本文關(guān)鍵詞：金融市場的風(fēng)險控制指標(biāo)和模型研究　出處：《廣西大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 投資組合優(yōu)化模型 絕對離差 CVaR 遺傳算法 粒子群算法

【摘要】：從Markowitz第一次使用方差和協(xié)方差測度風(fēng)險以來,學(xué)者們對金融市場的風(fēng)險控制指標(biāo)進(jìn)行了大量的研究.半個多世紀(jì)以來,研究者們先后提出了各種各樣的風(fēng)險控制指標(biāo),比如半方差,絕對離差,半絕對離差,在險價值,條件在險價值等.并構(gòu)建含有這些風(fēng)險控制指標(biāo)的投資組合模型,同時設(shè)計更快捷有效的計算方法.但是,無論使用哪種風(fēng)險控制指標(biāo),都存在著這樣或那樣的不足.為了彌補(bǔ)這些風(fēng)險控制指標(biāo)的不足,理論界展開了廣泛的研究和探討.論文就是對風(fēng)險控制指標(biāo)和模型做進(jìn)一步的討論,主要內(nèi)容和成果如下： 為了更好的控制風(fēng)險,文章在負(fù)絕對離差風(fēng)險控制指標(biāo)中,考慮偏離程度的波動性,構(gòu)建了含有波動性的負(fù)絕對離差風(fēng)險控制指標(biāo).并將此波動性引入正絕對離差風(fēng)險控制指標(biāo).然后通過調(diào)整因子將含有波動性的正絕對離差指標(biāo)和含有波動性的負(fù)絕對離差指標(biāo)相結(jié)合,構(gòu)建新的風(fēng)險控制指標(biāo).在此指標(biāo)下建立投資組合模型,并運(yùn)用混合遺傳算法來求解模型并給出具體算例. 給出一致性風(fēng)險控制指標(biāo)CVaR的定義,分析其數(shù)量經(jīng)濟(jì)學(xué)意義.對含有此風(fēng)險控制指標(biāo)的模型分別進(jìn)行線性化和離散化處理,建立單損失的MCVaR模型.進(jìn)一步考慮市場的摩擦因子,在單損失模型中添加改進(jìn)的典型交易成本函數(shù).然后在相關(guān)性粒子群算法中引入動態(tài)下降的慣性權(quán)重,用此改進(jìn)后的算法來求解模型并給出具體算例.
[Abstract]:Since Markowitz first used variance and covariance to measure risk, scholars have done a lot of research on the risk control index of financial market for more than half a century. Researchers have proposed a variety of risk control indicators, such as semi-variance, absolute deviation, semi-absolute deviation, at risk value. A portfolio model with these risk control indicators is constructed, and a faster and more effective calculation method is designed. However, no matter which risk control index is used. In order to make up for the shortcomings of these risk control indicators, the theoretical circle has carried out a wide range of research and discussion. This paper is to further discuss the risk control indicators and models. The main elements and outcomes are as follows: In order to control the risk better, the volatility of deviation degree is considered in the negative absolute deviation risk control index. The negative absolute deviation risk control index with volatility is constructed, and the volatility is introduced into the positive absolute deviation risk control index. Then, the positive absolute deviation index with volatility and the positive absolute deviation index with volatility are adjusted by adjusting factors. The negative absolute deviation index is combined. A new risk control index is constructed, under which a portfolio model is established, and a hybrid genetic algorithm is used to solve the model and an example is given. The definition of consistent risk control index (CVaR) is given, and its quantitative economic significance is analyzed. The model with this risk control index is linearized and discretized, respectively. The MCVaR model of single loss is established and the friction factor of the market is further considered. The improved transaction cost function is added to the single loss model, and then the dynamic decreasing inertia weight is introduced into the correlation particle swarm optimization algorithm. The improved algorithm is used to solve the model and an example is given.
【學(xué)位授予單位】：廣西大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F224;F832.5

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本文編號：1413805