發(fā)布時(shí)間：2018-04-26 11:52

  本文選題：譜風(fēng)險(xiǎn)度量 + G-H分布　； 參考：《北京化工大學(xué)》2012年碩士論文

【摘要】：在金融市場中，由于受到市場波動(dòng)的影響，投資者和金融機(jī)構(gòu)正面臨著更加嚴(yán)峻的金融風(fēng)險(xiǎn)。如此一來，如何準(zhǔn)確度量市場風(fēng)險(xiǎn)，來幫助投資者及金融機(jī)構(gòu)的管理者規(guī)避風(fēng)險(xiǎn)，是經(jīng)濟(jì)學(xué)家們不斷探索研究的主要工作。本文選用譜風(fēng)險(xiǎn)度量模型，，引入G-H分布，來作為對(duì)金融市場風(fēng)險(xiǎn)度量的主要方法。 風(fēng)險(xiǎn)譜函數(shù)用來反應(yīng)投資者對(duì)風(fēng)險(xiǎn)的厭惡程度，主要形式有三種，分別為指數(shù)型、冪型和雙曲型風(fēng)險(xiǎn)譜函數(shù)。本文分別采用指數(shù)型和雙曲型風(fēng)險(xiǎn)譜函數(shù)，選用G-H分布，結(jié)合譜風(fēng)險(xiǎn)度量方法，得到投資組合度量模型，并分別利用復(fù)化梯形法得到離散形式。 對(duì)單支股票的風(fēng)險(xiǎn)度量分析，本文選取康美藥業(yè)收益率序列作為樣本數(shù)據(jù)，分別采用分位點(diǎn)估計(jì)法和矩估計(jì)法做G-H分布的參數(shù)估計(jì)，并分別與基于正態(tài)分布的譜風(fēng)險(xiǎn)度量模型進(jìn)行對(duì)比，得到結(jié)論：選用G-H分布，并采用矩估計(jì)法估計(jì)參數(shù)，擬合效果最優(yōu)。選用Kupiec失敗率檢驗(yàn)法，在一定置信水平下，利用樣本內(nèi)數(shù)據(jù)做譜風(fēng)險(xiǎn)度量分析，并與樣本外數(shù)據(jù)進(jìn)行比較，得到預(yù)測失敗個(gè)數(shù)，得到結(jié)論。 對(duì)于投資組合的風(fēng)險(xiǎn)度量分析，本文選取上海家化等四支證券收益率序列，分別采用指數(shù)型和雙曲型風(fēng)險(xiǎn)譜函數(shù)，得到投資組合風(fēng)險(xiǎn)度量模型，得到結(jié)論：隨著期望收益率的增加或絕對(duì)風(fēng)險(xiǎn)厭惡因子的增加，譜風(fēng)險(xiǎn)度量值也會(huì)相應(yīng)增加，并且分配權(quán)重會(huì)向平均收益率高的證券轉(zhuǎn)移。 運(yùn)用copula函數(shù)來度量不同證券之間相關(guān)性。選取萬科A和海螺水泥兩支證券，選取適當(dāng)copula函數(shù)，建立投資組合模型。實(shí)證結(jié)果表明，由于考慮了證券收益的尾部相關(guān)性，可以得到更加貼近市場實(shí)際情況的風(fēng)險(xiǎn)度量結(jié)果。
[Abstract]:In the financial market, investors and financial institutions are facing more severe financial risks due to the influence of market fluctuations. Thus, how to measure the market risk accurately and help the investors and the managers of the financial institutions to avoid risk is the main work of the economists. This paper selects the spectrum risk measurement. The G-H distribution is used as the main method to measure the risk of financial market.
The risk spectrum function is used to reflect the degree of investor aversion to risk. There are three main forms, which are exponential type, power type and hyperbolic risk spectrum function. This paper uses exponential and hyperbolic risk spectrum functions, selects G-H distribution and combines spectral risk measurement method to obtain portfolio measurement model, and uses complex trapezoid method respectively. To the discrete form.
In the analysis of the risk measurement of single stock, this paper selects the recovery sequence of Kangmei pharmaceutical industry as the sample data, uses the point estimation method and the moment estimation method to estimate the parameters of the G-H distribution, and compares it with the spectral risk measurement model based on the normal distribution, and obtains the conclusion: the G-H distribution is selected and the moment estimation method is used to estimate the parameters. The fitting effect is optimal. The Kupiec failure rate test method is selected. Under certain confidence level, the spectrum risk measurement is analyzed by using the data in the sample, and compared with the data from the sample, the number of failure prediction is obtained and the conclusion is obtained.
For the risk measurement analysis of portfolio, this paper selects four stock return sequences, such as Shanghai domestication, and uses exponential and hyperbolic risk spectrum functions to get the portfolio risk measurement model. The conclusion is that as the expected return rate increases or the absolute risk aversion factor increases, the spectral risk measurement will also increase accordingly. And the distribution weights will be transferred to the securities with high average returns.
The copula function is used to measure the correlation between different securities. Two securities of Vanke A and conch cement are selected and appropriate Copula Functions are selected to establish an investment portfolio model. The empirical results show that the risk measurement results which are closer to the actual situation of the market can be obtained because of the tail correlation of stock returns.

【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;F832.51

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本文編號(hào)：1805905