【摘要】：隨著全球金融市場的蓬勃發(fā)展,新的金融產(chǎn)品和金融業(yè)務(wù)不斷出現(xiàn)。與此同時,存在于經(jīng)濟、社會和政治等各方面風險因素也在顯著增加,各個類型的金融機構(gòu)都面臨著更多的系統(tǒng)性和非系統(tǒng)性風險。百年難得一遇的全球金融危機更是加劇了人們對于金融風險的擔憂,削弱了普通民眾對于金融穩(wěn)定的信心。作為金融風險的管理者和承擔者,為了獲取相應(yīng)的超額收益,金融機構(gòu)不可能完全規(guī)避風險,只能從風險的識別、計量、檢測和控制上進行提高和改進,盡量減少不惜要的損失。在這種背景下,VaR作為一種測度資產(chǎn)組合在一定概率下最大損失值的方法,得到了越來越多的應(yīng)用 計算VaR的傳統(tǒng)方法包括分析法(方差-協(xié)方差法)、歷史模擬法和蒙特卡羅模擬法。作為一種全值估計法,蒙特卡羅模擬法計算VaR的應(yīng)用范圍十分廣泛,可以適用于非線性資產(chǎn)組合、非正態(tài)隨機分布和多維風險因子等比較復雜的就算中。但是,蒙特卡羅模擬法也存在著一些較為明顯的缺陷,如收斂速率過低和“偽隨機數(shù)”現(xiàn)象。這些缺陷不僅增大了蒙特卡羅方法的計算量,同時也降低了VaR計算的準確性。因此,本文試圖引入擬蒙特卡羅方法對其進行改進。擬蒙特卡羅方法又被稱為低差異序列方法。 在文章中,我們構(gòu)建了擬蒙特卡羅方法計算VaR的基本步驟,并以上證指數(shù)為例,在一般誤差分布對其進行擬合的基礎(chǔ)上,對其收斂性和準確性進行了實證研究。通過實證研究,我們得到了如下結(jié)論：1、股票市場收益率的分布規(guī)律具有尖峰厚尾的特征,一般誤差分布可以很好地擬合股票市場收益率的概率密度分布函數(shù)；2、在計算VaR的過程中,相比較于蒙特卡羅方法,擬蒙特卡羅方法具有較快的收斂速率；3、將VaR計算中的蒙特卡羅模擬法替換為擬蒙特卡羅模擬法后,VaR計算的準確性有了明顯的提高。擬蒙特卡羅方法在所有的置信水平下都可以通過失敗頻率檢驗法的準確性檢驗。
[Abstract]:With the vigorous development of the global financial market, new financial products and financial business are emerging. At the same time, the risk factors in economic, social and political aspects are also increasing significantly, and various types of financial institutions are facing more systemic and non-systemic risks. The once-in-a-century global financial crisis has exacerbated fears of financial risk and weakened the confidence of ordinary people in financial stability. As financial risk managers and stakeholders, in order to obtain the corresponding excess returns, financial institutions can not completely avoid risk, can only from the risk identification, measurement, detection and control to improve and improve. Try to minimize the losses at your expense. In this context, VaR, as a method to measure the maximum loss value of portfolio under certain probability, has obtained more and more traditional methods to calculate VaR, which include the analysis method (variance-covariance method). Historical simulation and Monte Carlo simulation. As a full value estimation method, Monte Carlo simulation method is widely used to calculate VaR, which can be used in complex cases such as nonlinear portfolio, non-normal random distribution and multi-dimensional risk factors. However, Monte Carlo simulation method also has some obvious defects, such as low convergence rate and "pseudorandom number" phenomenon. These defects not only increase the computational complexity of Monte Carlo method, but also reduce the accuracy of VaR calculation. Therefore, this paper attempts to introduce the quasi-Monte Carlo method to improve it. Quasi-Monte Carlo method is also called low-difference sequence method. In this paper, we construct the basic steps of quasi Monte Carlo method to calculate VaR, and take the Shanghai Stock Exchange Index as an example to study its convergence and accuracy on the basis of general error distribution fitting. Through the empirical research, we get the following conclusions: 1, the distribution law of the stock market yield has the characteristic of sharp peak and thick tail, the general error distribution can fit the probability density distribution function of the stock market rate of return well; 2. In the process of calculating VaR, compared with Monte Carlo method, the quasi-Monte Carlo method has a faster convergence rate. 3. After replacing Monte Carlo simulation method in VaR calculation with quasi Monte Carlo simulation method, the accuracy of VaR calculation is improved obviously. The quasi-Monte Carlo method can pass the accuracy test of failure frequency test at all confidence levels.
【學位授予單位】：復旦大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F224;F832.51

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