基于APARCH和POT模型的上證綜指風(fēng)險(xiǎn)度量
發(fā)布時(shí)間：2018-11-04 16:46
【摘要】：近年來(lái)金融市場(chǎng)迅猛發(fā)展的同時(shí)也伴隨著波動(dòng)性增強(qiáng),并致使許多金融危機(jī)事件頻繁發(fā)生。金融監(jiān)管機(jī)構(gòu)和眾多的投資者因此也增加了對(duì)市場(chǎng)風(fēng)險(xiǎn)的關(guān)注度。如何尋找到一種更精準(zhǔn)的度量工具成為了當(dāng)前度量市場(chǎng)風(fēng)險(xiǎn)的首要問(wèn)題,風(fēng)險(xiǎn)價(jià)值(VaR)作為目前最普遍的風(fēng)險(xiǎn)度量工具,它表示在一定的置信水平下,資產(chǎn)或資產(chǎn)組合在未來(lái)某一特定時(shí)間內(nèi)的最大可能損失,也即是給定顯著性水平資產(chǎn)組合收益損失分布函數(shù)的分位數(shù)點(diǎn)。 傳統(tǒng)VaR中假定收益率服從某一特定分布,鑒于此極值理論中POT模型逐漸成為VaR估計(jì)的主流方式之一。這是因?yàn)镻OT模型只需要研究收益率序列的尾部特征,利用GPD分布來(lái)擬合尾部分布。本文在POT模型基礎(chǔ)上引入APARCH模型,將二者結(jié)合研究上證市場(chǎng)風(fēng)險(xiǎn),這在當(dāng)前主流觀點(diǎn)認(rèn)為關(guān)注極端VaR的尾部風(fēng)險(xiǎn)比關(guān)注正常情況下的風(fēng)險(xiǎn)更有實(shí)際意義的背景下,本文對(duì)如何有效準(zhǔn)確地度量市場(chǎng)風(fēng)險(xiǎn)進(jìn)行了有益探索。 本文以上證指數(shù)1990年12月19日-2012年3月19日日收盤價(jià)為原始數(shù)據(jù),在借鑒吸收前人成功的研究成果同時(shí),采用實(shí)證分析方法對(duì)上海證股票市場(chǎng)風(fēng)險(xiǎn)進(jìn)行分析,并比較研究了不同置信水平下VaR估計(jì)值。 首先,本文對(duì)收益率序列的統(tǒng)計(jì)性特征進(jìn)行了描述,以便為選擇合理的VaR估計(jì)模型。通過(guò)正態(tài)性、自相關(guān)性以及ARCH效應(yīng)的檢驗(yàn),本文發(fā)現(xiàn)我國(guó)股市收益率序列具有尖峰厚尾性,弱自相關(guān)性,波動(dòng)集聚性。 其次,本文采用APARCH模型捕捉收益率序列的自相關(guān)和異方差現(xiàn)象,并采用極大似然法估計(jì)模型參數(shù),因?yàn)闃O大似然估計(jì)需假設(shè)殘差的分布,故而使用GMM估計(jì)對(duì)MLE估計(jì)結(jié)果進(jìn)行校正。最終獲得近似獨(dú)立同分布的殘差序列。再利用POT模型對(duì)經(jīng)過(guò)ARARCH模型篩選過(guò)的殘差進(jìn)行極值分析,并根據(jù)VaR的可加性計(jì)算出收益率序列在不同置信水平下的VaR。 為了比較研究,本文使用一般POT模型估計(jì)了上證綜指的VaR,由于一般POT模型不能避免超出量序列的相關(guān)性,故而可能會(huì)高估真實(shí)的市場(chǎng)風(fēng)險(xiǎn)。通過(guò)比較不同置信水平下兩模型VaR的值,可以得出以下結(jié)論： 1.在不同的顯著性水平下,一般的POT模型估計(jì)出的均VaR高于APARCH-POT模型估計(jì)出的VaR值,這表明一般的POT模型的確高估了市場(chǎng)風(fēng)險(xiǎn),且經(jīng)APARCH—POT模型的估計(jì)結(jié)果更保守,這也大大了提高尾部分布VaR的穩(wěn)定性。 2.本文通過(guò)使用Kupiec失敗返回檢驗(yàn)法對(duì)各VaR值進(jìn)行了有效性檢驗(yàn)并發(fā)現(xiàn)：POT模型和經(jīng)APARCH過(guò)濾后的POT模型在95%、99%的置信水平上Kupiec檢驗(yàn)結(jié)果均是有效的。但在99%置信水平上POT模型檢驗(yàn)效果更好。 3.通過(guò)對(duì)使用APARCH過(guò)濾后的POT模型進(jìn)行動(dòng)態(tài)建模得到了整個(gè)樣本期內(nèi)的VaR值。根據(jù)樣本期間的VaR分布可以得出以下結(jié)論：樣本期的VaR按集聚特征分為五個(gè)區(qū)間,其中1990-1994年,滬市建立之初,政府對(duì)股市的態(tài)度決定了此時(shí)市場(chǎng)風(fēng)險(xiǎn),風(fēng)險(xiǎn)值最大；1995-1999年,隨著政府對(duì)發(fā)展股市信心的堅(jiān)定,市場(chǎng)風(fēng)險(xiǎn)開始回落,但整體風(fēng)險(xiǎn)仍較大；2000-2006年,隨著制度的成熟和機(jī)構(gòu)投資者的進(jìn)入,市場(chǎng)風(fēng)險(xiǎn)進(jìn)入平緩期,風(fēng)險(xiǎn)值相對(duì)較低；2006-2010年頻繁的經(jīng)濟(jì)刺激政策推高市場(chǎng)風(fēng)險(xiǎn)；2010-至今,政策打壓下投機(jī)泡沫減少,上個(gè)區(qū)間段風(fēng)險(xiǎn)再次回落。 本文的主要?jiǎng)?chuàng)新點(diǎn)在于使用APARCH模型對(duì)過(guò)濾收益率序列建模時(shí),對(duì)比分析了擾動(dòng)項(xiàng)服從不同分布時(shí)APARCH模型捕捉收益率特征的有效性。另外,本文采用APARCH模型和POT模型相結(jié)合的方式擬合收益率,避免了傳統(tǒng)POT模型由于數(shù)據(jù)相關(guān)而造成VaR高估的問(wèn)題。
[Abstract]:With the rapid development of financial markets in recent years, the volatility has been accompanied by the frequent occurrence of many financial crisis events. Financial regulators and many investors have therefore increased attention to market risks. How to find a more precise measurement tool has become the primary problem of the current measure market risk, and the risk value (VaR) is the most common risk measure tool at present, which means that at a certain confidence level, The maximum possible loss of a portfolio of assets or assets over a certain period of time, i.e., a fractional number of digits of the distribution function for the combined benefit of a given significance level asset portfolio. In the traditional VaR, we assume that the yield is subject to a certain distribution. In view of this extreme value theory, the POT model gradually becomes the mainstream of VaR estimation. This is because the POT model only needs to study the tail feature of the yield sequence and use the GPD distribution to fit the tail. In this paper, we introduce the APARCH model on the basis of the POT model, and combine them in the research of the market risk, which in the current mainstream view is concerned that the tail risk of extreme VaR is more meaningful than the risk of paying attention to the normal situation. Under the scene, this article is helpful for how to measure market risk accurately and accurately In this paper, from December 19, 1990 to March 19, 2012 as raw data, the paper analyses the risk of Shanghai Shanghai Stock Market by using the empirical analysis method, and compares the V at different confidence levels. First of all, this paper describes the statistical characteristics of the yield sequence, so as to select reasonable price. By means of positive state, self-correlation and ARCH effect, this paper finds that the stock market rate of return on stock market has peak-thickness tail, weak self-phase, Secondly, we use the APARCH model to capture the autocorrelation and heteroscvariance of the yield sequence, and estimate the model parameters by using the maximum likelihood method, because the distribution of the residual is assumed to be assumed greatly, thus the GMM estimation is used. The MLE estimation result is corrected. Finally, near We use POT model to analyze the residual error filtered through ARARCH model, and calculate the yield sequence according to the additivity of VaR. In order to compare the VaR under different confidence levels, the VaR of Shanghai Composite Index is estimated using the general POT model. So it's possible to overestimate the real market risk. By comparing two models at different levels of confidence Based on VaR, the following conclusions can be obtained: 1. Under different saliency levels, the average VaR of the general POT model is higher than the VaR value estimated by the APARCH-POT model, which indicates that The general POT model does overestimate the market risk, and the estimation results of the APARCH-POT model are more conservative, In this paper, the validity of VaR is verified by using Krupiec failure return test. The POT model and POT model after APARCH filtering are 95% and 99% respectively. The Krupiec test results are valid at the confidence level. The POT model test effect is better at 99% confidence level. 3. By using the PO filtered by APARCH The VaR of the whole sample period is obtained by the dynamic modeling of the T model. Based on the VaR distribution during the sample period, the following conclusions can be obtained: the VaR of the sample period is divided into five sections according to the accumulation characteristics. In 1990-1994, the attitude of the government to the stock market determines the market risk at this time, and the risk value is the largest; 19 From 95 to 1999, as the government is firm in developing stock market confidence, the market risk begins to fall back, but the overall risk is still large; in 2000-2006, with the maturity of the system and the entry of institutional investors, the market risk is relatively low, and the risk value is relatively low; 2006-2010 Frequent economic stimulus policies pushing high market risk; 2010-present, The main innovation point in this paper lies in the comparison and analysis of the disturbance items when the APARCH model is used to model the filtering rate of return. In addition, we use APARCH model and POT model to fit the yield and avoid
【學(xué)位授予單位】：東北財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F832.51
本文編號(hào)：2310496
[Abstract]:With the rapid development of financial markets in recent years, the volatility has been accompanied by the frequent occurrence of many financial crisis events. Financial regulators and many investors have therefore increased attention to market risks. How to find a more precise measurement tool has become the primary problem of the current measure market risk, and the risk value (VaR) is the most common risk measure tool at present, which means that at a certain confidence level, The maximum possible loss of a portfolio of assets or assets over a certain period of time, i.e., a fractional number of digits of the distribution function for the combined benefit of a given significance level asset portfolio. In the traditional VaR, we assume that the yield is subject to a certain distribution. In view of this extreme value theory, the POT model gradually becomes the mainstream of VaR estimation. This is because the POT model only needs to study the tail feature of the yield sequence and use the GPD distribution to fit the tail. In this paper, we introduce the APARCH model on the basis of the POT model, and combine them in the research of the market risk, which in the current mainstream view is concerned that the tail risk of extreme VaR is more meaningful than the risk of paying attention to the normal situation. Under the scene, this article is helpful for how to measure market risk accurately and accurately In this paper, from December 19, 1990 to March 19, 2012 as raw data, the paper analyses the risk of Shanghai Shanghai Stock Market by using the empirical analysis method, and compares the V at different confidence levels. First of all, this paper describes the statistical characteristics of the yield sequence, so as to select reasonable price. By means of positive state, self-correlation and ARCH effect, this paper finds that the stock market rate of return on stock market has peak-thickness tail, weak self-phase, Secondly, we use the APARCH model to capture the autocorrelation and heteroscvariance of the yield sequence, and estimate the model parameters by using the maximum likelihood method, because the distribution of the residual is assumed to be assumed greatly, thus the GMM estimation is used. The MLE estimation result is corrected. Finally, near We use POT model to analyze the residual error filtered through ARARCH model, and calculate the yield sequence according to the additivity of VaR. In order to compare the VaR under different confidence levels, the VaR of Shanghai Composite Index is estimated using the general POT model. So it's possible to overestimate the real market risk. By comparing two models at different levels of confidence Based on VaR, the following conclusions can be obtained: 1. Under different saliency levels, the average VaR of the general POT model is higher than the VaR value estimated by the APARCH-POT model, which indicates that The general POT model does overestimate the market risk, and the estimation results of the APARCH-POT model are more conservative, In this paper, the validity of VaR is verified by using Krupiec failure return test. The POT model and POT model after APARCH filtering are 95% and 99% respectively. The Krupiec test results are valid at the confidence level. The POT model test effect is better at 99% confidence level. 3. By using the PO filtered by APARCH The VaR of the whole sample period is obtained by the dynamic modeling of the T model. Based on the VaR distribution during the sample period, the following conclusions can be obtained: the VaR of the sample period is divided into five sections according to the accumulation characteristics. In 1990-1994, the attitude of the government to the stock market determines the market risk at this time, and the risk value is the largest; 19 From 95 to 1999, as the government is firm in developing stock market confidence, the market risk begins to fall back, but the overall risk is still large; in 2000-2006, with the maturity of the system and the entry of institutional investors, the market risk is relatively low, and the risk value is relatively low; 2006-2010 Frequent economic stimulus policies pushing high market risk; 2010-present, The main innovation point in this paper lies in the comparison and analysis of the disturbance items when the APARCH model is used to model the filtering rate of return. In addition, we use APARCH model and POT model to fit the yield and avoid
【學(xué)位授予單位】：東北財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F832.51
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