本文選題：同業(yè)拆借市場 + VaR�。� 參考：《西南財經(jīng)大學(xué)》2012年碩士論文

【摘要】：在現(xiàn)代經(jīng)濟環(huán)境中,金融處于一個十分重要的位置,而商業(yè)銀行在其中發(fā)揮著至關(guān)重要的作用,如何透過商業(yè)銀行的視角對整體金融風(fēng)險有一定程度的控制,不僅是商業(yè)銀行關(guān)注的問題,更是監(jiān)管當(dāng)局和學(xué)術(shù)界研究的重點問題。在我國商業(yè)銀行的整體風(fēng)險管控中,利率風(fēng)險預(yù)防和控制是首個突出并且應(yīng)當(dāng)重視的問題。由于國際上利率風(fēng)險控制浪潮的推進(jìn),以及我國倡導(dǎo)利率市場化的步伐加快,中資銀行在面臨全球金融機構(gòu)的挑戰(zhàn),如何對商業(yè)銀行的風(fēng)險進(jìn)行有效的度量和管理研究,已經(jīng)是商業(yè)銀行管理面臨的一大重要課題。商業(yè)銀行利率風(fēng)險研究是一個成熟而又新穎的課題,隨著國際經(jīng)濟環(huán)境下對金融市場管制的逐步放松,出現(xiàn)了大規(guī)模的金融產(chǎn)品創(chuàng)新,使得金融市場波動日益頻繁,金融機構(gòu)由過去的市場資源化探索向內(nèi)部管理和創(chuàng)新方式改革轉(zhuǎn)變,在金融機構(gòu)經(jīng)營管理模式漸漸發(fā)生改變的同時,有關(guān)利率風(fēng)險方面的預(yù)防和控制度量模型理論和實踐也在不斷探索與創(chuàng)新中。VaR模型在這一背景下發(fā)展起來并受到廣泛認(rèn)同,已成為西方各國公認(rèn)的市場風(fēng)險度量與管理工具之一。 受當(dāng)前世界經(jīng)濟環(huán)境和我國金融市場發(fā)展的影響,中國對商業(yè)銀行利率風(fēng)險度量的重視程度也在逐漸加強,對利率風(fēng)險的關(guān)注和利率風(fēng)險預(yù)防和控制的技術(shù)引入,甚至利率風(fēng)險的體制問題都有所改善和進(jìn)步。但是總的來看,隨著利率市場化波動的愈發(fā)明顯,我國商業(yè)銀行的計量風(fēng)險技術(shù)還不能滿足市場風(fēng)險正在逐步變大的形勢,這與發(fā)達(dá)國家的商業(yè)銀行風(fēng)險度量水平還存在一定的距離。當(dāng)前我國大部分商業(yè)銀行少有運用比傳統(tǒng)的風(fēng)險度量方法更為精確的VaR模型法進(jìn)行利率風(fēng)險的度量,這與國際上通行的先進(jìn)計量方法存在著較大差距,因此通過建立合適的VaR模型對我國商業(yè)銀行利率風(fēng)險進(jìn)行度量和監(jiān)管,具有重要的意義,因此,本文基于我國銀行同業(yè)拆借市場中的隔夜拆借利率進(jìn)行實證分析,希望在利率風(fēng)險的理論和實踐分析上能結(jié)果我國國情有所創(chuàng)新,能為我國從利率風(fēng)險的理論、模型建立以及監(jiān)管體系方面的監(jiān)管擴展做出實際的學(xué)術(shù)貢獻(xiàn)。 在對利率風(fēng)險相關(guān)課程的學(xué)習(xí)以及論文文獻(xiàn)的研讀中,了解到利率風(fēng)險是指非預(yù)期的市場利率變化導(dǎo)致的對商業(yè)銀行表內(nèi)和表外頭寸的影響,會對商業(yè)銀行的資產(chǎn)價值及收益產(chǎn)生直接的影響。其中,在對利率風(fēng)險度量的方法中,VaR作為一種新興的模型正在被世界各地的風(fēng)險監(jiān)管當(dāng)局和金融機構(gòu)所推祟。VaR (Value at Risk)也叫在險價值,是一種利用統(tǒng)計方法計量風(fēng)險價值的度量方法�，F(xiàn)在,VaR模型己經(jīng)成為了很多國家的金融風(fēng)險管理標(biāo)準(zhǔn),并且將其作為分析工具監(jiān)管金融機構(gòu)風(fēng)險的重要工具,其動態(tài)監(jiān)測和量化監(jiān)管的特點受到金融監(jiān)管當(dāng)局和金融機構(gòu)的認(rèn)同與歡迎。 本文第三章對商業(yè)銀行利率風(fēng)險的基本概念進(jìn)行了理解和闡述,其中包括闡述商業(yè)銀行利率風(fēng)險的定義,以及對商業(yè)銀行利率風(fēng)險種類的了解。在對利率風(fēng)險清晰定義的基礎(chǔ)上,進(jìn)一步深入了解商業(yè)銀行利率風(fēng)險的分類共有四種,包括重新定價風(fēng)險、基準(zhǔn)風(fēng)險、收益率曲線風(fēng)險和隱含期權(quán)風(fēng)險,并對四種利率風(fēng)險進(jìn)行了詳盡的定義和概念的詮釋。對商業(yè)銀行利率風(fēng)險的清晰定義和了解有助于正確選擇合適的風(fēng)險度量工具,合理的規(guī)避風(fēng)險。第二部分主要闡述商業(yè)銀行利率風(fēng)險度量方法的演進(jìn)歷程,正確認(rèn)識和選擇利率風(fēng)險的度量方法是實現(xiàn)利率風(fēng)險科學(xué)管理的重要前提,因此對商業(yè)銀行利率風(fēng)險度量工具的演變過程了解十分必要。在對傳統(tǒng)的兩種利率風(fēng)險度量方法,即利率敏感性缺口分析和持續(xù)期模型的了解后,總結(jié)出靜態(tài)的風(fēng)險度量方法的優(yōu)點在于簡潔易懂,且易于操作和計算,但是都不能全面的反應(yīng)賬戶中的綜合交易情況。VaR相對于傳統(tǒng)的方法,能更為全面的度量綜合復(fù)雜的銀行利率風(fēng)險,因此受到廣大風(fēng)險監(jiān)管者和金融機構(gòu)的推廣。總的來說,VaR方法可以分為參數(shù)法、非參數(shù)法以及半?yún)?shù)法。該部分中主要介紹了GARCH族模型和基于Risk Metrics的混合正態(tài)分布兩種參數(shù)方法,以及歷史模擬法和蒙特卡羅模擬法兩種非參數(shù)方法。在對方法了解的基礎(chǔ)上,擬選用GARCH族模型和基于Risk Metrics的混合正態(tài)分布兩種參數(shù)方法對上海銀行間同業(yè)拆借市場的隔夜拆借利率進(jìn)行VaR度量的實證分析。 第四章主要對上海銀行間同業(yè)拆借市場的利率數(shù)據(jù)進(jìn)行進(jìn)行統(tǒng)計特征分析,由于SHIBOR比CHIBOR更為符合市場波動規(guī)律,市場化自由程度更廣且隔夜拆借利率數(shù)據(jù)最為頻繁,因此選擇上海銀行間同業(yè)拆借利率數(shù)據(jù)進(jìn)行實證分析,并對其做收益率對數(shù)處理。在對SHIOBR對數(shù)收益率數(shù)據(jù)的統(tǒng)計特征分析中,可以得出以下結(jié)論：SHIBOR對數(shù)收益率數(shù)據(jù)并不服從正態(tài)分布,具有明顯的尖峰厚尾特征,該樣本是平穩(wěn)的時間序列數(shù)據(jù),并且存在自相關(guān)性。在對SHIBOR對數(shù)收益率的條件異方差檢驗中發(fā)現(xiàn),樣本序列的殘差序列存在明顯的ARCH效應(yīng),應(yīng)該可以用于建立ARMA-GARCH模型。在第五章中,用GARCH族模型來對SHIBOR對數(shù)收益率數(shù)據(jù)進(jìn)行利率風(fēng)險度量的實證分析。在對GARCH族模型基本思想了解的基礎(chǔ)上,首先確定了SHIBOR樣本序列的自回歸移動平均模型為AR(1),并得出結(jié)論：AR(1)基本滿足平穩(wěn)的要求,且不存在序列相關(guān)。在此基礎(chǔ)上,選擇了GARCH、T-GARCH、E-GARCH這三種GARCH族模型的形式來擬合模型的條件異方差,在對殘差分布的選擇中,不僅使用正態(tài)分布形式,還選擇了T分布以及廣義誤差分布分別進(jìn)行擬合,選擇了18種GARCH模型形式對比其AIC值,其中GARCH(1,1)-T、GARCH(1,2)-T、 GARCH(1,1)-G、GARCH(1,2)-G、TGARCH(1,1)-G、TGARCH(1,2)-G EGARCH(1,1)-T、EGARCH (1,2)-T、EGARCH (1,1)-G、EGARCH (1,2)-G模型的AIC和SC值較小,然后對上述選擇的模型進(jìn)行參數(shù)顯著性檢驗,剔除系數(shù)不顯著及效果不優(yōu)的模型,選擇EGARCH(1,2)-G進(jìn)行VaR值的計算。第三部分對EGARCH(1,2)-G的VaR值進(jìn)行回測檢驗,該模型在95%和99%的置信水平下沒有通過檢驗,說明模型對于風(fēng)險的估計過于保守,或者可能因為數(shù)據(jù)樣本量不夠,但在99%的置信水平下擬合效果較好,從一定程度上能刻畫SHIBOR收益率的尖峰厚尾特征。接下來,本文使用參數(shù)法中的另一個種方法—基于Risk Metrics的混合正態(tài)分布來對SHIBOR對數(shù)收益率數(shù)據(jù)進(jìn)行擬合,在假定上海銀行間同業(yè)拆借利率對數(shù)收益率服從雙正態(tài)混合分布的前提下,取日收益率衰減因子為0.94,利用Matlab進(jìn)行EM迭代,最后求得混合正態(tài)分布密度函數(shù)的參數(shù)估計值。通過標(biāo)準(zhǔn)化的SHIBOR對數(shù)收益率直方圖可以發(fā)現(xiàn),數(shù)據(jù)的尖峰厚尾信息特征較為明顯,特別是尖峰信息特征,因此可以得出結(jié)論,混合正態(tài)分布相較于正態(tài)分布來說,擬合效果較好,在VaR值的計算上也能獲得更高的精度。從混合正態(tài)分布VaR值的回測檢驗來看,在置信水平為99%、95%、90%三種情況下,LR統(tǒng)計量均小于臨界值,模型通過回測檢驗,說明混合正態(tài)分布情況下,其實際失敗天數(shù)與期望失敗天數(shù)非常接近,該模型所測算的VaR值精確度較高,是一種值得推廣的VaR度量方法。通過對GARCH族模型和基于Risk-Metrics的混合正態(tài)分布模型法的實證結(jié)果對比分析來看,首先,兩種方法都是利用同一組數(shù)據(jù)進(jìn)行實證分析,因此分析的結(jié)果可以直接進(jìn)行對比,來判斷方法不同所帶來VaR值結(jié)果及其精準(zhǔn)度不同的影響；其次,兩種方法都是基于參數(shù)估計的基礎(chǔ)得以實現(xiàn),都屬于參數(shù)法的范疇,可以對比其同種方法范疇下的不同之處,對方法的選擇對比具有實際意義；最后,兩種估計法在軟件上能較為快速的實現(xiàn),GARCH族模型通過Eviews軟件來實現(xiàn),而混合正態(tài)分布則需要通過Matlab進(jìn)行編程計算,其計算速度并無差別,都能在軟件中短時間顯示結(jié)果。然而,兩種實證分析方法的不同之處能體現(xiàn)在模型理論理解難易程度、軟件實現(xiàn)難易程序、計算復(fù)雜程度、方法推廣程度、計算結(jié)果精準(zhǔn)度這五個方面的不同。最后,本文針對VaR模型的兩種方法實證分析,得出以下結(jié)論：第一,在GARCH族模型的實證研究中,針對上海銀行間同業(yè)拆借利率數(shù)據(jù),經(jīng)過一系列測算,最終選擇EGARCH(1,2)-G模型為風(fēng)險度量模型,利用EGARCH(1,2)-G擬合的的方差預(yù)測值對其分別求出每日動態(tài)VaR值。第二,在基于混合正態(tài)分布方法的擬合中,本文發(fā)現(xiàn)混合正態(tài)分布對金融時間序列尖峰厚尾特征的描述較為貼切,且能較為靈活的調(diào)整雙正態(tài)分布各自擬合比例,最終得出較為穩(wěn)定的參數(shù)估計值。第二,EGARCH(1,2)-G只有在99%的置信水平下通過Kupiec檢驗,而混合正態(tài)分布方法在三種置信水平下均通過檢驗。第四,對于極端風(fēng)險情況的處理,GARCH族模型比混合正態(tài)分布模型更優(yōu),同時,混合正態(tài)分布擬合的每日VaR值更接近風(fēng)險平均水平,因此對總體水平上的風(fēng)險度量更為優(yōu)化,是另一種值得考慮使用的風(fēng)險度量方法。
[Abstract]:In the modern economic environment, finance is in a very important position, and commercial banks play a vital role in it. How to control the overall financial risk to a certain extent through the perspective of commercial banks is not only a concern of commercial banks, but also a key issue in the research of the regulatory authorities and academia. In the overall risk control of commercial banks, the prevention and control of interest rate risk is the first prominent and important problem. Because of the promotion of the wave of interest rate risk control in the world and the quickening pace of our country's advocacy of interest rate marketization, the Chinese banks are facing the challenges of global financial institutions and how to make the risk of commercial banks effective. The research on measurement and management has been an important subject in the management of commercial banks. The interest rate risk study of commercial banks is a mature and novel topic. With the gradual relaxation of the financial market regulation under the international economic environment, a large scale of financial product innovation has emerged, which makes the financial market fluctuating more and more frequently, and the financial institutions are becoming more and more frequent. From the past market resource exploration to the reform of internal management and innovation mode, while the management model of financial institutions is changing gradually, the theory and practice of the prevention and control measurement model of interest rate risk are also developed and widely recognized in this context in the continuous exploration and innovation of the.VaR model. It has been recognized as one of the tools of market risk measurement and management in western countries.
Influenced by the current world economic environment and the development of China's financial market, China's attention to the interest rate risk measurement of commercial banks is gradually strengthened. The interest rate risk, the introduction of interest rate risk prevention and control technology, and the institutional problems of interest rate risk are improved and progressed. However, in general, with the interest rate market As the fluctuation of the field becomes more and more obvious, the measurement risk technology of the commercial banks in our country can not meet the situation that the market risk is becoming bigger and bigger. There is a certain distance between the risk measurement of the commercial banks in the developed countries. At present, most of the commercial banks in our country have less precise VaR model using the risk measurement method more than traditional. There is a big gap between the measurement of interest rate risk and the advanced measurement methods prevailing in the world. Therefore, it is of great significance to measure and supervise the interest rate risk of commercial banks in China by establishing a suitable VaR model. Therefore, this paper is based on the empirical analysis of the overnight lending rate in the interbank lending market of China. It is hoped that the theoretical and practical analysis of interest rate risk can result in the innovation of our country's conditions, and can make a practical contribution to our country from the theory of interest rate risk, the establishment of model and supervision system.
In the study of interest rate risk related courses and the study of paper literature, it is understood that the interest rate risk is the effect of the unexpected market interest rate changes on the balance of the commercial bank's balance in the balance sheet and out of the table, which will have a direct impact on the value and income of the commercial banks. In the method of measuring the interest rate risk, VaR is used as a method. A new model is being called the value of.VaR (Value at Risk) by risk regulatory authorities and financial institutions all over the world. It is a measure of measuring the value of risk by statistical methods. Now, the VaR model has become a financial risk management standard in many countries and is used as an analytical tool to monitor the risk. The importance of dynamic monitoring and quantitative regulation is the recognition and welcome of financial regulatory authorities and financial institutions.
In the third chapter, the basic concepts of the interest rate risk of commercial banks are understood and expounded, including the definition of the interest rate risk of commercial banks and the understanding of the types of interest rate risks in commercial banks. On the basis of a clear definition of interest rate risk, there are four kinds of classification of interest rate risks in commercial banks, including the classification of interest risk. Re pricing risk, benchmark risk, yield curve risk and implied option risk, and a detailed definition and interpretation of four kinds of interest rate risks. A clear definition and understanding of the interest rate risk of commercial banks is helpful for the correct choice of appropriate risk measurement tools and reasonable avoidance of risk. The second part mainly describes commercial banks. It is necessary to understand and select the measure method of interest rate risk correctly to realize the scientific management of interest rate risk. Therefore, it is necessary to understand the evolution process of the interest rate risk measurement tool for commercial banks. In the traditional two interest rate risk measurement methods, that is, the interest rate sensitivity gap analysis and the analysis of the interest rate risk. After the understanding of the duration model, it is concluded that the advantages of the static risk measurement method are simple and easy to understand, and easy to operate and calculate, but the comprehensive transaction situation.VaR in the comprehensive response account can be more comprehensive to measure the complex bank interest rate risk compared with the traditional method, so it is subject to the risk supervisor. In general, the VaR method can be divided into parameter method, non parametric method and semi parametric method. In this part, we mainly introduce two parameter methods of GARCH model and mixed normal distribution based on Risk Metrics, and two non parametric methods of historical simulation method and Monte Carlo simulation method. The GARCH model and the mixed normal distribution based on Risk Metrics are selected to make an empirical analysis on the VaR measurement of the overnight lending rate of interbank interbank lending market in Shanghai.
The fourth chapter mainly analyzes the statistical characteristics of the interest rate data in the interbank lending market in Shanghai. Because SHIBOR is more in line with the law of market volatility than CHIBOR, the liberalization of the market is more extensive and the interest rate data is the most frequent. Therefore, the data of interbank lending rate in Shanghai is selected for empirical analysis, and it is done to it. In the analysis of the statistical characteristics of the SHIOBR log return data, we can draw the following conclusion: the SHIBOR logarithmic yield data does not obey the normal distribution, and has the obvious peak and thick tail features. The sample is a stationary time series data, and there is a self correlation. The condition of the SHIBOR logarithmic return rate is different. In the variance test, it is found that the residual sequence of the sample sequence has an obvious ARCH effect and should be used to establish the ARMA-GARCH model. In the fifth chapter, the GARCH model is used to carry out an empirical analysis of the rate risk measurement of the SHIBOR logarithmic return data. On the basis of the basic idea of the GARCH family model, the SHIBOR sample is first determined. The autoregressive moving average model of this sequence is AR (1), and draws the conclusion that AR (1) basically satisfies the requirement of stationary and does not have sequence correlation. On this basis, the conditional heteroscedasticity of the model is fitted with the form of three GARCH family models of GARCH, T-GARCH and E-GARCH, and not only the normal distribution is used in the selection of residual distribution, but also in the selection of residual distribution. The T distribution and the generalized error distribution are selected, and 18 GARCH models are selected to compare their AIC values, including GARCH (1,1) -T, GARCH (1,2) -T, GARCH (1,1). EGARCH (1,2) -G is selected to calculate the value of VaR by selecting the model that is not significant and the effect is not good. The third part of the model is to test the VaR value of EGARCH (1,2) -G, and the model is not tested under the confidence level of 95% and 99%, indicating that the model is too conservative for the risk estimation, or possible. Because the data sample is not enough, the fitting effect is better under the 99% confidence level, and it can describe the peak and thick tail characteristics of the SHIBOR yield to a certain extent. Next, this paper uses a hybrid normal distribution of Risk Metrics based on the mixed normal distribution of the parameter method to fit the data of the logarithmic return of the SHIBOR, in the assumption of Shanghai silver. Under the premise that the rate of return on interbank lending rate obeys the mixed distribution of double normal state, the attenuation factor of daily return rate is 0.94, and EM iteration is carried out by Matlab. Finally, the parameter estimation of the mixed normal distribution density function is obtained. Through the standardized SHIBOR logarithmic return rate direct graph, the information features of the peak and thick tail of the data are compared. For obvious, especially peak information characteristics, it can be concluded that the mixed normal distribution is better than the Yu Zheng state distribution, and can also obtain higher precision in the calculation of the VaR value. From the back test of the VaR value of the mixed normal distribution, the LR statistics are less than the critical in the 99%, 95%, and 90% confidence levels. The model shows that the actual failure days of the mixed normal distribution are very close to the expected number of failed days. The VaR value calculated by the model is high, and it is a kind of VaR measure worth popularizing. By comparing the empirical results of the GARCH model and the mixed normal distribution model based on Risk-Metrics. In the first place, first of all, the two methods use the same group of data for empirical analysis, so the results of the analysis can be compared directly to judge the effect of different VaR results and their accuracy. Secondly, the two methods are based on the base of parameter estimation, all of which belong to the category of parameter method. Compared with the same method category, the selection and contrast of the method has practical significance. At last, the two estimation methods can be realized more quickly on the software, the GARCH model is realized through the Eviews software, and the mixed normal distribution needs to be programmed by Matlab, and the calculation speed is not different and can be short in the software. Time shows the results. However, the difference between the two empirical methods can be reflected in the difficulty of understanding the model theory, the difficulty of software implementation, the complexity of calculation, the degree of popularization of the method and the accuracy of the calculation results. Finally, the following conclusions are drawn to the following conclusions for the two methods of the VaR model: the following conclusions: First, in the empirical study of the GARCH model, according to the interbank interbank interest rate data in Shanghai, after a series of calculations, the EGARCH (1,2) -G model is selected as the risk measurement model, and the daily dynamic VaR value is obtained by using the variance prediction value fitted by EGARCH (1,2) -G. Second, in the fitting based on the mixed normal distribution method, It is found that the mixed normal distribution is more appropriate for the description of the characteristics of the peak and thick tail of the financial time series, and it can be more flexible to adjust the fit ratio of the double normal distribution, and finally get a more stable parameter estimate. Second, EGARCH (1,2) -G is only through the Kupiec test under the confidence level of 99%, and the mixed normal distribution method is three. Fourth, the GARCH family model is better than the mixed normal distribution model for the treatment of extreme risk. At the same time, the daily VaR value of the mixed normal distribution is closer to the risk average, so the risk measurement on the overall level is better. It is another kind of risk measure worthy of consideration. Law.

【學(xué)位授予單位】：西南財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F224;F832.33;F822

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