本文選題：模糊變量 + 可信性理論��； 參考：《山東師范大學(xué)》2014年碩士論文

【摘要】：在馬柯維茨（Markowitz）經(jīng)典的均值—方差理論中，把收益率假設(shè)成為服從正態(tài)分布，利用收益率的方差度量投資中的風(fēng)險(xiǎn)，但這個(gè)假設(shè)經(jīng)常與現(xiàn)實(shí)情況不一樣。我國商業(yè)銀行的貸款收益率具有模糊不確定性，因此，我們把收益率假設(shè)成模糊變量，引入模糊熵和信息熵來度量貸款的風(fēng)險(xiǎn)程度。 模糊熵具有描述信息不確定程度的性質(zhì)。當(dāng)我們把收益率考慮成隨機(jī)變量且服從正態(tài)分布時(shí)，模糊熵與方差在度量風(fēng)險(xiǎn)方面是等價(jià)的；但是當(dāng)考慮收益率為模糊變量且不服從正態(tài)分布時(shí)，受到貸款資金在不同收益下風(fēng)險(xiǎn)等級不同的影響，模糊熵在風(fēng)險(xiǎn)衡量方面比方差更加合理。模糊熵改進(jìn)了方差依靠概率分布且計(jì)算復(fù)雜的缺陷，在度量貸款風(fēng)險(xiǎn)時(shí)更加符合實(shí)際情況。 使用模糊熵度量貸款組合的風(fēng)險(xiǎn)時(shí)，因?yàn)椴煌馁J款項(xiàng)目之間會有復(fù)雜的相關(guān)性，所以如果忽視相關(guān)性構(gòu)建貸款組合模型求解會使決策選擇集中在一個(gè)或者某幾個(gè)高收益的貸款項(xiàng)目中，與我們組合貸款的思想相違背。因此，為了解決這個(gè)小小的瑕疵，在模型中加入了分散風(fēng)險(xiǎn)的約束條件，從而彌補(bǔ)對忽視貸款項(xiàng)目間相關(guān)性以及貸款組合的組合數(shù)目過少的補(bǔ)償缺陷。 利用模糊模擬和遺傳算法相結(jié)合的混合智能算法解決模型求解問題。該算法打破常規(guī)，使得求得最優(yōu)解變?yōu)榭赡埽�，并�?yàn)證了算法的可行性。
[Abstract]:In the classical mean-variance theory of Markowitz (Markowitz), the assumption of return is changed from normal distribution to measure the risk in investment with the variance of return, but this assumption is often different from the real situation. The loan yield of commercial banks in our country has fuzzy uncertainty. Therefore, we assume the rate of return as a fuzzy variable, and introduce fuzzy entropy and information entropy to measure the risk degree of loan. Fuzzy entropy has the property of describing the degree of uncertainty of information. When we consider the rate of return as a random variable and take it from the normal distribution, the fuzzy entropy and variance are equivalent in measuring risk, but when we consider the rate of return as a fuzzy variable and do not agree with the normal distribution, The fuzzy entropy is more reasonable than variance in risk measurement because of the different risk grade of loan funds under different income. Fuzzy entropy improves the defect that variance depends on probability distribution and computes complexity, which is more in line with the actual situation in measuring loan risk. When using fuzzy entropy to measure the risk of a loan portfolio, because of the complex correlation between different loan projects, Therefore, if we ignore the correlation and construct the loan portfolio model solution, the decision will be concentrated in one or several high-yield loan projects, which is contrary to our idea of portfolio loan. Therefore, in order to solve this small flaw, the constraint condition of decentralized risk is added to the model to compensate for ignoring the correlation between loan projects and the small number of loan portfolio. A hybrid intelligent algorithm, which combines fuzzy simulation and genetic algorithm, is used to solve the problem of model solving. The algorithm breaks the convention and makes it possible to find the optimal solution, and verifies the feasibility of the algorithm.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F224;F830.5

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