本文選題：尾部風(fēng)險 + 尾部相關(guān)性��； 參考：《電子科技大學(xué)》2012年博士論文

【摘要】：頻繁發(fā)生的金融危機一次又一次給投資者、金融市場甚至全球經(jīng)濟帶來嚴重的不良后果。在這些危機中，市場上呈現(xiàn)出與正常情形下不同的特殊特征：單變量的情形下，投資者面臨著發(fā)生極端損失的“尾部風(fēng)險”；在多變量情形下，金融市場或金融資產(chǎn)間存在著相對更強的“尾部相關(guān)性”。如何把握并應(yīng)對這兩種特殊的市場特征，無論對于投資者和風(fēng)險管理者還是政策制定者和監(jiān)管者，都是一個至關(guān)重要的問題。因此，本文綜合運用各種靈活的計量經(jīng)濟方法來捕捉危機時期中出現(xiàn)的“尾部風(fēng)險”和“尾部相關(guān)性”特征，并進一步分析其對風(fēng)險管理、資產(chǎn)配置和資產(chǎn)定價的影響。 首先，針對單個資產(chǎn)所面臨的尾部風(fēng)險，本文引入檢驗效力更強的鞍點技術(shù)返回檢驗方法對各種風(fēng)險模型的VaR和ES預(yù)測準(zhǔn)確性進行了嚴格的再檢驗，重新探討了何種模型能夠最為準(zhǔn)確地捕捉單變量情形下的尾部風(fēng)險�；谥袊墒械膶嵶C分析發(fā)現(xiàn)，簡單的GARCH-Normal模型無法合理地捕捉中國股票市場的風(fēng)險特征，而最好的模型為GARCH-EVT模型。進一步基于更多成熟股市和更多風(fēng)險模型的研究同樣證實，為了得到足夠準(zhǔn)確的風(fēng)險預(yù)測，則有必要借助極值理論EVT來對金融資產(chǎn)收益率的分布尾部進行單獨建模。僅使用一種分布形式很難同時捕捉到分布尾部和分布中間的特征，即使是以往文獻中推薦使用的能同時捕捉分布偏斜特征和厚尾特征的有偏學(xué)生t分布。此外，通過對GARCH類模型中影響風(fēng)險預(yù)測準(zhǔn)確性的兩個維度的相對重要程度首次進行正式統(tǒng)計檢驗發(fā)現(xiàn)，，殘差分布的尾部設(shè)定對VaR和ES預(yù)測的影響要強于波動率方程形式。 其次，針對尾部相關(guān)性對風(fēng)險管理的影響，本文首先提出一種基于多元Copula函數(shù)模擬的方法來計算組合中個別資產(chǎn)的風(fēng)險貢獻，從而實現(xiàn)了對不同資產(chǎn)風(fēng)險貢獻區(qū)別的顯著性檢驗。同時由于Copula函數(shù)在刻畫資產(chǎn)間非線性相關(guān)結(jié)構(gòu)以及尾部相關(guān)性特征方面的優(yōu)勢，使用本文方法計算所得的風(fēng)險貢獻結(jié)果還變得更為一致而不再受置信水平和風(fēng)險度量指標(biāo)的影響。此外，本文還引入一種更為靈活的多元相關(guān)結(jié)構(gòu)建模工具，正則藤Copula函數(shù)，以克服現(xiàn)有研究中可選擇的多元Copula函數(shù)類型的有限性以及存在的不同缺陷�；谏虾�、香港和臺灣三個股市的實證分析證實了正則藤Copula在刻畫多元相關(guān)結(jié)構(gòu)方面的優(yōu)越性。更具有實踐意義的是，基于不同交易策略和不同模擬樣本的風(fēng)險預(yù)測結(jié)果進一步表明，使用正則藤Copula函數(shù)來對多元相關(guān)結(jié)構(gòu)進行靈活建模，可以帶來更為穩(wěn)健和準(zhǔn)確的組合VaR預(yù)測績效。 再次，針對尾部相關(guān)性對資產(chǎn)配置的影響，本文采用馬爾可夫轉(zhuǎn)換Copula模型來同時捕捉資產(chǎn)間相關(guān)關(guān)系的非線性和時變性特征，并基于該模型設(shè)計了一種選擇組合調(diào)整時機的方法�；谥袊墒兄袃深惞善苯M合（高風(fēng)險和低風(fēng)險股票組合）的實證結(jié)果證實了金融資產(chǎn)間相關(guān)結(jié)構(gòu)的依狀態(tài)轉(zhuǎn)換特征，從而指出以往文獻中在較長投資期限內(nèi)基于一個固定模型所構(gòu)建的靜態(tài)策略是不適宜的。本文提出可以借助馬爾可夫轉(zhuǎn)換Copula模型預(yù)測未來狀態(tài)轉(zhuǎn)換的時刻，采用狀態(tài)變化后新的Copula函數(shù)類型來重新預(yù)測分布并計算的新的組合權(quán)重。樣本外資產(chǎn)配置績效分析表明，相比文獻中已有策略，本文的擇時策略確實能給投資者帶來更高的平均已實現(xiàn)收益率和確定性等價收益率。 最后，針對尾部相關(guān)性對資產(chǎn)定價的影響，本文關(guān)注了個別股票與整個市場之間的尾部相關(guān)性，并分析了其對股票收益率的影響作用。賣空限制的存在往往導(dǎo)致遠比上漲風(fēng)險更為嚴重的極端下跌市場風(fēng)險的產(chǎn)生，然而線性的Beta卻無法對其區(qū)分。本文使用尾部相關(guān)性系數(shù)來捕捉這種個股隨整個市場同時暴跌的極端下跌市場風(fēng)險�；谏献CA股的實證分析證實了個股與市場間尾部相關(guān)性是普遍存在的，而且更為值得關(guān)注的是，這種尾部相關(guān)性對滬市中股票收益率具有顯著的解釋能力，其解釋能力即使在控制了其他定價因素（尤其是線性Beta）的影響后依然存在。因此，尾部相關(guān)性系數(shù)提供了一種刻畫市場風(fēng)險的新角度，可能包含著已有定價因素之外的信息而有潛力成為新的定價因子。
[Abstract]:Frequent financial crises have brought serious adverse consequences to investors, financial markets and even the global economy. In these crises, the market presents a special feature different from the normal situation: in the case of a single variable, investors face the "tail risk" of extreme loss; in a multivariable case, gold is in the case of gold. There is a relatively stronger "tail relevance" among market or financial assets. How to grasp and cope with these two special market characteristics is a crucial issue for both investors and risk managers, policymakers and regulators. Therefore, this paper applies a variety of flexible econometric methods to capture. The "tail risk" and "tail dependence" characteristics in the crisis period are analyzed, and their impact on risk management, asset allocation and asset pricing is further analyzed.
First, in view of the tail risk faced by a single asset, this paper introduces a more effective inspection method of the saddle point technology return test for the VaR and ES prediction accuracy of various risk models, and reexamines what model can most accurately capture the tail risk under the single variable condition. Based on the Chinese stock market The empirical analysis shows that the simple GARCH-Normal model can not reasonably capture the risk characteristics of the Chinese stock market, and the best model is the GARCH-EVT model. Further research based on more mature stock markets and more risk models also confirms that in order to get enough accurate risk prediction, it is necessary to use the extreme value theory EVT to finance the finance. The distribution tail of the rate of return is modeled separately. It is difficult to capture the characteristics of the distribution tail and distribution at the same time using only one form of distribution, even if it is recommended in previous literature to capture the t distribution of skewed and thick tail characteristics at the same time. In addition, the risk prediction is influenced by the GARCH model. For the first time, the relative importance of the two dimensions of the certainty is carried out by formal statistical tests. It is found that the effect of the tail setting of the residual distribution on the prediction of VaR and ES is stronger than the wave rate equation.
Secondly, in view of the effect of tail correlation on risk management, this paper first proposes a method based on multivariate Copula function simulation to calculate the risk contribution of individual assets in the combination, thus realizing the significant test of the difference between different asset risk contributions. At the same time, the Copula function is used to describe the nonlinear correlation structure between assets and the relationship between assets. The advantages of the tail correlation characteristics, the results of the risk contribution calculated using this method have also become more consistent and no longer affected by the confidence level and risk metrics. In addition, this paper also introduces a more flexible multivariate correlation structure modeling tool, the canonical Copula function, to overcome the choice in the existing research. The finite nature of the type of meta Copula function and the existence of different defects. Based on the empirical analysis of three stock markets in Shanghai, Hongkong and Taiwan, the advantages of the canonical Copula in the characterization of multiple correlation structures are confirmed. Flexible modeling of multivariate correlation structures with regular rattan Copula functions can bring more robust and accurate performance of combined VaR prediction.
Thirdly, in view of the effect of tail correlation on asset allocation, this paper uses the Markov transformation Copula model to capture the nonlinear and time-varying characteristics of the correlation between assets, and designs a method for selecting the timing of combination adjustment based on the model. Based on the two types of stock portfolios in China's stock market (high risk and low risk stock groups) The empirical results confirm the state transition characteristics of the related structure between financial assets, and then point out that the static strategy based on a fixed model in the long term literature is not suitable in the previous literature. This paper proposes that the Markov transform Copula model can be used to predict the time for the transition of the future state and adopt the state change. The new Copula function type is used to re predict the new combined weights of distribution and calculation. The performance analysis of asset allocation shows that, compared with the existing strategies in the literature, the timing strategy of this paper can indeed bring higher average realized yield and certainty equivalent yield to investors.
Finally, in view of the effect of tail correlation on asset pricing, this paper focuses on the tail correlation between individual stock and the whole market, and analyzes its effect on the stock returns. The existence of short selling limit often leads to extreme market risk which is far more serious than the risk of rising, but the linear Beta is not possible. This paper uses the tail correlation coefficient to capture the extreme falling market risk of this stock with the whole market falling at the same time. Empirical analysis based on the Shanghai Stock A shares confirms that the tail correlation between the stock and the market is common, and it is more worthy of concern that the tail correlation has the stock returns in the Shanghai stock market. The explanatory power, even after controlling the influence of other pricing factors (especially linear Beta), still exists. Therefore, the tail correlation coefficient provides a new perspective of market risk, which may contain information other than the existing pricing factors and have the potential to become a new pricing factor.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2012
【分類號】：F832.51;F224

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