本文選題：極端風險　切入點：奇異分布　出處：《哈爾濱工業(yè)大學》2014年碩士論文　論文類型：學位論文

【摘要】：在金融投資活動中，既需要描繪出資產(chǎn)的收益狀況，又需要準確度量資產(chǎn)的風險狀況。金融資產(chǎn)的收益分布的研究是金融風險測量的基礎，對收益率分布形式的研究不僅有利于解決投資者的現(xiàn)實需求，而且有助于金融市場的管理。 本文嘗試將金融資產(chǎn)的極端收益率與正常分布的收益率合在一起研究，所以首先分析了該研究的可行性。通過對正常收益的波動因素，以及極端收益波動因素的分析，結(jié)合理論與實際兩方面，界定了極端風險的內(nèi)涵；同時搜集了281家上市股票在2004年到2013年的市場收益數(shù)據(jù)樣本，從實際的金融股票市場中找出樣本數(shù)據(jù)中極端風險發(fā)生的實例，共統(tǒng)計了48次極端收益率，在此基礎上提出了將金融資產(chǎn)的極端收益率與正常收益率合在一起研究的研究思路。 本文針對一般風險和極端風險分別構造了兩個密度函數(shù)，正常分布密度函數(shù)反映了一般風險，尾部分布密度函數(shù)反映極端風險，聯(lián)合得到一個新的分布，，稱之為奇異分布。根據(jù)均值——方差法，推導了該模型的數(shù)學期望與標準差，以及由極端風險引起的均值、方差以及高階矩變化的公式。并用樣本數(shù)據(jù)分析極端風險對均值方差的影響，結(jié)果表明金融資產(chǎn)的實際收益率低于正常分布的收益率，而真實風險要高于正常分布的風險。 最后，本文運用VaR方法評估奇異分布的風險水平，推導出相關的公式；分析了極端風險發(fā)生的概率這一參數(shù)對奇異分布曲線的影響；并用樣本數(shù)據(jù)計算出在不同置信度下，正常分布與奇異分布的VaR。結(jié)果顯示，在收益的尾部，相同的置信水平上，奇異分布的VaR明顯要低于正常分布的VaR，也就是投資者面臨著更大的真實風險。
[Abstract]:In the financial investment activities, it is necessary not only to depict the income situation of the assets, but also to measure the risk of the assets accurately. The study of the distribution of the income of the financial assets is the basis of the financial risk measurement. The study of yield distribution is not only helpful to solve the real demand of investors, but also helpful to the management of financial market. This paper tries to combine the extreme return rate of financial assets with the return rate of normal distribution, so the feasibility of the study is analyzed. Combined with theory and practice, this paper defines the connotation of extreme risk, collects the data samples of 281 listed stocks from 2004 to 2013, and finds out the examples of extreme risk in the sample data from the actual financial stock market. On the basis of the statistics of 48 times of extreme rate of return, this paper puts forward the research idea of combining the extreme rate of return of financial assets with the normal rate of return. In this paper, two density functions are constructed for general risk and extreme risk respectively. The normal distribution density function reflects the general risk, the tail distribution density function reflects the extreme risk, and a new distribution is obtained. Called singular distribution, the mathematical expectation and standard deviation of the model and the mean value caused by extreme risk are derived according to the mean-variance method. The influence of extreme risk on mean variance is analyzed with sample data. The results show that the real return rate of financial assets is lower than that of normal distribution, but the real risk is higher than that of normal distribution. Finally, this paper uses VaR method to evaluate the risk level of singular distribution, deduces the relevant formula, analyzes the influence of the probability of extreme risk occurrence on the singular distribution curve, and calculates the different confidence degree with the sample data. The results show that the VaR of singular distribution is obviously lower than that of normal distribution at the same confidence level at the end of the return, that is, investors are facing a greater real risk.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F224;F830.91

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