【摘要】：VaR作為一種新興的風(fēng)險(xiǎn)度量方法，較之傳統(tǒng)的風(fēng)險(xiǎn)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(diǎn)，受到風(fēng)險(xiǎn)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(jì)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(duì)象，對(duì)這些指數(shù)的收益率序列進(jìn)行了基本的統(tǒng)計(jì)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(rèn)為對(duì)這三個(gè)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(jì)算各個(gè)指數(shù)的VaR值，作了初步的模型和結(jié)果分析，同時(shí)，使用MCMC方法來估計(jì)GARCH模型的參數(shù)，并與傳統(tǒng)的極大似然估計(jì)法來比較。 在本文的最后，將多個(gè)回測方法結(jié)合在一起，從準(zhǔn)確性、保守性和有效性等三個(gè)方面來評(píng)價(jià)各模型，得出的主要結(jié)論有：(1)由于通過三個(gè)證券市場的收益率數(shù)據(jù)所計(jì)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(jì)GARCH模型的參數(shù)，在不同置信水平下，對(duì)上證180指數(shù)和恒生指數(shù)進(jìn)行VaR的計(jì)算，表明GARCH-N-MCMC模型的有效性比另外兩個(gè)模型高，在模型準(zhǔn)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(zhǔn)備金機(jī)會(huì)成本。(3) APARCH模型與GARCH模型的比較：當(dāng)置信水平較高時(shí)，一方面，APARCH模型在準(zhǔn)確性和有效性方面比GARCH模型有所提高，但同時(shí)APARCH模型也相對(duì)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(dòng)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(jì)算這三個(gè)市場的VaR值效果更好。本文對(duì)VaR的計(jì)算方法作了一些嘗試，期望能給風(fēng)險(xiǎn)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(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F830

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