VaR的計算及其在風險管理中的應(yīng)用
發(fā)布時間：2019-02-22 10:24
【摘要】：VaR作為一種新興的風險度量方法，較之傳統(tǒng)的風險度量方法，，如情景模擬法、壓力測試法、靈敏度方法等，因其方法直觀、結(jié)果量化、易懂等特點，受到風險管理者們的青睞，也得到眾多學(xué)者的研究。 本文首先介紹了VaR的基本理論及一些傳統(tǒng)的計算方法，隨后，選擇上證180指數(shù)、深圳成份指數(shù)和香港恒生指數(shù)為研究對象，對這些指數(shù)的收益率序列進行了基本的統(tǒng)計分析，表明這些市場的收益率序列具有尖峰厚尾性和具有ARCH效應(yīng)，因此認為對這三個市場使用GARCH族模型是合適的。接著，使用GARCH模型和APARCH模型在正態(tài)分布、t分布、GED分布、偏態(tài)分布和偏態(tài)GED分布下來計算各個指數(shù)的VaR值，作了初步的模型和結(jié)果分析，同時，使用MCMC方法來估計GARCH模型的參數(shù)，并與傳統(tǒng)的極大似然估計法來比較。 在本文的最后，將多個回測方法結(jié)合在一起，從準確性、保守性和有效性等三個方面來評價各模型，得出的主要結(jié)論有：(1)由于通過三個證券市場的收益率數(shù)據(jù)所計算的APARCH模型的參數(shù)1均非零，且為正值，說明上海、深圳和香港證券市場都存在明顯的“杠桿效應(yīng)”。(2)利用MCMC方法來估計GARCH模型的參數(shù)，在不同置信水平下，對上證180指數(shù)和恒生指數(shù)進行VaR的計算，表明GARCH-N-MCMC模型的有效性比另外兩個模型高，在模型準確性得到保證的前提下，使用該方法，可以讓投資者有最小的準備金機會成本。(3) APARCH模型與GARCH模型的比較：當置信水平較高時，一方面，APARCH模型在準確性和有效性方面比GARCH模型有所提高，但同時APARCH模型也相對保守些；另一方面，基于偏態(tài)廣義誤差分布的APARCH模型更能捕捉到金融市場的各種特性，如波動聚集性，尖峰厚尾性等，且APARCH模型較GARCH模型更有效，采用APARCH模型結(jié)合偏態(tài)分布來分析計算這三個市場的VaR值效果更好。本文對VaR的計算方法作了一些嘗試，期望能給風險管理者提供一些決策支撐。
[Abstract]:Compared with the traditional risk measurement methods, such as scenario simulation, stress test and sensitivity, VaR, as a new risk measurement method, is favored by risk managers because of its intuitive, quantitative and understandable characteristics. It has also been studied by many scholars. This paper first introduces the basic theory of VaR and some traditional calculation methods, then selects the Shanghai Stock Exchange 180 Index, Shenzhen component Index and Hong Kong Hang Seng Index as the research object, carries on the basic statistical analysis to these index return series. It is shown that the yield series of these markets have spikes and thick tails and have ARCH effect. Therefore, it is considered appropriate to use the GARCH family model for these three markets. Then, the GARCH model and APARCH model are used to calculate the VaR values of each index in normal distribution, t distribution, GED distribution, skew distribution and skewness GED distribution. The parameters of GARCH model are estimated by MCMC method and compared with the traditional maximum likelihood estimation method. At the end of this paper, several methods are combined to evaluate the models from three aspects: accuracy, conservatism and effectiveness. The main conclusions are as follows: (1) the parameters of the APARCH model calculated through the return data of the three securities markets are all non-zero and positive, indicating Shanghai. There are obvious "leverage effects" in both Shenzhen and Hong Kong stock markets. (2) the parameters of GARCH model are estimated by MCMC method, and the VaR calculations of Shanghai Stock Exchange 180 Index and Hang Seng Index are carried out under different confidence levels. It is shown that the GARCH-N-MCMC model is more effective than the other two models, and this method is used to ensure the accuracy of the model. (3) the comparison between APARCH model and GARCH model: when the confidence level is high, on the one hand, the accuracy and validity of APARCH model are higher than that of GARCH model. At the same time, the APARCH model is more conservative. On the other hand, the APARCH model based on skew generalized error distribution can capture the characteristics of financial market, such as volatility aggregation, peak and tail, and APARCH model is more effective than GARCH model. It is better to use APARCH model and skew distribution to analyze and calculate the VaR value of these three markets. In this paper, some attempts are made on the calculation method of VaR, which is expected to provide some decision support for risk managers.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F224;F830
本文編號：2428123
[Abstract]:Compared with the traditional risk measurement methods, such as scenario simulation, stress test and sensitivity, VaR, as a new risk measurement method, is favored by risk managers because of its intuitive, quantitative and understandable characteristics. It has also been studied by many scholars. This paper first introduces the basic theory of VaR and some traditional calculation methods, then selects the Shanghai Stock Exchange 180 Index, Shenzhen component Index and Hong Kong Hang Seng Index as the research object, carries on the basic statistical analysis to these index return series. It is shown that the yield series of these markets have spikes and thick tails and have ARCH effect. Therefore, it is considered appropriate to use the GARCH family model for these three markets. Then, the GARCH model and APARCH model are used to calculate the VaR values of each index in normal distribution, t distribution, GED distribution, skew distribution and skewness GED distribution. The parameters of GARCH model are estimated by MCMC method and compared with the traditional maximum likelihood estimation method. At the end of this paper, several methods are combined to evaluate the models from three aspects: accuracy, conservatism and effectiveness. The main conclusions are as follows: (1) the parameters of the APARCH model calculated through the return data of the three securities markets are all non-zero and positive, indicating Shanghai. There are obvious "leverage effects" in both Shenzhen and Hong Kong stock markets. (2) the parameters of GARCH model are estimated by MCMC method, and the VaR calculations of Shanghai Stock Exchange 180 Index and Hang Seng Index are carried out under different confidence levels. It is shown that the GARCH-N-MCMC model is more effective than the other two models, and this method is used to ensure the accuracy of the model. (3) the comparison between APARCH model and GARCH model: when the confidence level is high, on the one hand, the accuracy and validity of APARCH model are higher than that of GARCH model. At the same time, the APARCH model is more conservative. On the other hand, the APARCH model based on skew generalized error distribution can capture the characteristics of financial market, such as volatility aggregation, peak and tail, and APARCH model is more effective than GARCH model. It is better to use APARCH model and skew distribution to analyze and calculate the VaR value of these three markets. In this paper, some attempts are made on the calculation method of VaR, which is expected to provide some decision support for risk managers.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F224;F830
【參考文獻】
相關(guān)期刊論文 前10條
1 薛宏剛,徐成賢,李三平,苗寶山;金融風險管理的VaR方法及實證分析[J];工程數(shù)學(xué)學(xué)報;2004年06期
2 鄭文通;金融風險管理的VAR方法及其應(yīng)用[J];國際金融研究;1997年09期
3 尹優(yōu)平,馬丹;基于分布擬合方法的高頻數(shù)據(jù)風險價值研究[J];金融研究;2005年03期
4 劉子斐;史敬;;VaR模型比較技術(shù)及其評價——理論、實證回顧及其應(yīng)用初探[J];金融研究;2008年05期
5 趙國慶;劉慶豐;;基于混合模型的上海股票市場VaR研究[J];金融研究;2009年11期
6 江濤;;基于GARCH與半?yún)?shù)法VaR模型的證券市場風險的度量和分析:來自中國上海股票市場的經(jīng)驗證據(jù)[J];金融研究;2010年06期
7 肖春來,宋然;VaR理論及其應(yīng)用研究[J];數(shù)理統(tǒng)計與管理;2003年02期
8 肖春來,柴文義,章月;基于經(jīng)驗分布的條件VaR計算方法研究[J];數(shù)理統(tǒng)計與管理;2005年05期
9 葉五一;繆柏其;吳振翔;;基于收益率修正分布的VaR估計[J];數(shù)理統(tǒng)計與管理;2007年05期
10 鐘波;汪青松;;基于Bayes估計的金融風險值——VaR計算[J];數(shù)理統(tǒng)計與管理;2007年05期
相關(guān)碩士學(xué)位論文 前1條
1 史敬;各類VaR方法的比較：基于中國股市的實證研究[D];湖南大學(xué);2005年
本文編號：2428123
本文鏈接：http://www.sikaile.net/guanlilunwen/huobilw/2428123.html
最近更新
教材專著
