本文選題：存貨質押融資 + 市場風險　； 參考：《安徽財經大學》2012年碩士論文

【摘要】：近些年來,物流與供應鏈金融創(chuàng)新活動在我國的物流業(yè)界和金融界都得到了一定的發(fā)展,它是一種關于銀行、物流企業(yè)和企業(yè)之間的業(yè)務整合的創(chuàng)新模式,它已經是生產流通中的一個潛力非常大的新的經濟增長點。而存貨質押融資業(yè)務是這種創(chuàng)新模式的最核心的形式,所以本文研究的就是存貨質押融資模式的市場風險。 本文先闡述了存貨質押融資的概念、運作模式和面臨的風險,并且對質押物樣本的對數收益率進行了統(tǒng)計描述和特征研究,結果發(fā)現數據有波動成群的特征,而且不服從正態(tài)分布和具有平穩(wěn)性的特征,這些為后面的風險度量在度量方法的選擇上提供了重要依據。在研究了存貨質押融資的市場風險之后,進一步對市場風險進行了定量研究。VaR方法是目前金融市場風險的測量中運用最普遍的方法,VaR是指在一定置信水平下,在正常市場情況下,所持有資產或者組合在未來特定持有期內的最大損失值。這種方法簡單且僅使用一個特定的數字來表示未來的風險值,因此便于管理當局分析和研究,但由于實際金融市場的復雜性,以及近幾年來頻發(fā)的國際性金融危機,單純的VaR方法已經不能充分并準確地度量市場風險了 因此在研究的方法上,本文采用分位數回歸來計算VaR值。由前面提到的VaR的定義,可以將VaR看成是金融收益分布的某一分位數。分位數回歸在進行回歸分析的時候不需要對序列的分布做出某種特定的假設；分位數回歸可以實現對于不同的分位點分別進行回歸分析,為研究提供一個更為全面的數據分析,特別是對于數據中出現異常點的擬合方面,其作用更為重要。本文在詳細討論了分位數回歸的模型,并將其運用到VaR值的計算當中。在不同的置信水平和不同的分位點上,基于分位數回歸的VaR模型對市場風險進行了度量,并得到分位數回歸的VaR模擬序列,從回歸的VaR模型可以明顯得出：市場的系統(tǒng)風險和非系統(tǒng)風險在分位數回歸的VaR模型中都得到了體現。
[Abstract]:In recent years, the innovation of logistics and supply chain finance has been developed in the logistics and financial circles of our country. It is an innovative mode of business integration among banks, logistics enterprises and enterprises.It has been a new economic growth point with great potential in production and circulation.Inventory pledge financing is the core of this innovative mode, so this paper studies the market risk of inventory pledge financing mode.In this paper, the concept, operation mode and risk of inventory pledge financing are introduced, and the logarithmic return rate of pledge sample is statistically described and studied. The results show that the data have the characteristic of fluctuating in groups.Moreover, we do not agree with normal distribution and stationary characteristics, which provide an important basis for the choice of measurement methods.After studying the market risk of inventory pledge financing, this paper makes further quantitative research on the market risk. VaR method is the most common method used in the measurement of financial market risk at present, which means that VaR is at a certain confidence level.Under normal market conditions, the maximum loss value of an asset or portfolio held in a specific future holding period.This method is simple and uses only one specific number to represent future risk values, thus facilitating management analysis and research, but due to the complexity of actual financial markets and the frequency of international financial crises in recent years,The simple VaR method is no longer sufficient and accurate to measure market riskTherefore, in this paper, the quantile regression is used to calculate the VaR value.By the definition of VaR mentioned above, VaR can be regarded as a sub-digit of the distribution of financial returns.The quantile regression does not need to make a certain hypothesis about the distribution of the sequence, and the quantile regression can carry out the regression analysis for different loci, which provides a more comprehensive data analysis for the study.Especially for the abnormal points in the data fitting, its role is more important.In this paper, the quantile regression model is discussed in detail and applied to the calculation of VaR value.At different confidence levels and different loci, the market risk was measured by VaR model based on quantile regression, and the VaR simulation sequence of quantile regression was obtained.From the regression VaR model, it is obvious that the market system risk and non-system risk are reflected in the quantile regression VaR model.
【學位授予單位】：安徽財經大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F830.91;F224

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