【摘要】：隨著近幾十年國際上金融行業(yè)的發(fā)展以及金融市場的變遷,金融資產(chǎn)管理的風(fēng)險也逐步加劇,因此資產(chǎn)管理配置問題也是近年來學(xué)術(shù)界研究的熱點問題之一。Markowitz(1952)的均值—方差模型為現(xiàn)代的資產(chǎn)組合理論奠定了一定的基礎(chǔ)[1]。Copula分布估計算法是結(jié)合Copula理論和分布估計算法二者產(chǎn)生的一種智能優(yōu)化算法。Copula理論,為獲取聯(lián)合分布提供了一種方法,它提出將聯(lián)合分布分解為一個連續(xù)函數(shù)和多個邊緣分布[2],其中,邊緣分布反映單變量變化,函數(shù)說明變量間的相關(guān)結(jié)構(gòu)。與聯(lián)合分布相比,獲取變量的邊緣分布相對更容易,取樣更簡單。智能優(yōu)化算法興起于20世紀(jì)30年代,引入了生物進(jìn)化的思想和特征,主要包括選擇、遺傳等,典型算法如遺傳算法、菌群算法粒子群優(yōu)化算法等。而分布估計算法是基于遺傳算法發(fā)展起來的一種進(jìn)化算法,其主要特點是建立概率模型來得到新個體[3]。分布估計算法在運算時具有高效的優(yōu)點,但是在估計概率模型時,其操作比較復(fù)雜,運算量也比較龐大。因此,本文將Copula理論和分布估計算法結(jié)合運用,利用Copula理論的優(yōu)勢簡化分布估計算法建立概率分布模型的過程,并將Copula分布估計算法運用到金融風(fēng)險分析領(lǐng)域進(jìn)行應(yīng)用,并且引入菌群算法復(fù)制的思想進(jìn)行改進(jìn)。然后將Copula-Va R模型計算的結(jié)果與Copula分布估計算法度量風(fēng)險的結(jié)果進(jìn)行對比,說明使用算法的有效性。在算法目標(biāo)優(yōu)化函數(shù)的選取上,本文引入了風(fēng)險調(diào)整資本收益這一目標(biāo)函數(shù),更符合風(fēng)險的實際含義。其次,通過實證說明了Copula函數(shù)可以很好的獲取到各金融資產(chǎn)之間的有效信息,尤其是金融資產(chǎn)不服從正態(tài)分布,具有尖峰厚尾分布特征的特性[2]。投資者在面對當(dāng)今風(fēng)險加劇的金融市場時,對于風(fēng)險分析度量的要求會越來越高,因為投資者會希望以更小的風(fēng)險達(dá)到最大化的收益,更好地進(jìn)行資產(chǎn)配置。因此本文將Copula分布估計算法應(yīng)用到風(fēng)險分析中是有現(xiàn)實意義的,為風(fēng)險度量提供了又一種方法。
[Abstract]:With the development of the international financial industry and the changes of the financial market in recent decades, the risk of financial asset management has been gradually increased. Therefore, asset management allocation is one of the hot issues in academic circles in recent years. The mean-variance model of Markowitz (1952) has laid a certain foundation for modern portfolio theory [1] .Copula distribution estimation algorithm is based on Copula theory and distribution. Copula theory, an intelligent optimization algorithm produced by both estimation algorithms, In order to obtain the joint distribution, a method is provided in which the joint distribution is decomposed into a continuous function and several edge distributions [2], in which the edge distribution reflects the variation of the single variable, and the function explains the correlation structure between the variables. Compared with the joint distribution, it is easier to obtain the marginal distribution of variables and to sample them more easily. Intelligent optimization algorithm was developed in 1930s. It introduces the idea and characteristics of biological evolution, including selection, heredity, typical algorithms, such as genetic algorithm, bacterial colony algorithm, particle swarm optimization algorithm and so on. The distribution estimation algorithm is an evolutionary algorithm based on genetic algorithm. Its main feature is to establish a probability model to get new individuals. The distributed estimation algorithm has the advantage of high efficiency in operation, but in estimating the probabilistic model, its operation is more complicated, and the computation is very large. Therefore, this paper combines the Copula theory and the distribution estimation algorithm to simplify the process of establishing the probability distribution model by using the advantage of the Copula theory, and applies the Copula distribution estimation algorithm to the field of financial risk analysis. And the idea of replicating bacteria colony algorithm is introduced to improve it. Then the results of Copula-Va R model calculation are compared with the results of Copula distribution estimation algorithm to measure the risk, which shows the effectiveness of using the algorithm. In the selection of the optimization function of the algorithm, this paper introduces the objective function of risk-adjusted capital return, which is more in line with the actual meaning of risk. Secondly, it is proved that the Copula function can obtain the effective information between the financial assets, especially the characteristics of the financial assets with the characteristics of peak and thick tail distribution. In the face of the financial market where the risk is increasing, the demand for risk analysis will be higher and higher, because investors will want to maximize the return with less risk and better asset allocation. Therefore, it is of practical significance to apply Copula distribution estimation algorithm to risk analysis, which provides another method for risk measurement.
【學(xué)位授予單位】：廣東財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F830;F224

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本文編號：2183044