(chǎng)的迅猛發(fā)展,金融衍生工具頻頻出新,市場(chǎng)的多元化對(duì)風(fēng)險(xiǎn)度量提出了更高的要求,對(duì)風(fēng)險(xiǎn)的定量分析顯得尤為重要。 本文在介紹譜風(fēng)險(xiǎn)度量理論及風(fēng)險(xiǎn)厭惡度量理論的基礎(chǔ)上,給出了雙曲型風(fēng)險(xiǎn)譜函數(shù)等三種風(fēng)險(xiǎn)譜函數(shù)的形式,得到了譜風(fēng)險(xiǎn)度量的估計(jì)量,從而構(gòu)造出投資組合的優(yōu)化模型。采用樣本外數(shù)據(jù)對(duì)模型的有效性進(jìn)行Kupiec檢驗(yàn)。實(shí)證部分計(jì)算了單個(gè)資產(chǎn)及多個(gè)資產(chǎn)投資組合的譜風(fēng)險(xiǎn)度量值。實(shí)證結(jié)果表明,風(fēng)險(xiǎn)厭惡因子和置信水平的選取均對(duì)單一資產(chǎn)的雙曲型譜風(fēng)險(xiǎn)度量值產(chǎn)生影響,風(fēng)險(xiǎn)厭惡因子可以作為譜風(fēng)險(xiǎn)度量的數(shù)值表征；對(duì)給定的置信水平和風(fēng)險(xiǎn)厭惡因子,隨著期望收益率的增加,高收益的股票所占權(quán)重逐漸增大。 將Copula函數(shù)運(yùn)用到投資組合的譜風(fēng)險(xiǎn)度量模型中是本文的一個(gè)重要?jiǎng)?chuàng)新點(diǎn)。通過Copula函數(shù)研究資產(chǎn)之間的相依結(jié)構(gòu),可以提高SRM估計(jì)的準(zhǔn)確性。核密度估計(jì)對(duì)樣本的擬合度高,本文選用其確定邊緣分布,選擇Copula函數(shù)描述尾部相依性。用極大似然估計(jì)和非參數(shù)方法估計(jì)Copula函數(shù)的參數(shù),結(jié)合經(jīng)驗(yàn)Copula函數(shù),運(yùn)用平方歐氏距離對(duì)參數(shù)的估計(jì)進(jìn)行評(píng)價(jià)。最后,通過Monte Carlo模擬方法得到一種新的Copula-SRM算法。實(shí)證部分得到了五種Copula函數(shù)的參數(shù)估計(jì)值及Kendall秩相關(guān)系數(shù)和Spearman秩相關(guān)系數(shù)。實(shí)證結(jié)果表明上證指數(shù)和深證指數(shù)的日對(duì)數(shù)收益率存在較強(qiáng)的正相關(guān),t-Copula模型能更好地?cái)M合原始數(shù)據(jù),且Copula-SRM算法比傳統(tǒng)的SRM算法得到的結(jié)果更準(zhǔn)確。
[Abstract]:With the rapid development of the financial market, the financial derivatives frequently come out new, the diversification of the market put forward higher requirements for risk measurement, the quantitative analysis of risk is particularly important. On the basis of introducing the theory of spectral risk measurement and the theory of risk aversion, this paper gives the form of three kinds of risk spectrum functions such as hyperbolic risk spectrum function, obtains the estimator of spectral risk measurement, and constructs the optimal model of investment portfolio. The validity of the model is tested by Kupiec with the data outside the sample. The empirical part calculates the spectral risk measures of individual assets and multiple asset portfolios. The empirical results show that the selection of risk aversion factor and confidence level have an effect on the hyperbolic spectral risk measure of a single asset, and risk aversion factor can be used as a numerical representation of spectral risk measurement. For a given confidence level and risk aversion factor, with the increase of expected rate of return, the weight of high yield stock increases gradually. It is an important innovation of this paper to apply the Copula function to the portfolio spectral risk measurement model. The accuracy of SRM estimation can be improved by studying the dependent structure of assets by Copula function. The kernel density estimation has a high fitting degree to the sample. In this paper, the edge distribution is determined and the Copula function is chosen to describe the tail dependence. The parameters of Copula function are estimated by maximum likelihood estimation and nonparametric method, and the estimation of parameters is evaluated by square Euclidean distance combined with empirical Copula function. Finally, a new Copula-SRM algorithm is obtained by Monte Carlo simulation. In the empirical part, the parameter estimates of five kinds of Copula functions and the Kendall rank correlation coefficients and Spearman rank correlation coefficients are obtained. The empirical results show that there is a strong positive correlation between the daily logarithmic returns of Shanghai Stock Exchange Index and Shenzhen Stock Exchange Index, and the t-Copula model can better fit the original data, and the Copula-SRM algorithm is more accurate than the traditional SRM algorithm.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F830.9

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本文編號(hào)：2338223