本文關(guān)鍵詞： GJR-Copula 投資組合 非對(duì)稱(chēng)性 風(fēng)險(xiǎn)度量　出處：《中央民族大學(xué)》2013年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：近些年來(lái),國(guó)際金融形勢(shì)發(fā)生了深刻的變化。金融市場(chǎng)的波動(dòng)愈加頻繁,危機(jī)經(jīng)常發(fā)生,對(duì)風(fēng)險(xiǎn)的度量已成為行業(yè)關(guān)注的話題之一。傳統(tǒng)的風(fēng)險(xiǎn)度量手段建立在馬克維茨的投資組合理論的基礎(chǔ)上,資產(chǎn)組合之間為線性相關(guān)關(guān)系,資產(chǎn)組合的收益率服從多元正態(tài)分布,使用VaR指標(biāo)對(duì)單一資產(chǎn)和資產(chǎn)組合進(jìn)行風(fēng)險(xiǎn)度量。但大量的事實(shí)證明,金融資產(chǎn)的收益率存在明顯的非正態(tài)的尖峰厚尾的特征,資產(chǎn)之間的相關(guān)關(guān)系非線性,傳統(tǒng)的正態(tài)模型會(huì)低估資產(chǎn)風(fēng)險(xiǎn)。另一方面,VaR方法存在一定的建模缺陷,不能滿(mǎn)足管理者對(duì)風(fēng)險(xiǎn)控制的需要。因此,本文引入Copula模型和CVaR技術(shù),對(duì)于單個(gè)金融資產(chǎn)的收益率,CVaR方法考慮到了其尾部損失的均值,更加適于測(cè)度風(fēng)險(xiǎn)；對(duì)于投資組合聯(lián)合收益率,建立Copula模型基于金融資產(chǎn)的非線性相關(guān)性,不限制邊緣分布從而構(gòu)建了相應(yīng)的的聯(lián)合分布損益函數(shù)。 本文首先討論了Copula模型在金融市場(chǎng)尾部相關(guān)性度量上的應(yīng)用；接著基于VaR與CVaR的風(fēng)險(xiǎn)測(cè)度方法,依次運(yùn)用歷史模擬法、方差協(xié)方差法、蒙特卡羅模擬法和極值理論測(cè)度道瓊斯工業(yè)指數(shù)和香港恒生指數(shù)的VaR和CVaR,結(jié)果表明,四種方法的計(jì)算的VaR都低估了實(shí)際股指的風(fēng)險(xiǎn)。然而,CVaR的失敗率大大降低,提高了對(duì)未來(lái)變動(dòng)的預(yù)測(cè)準(zhǔn)確度。考慮到資產(chǎn)收益率這一時(shí)間序列的非對(duì)稱(chēng)的時(shí)變特征,文章對(duì)單個(gè)股指的收益率序列建立GJR模型,并在此基礎(chǔ)上引,Copula方法,建立GJR-Copula模型,度量投資組合的VaR和CVaR,并通過(guò)返回檢驗(yàn)驗(yàn)證模型。結(jié)果證明,GJR-Copula模型的CVaR可以精準(zhǔn)地測(cè)度收益率具有非對(duì)稱(chēng)性的資產(chǎn)的風(fēng)險(xiǎn)價(jià)值以及投資組合的風(fēng)險(xiǎn)價(jià)值,為風(fēng)險(xiǎn)管理著的投資決策提供信息支持。
[Abstract]:In recent years, the international financial situation has undergone profound changes. The traditional risk measurement method is based on Markowitz's portfolio theory and the relationship between portfolio is linear. The return rate of portfolio is from multiple normal distribution, using VaR index to measure the risk of single asset and portfolio. But a lot of facts prove that. The return rate of financial assets has the characteristic of non-normal peak and thick tail, the correlation between assets is nonlinear, the traditional normal model will underestimate the risk of assets. VaR method has some defects in modeling and can not meet the needs of risk control. Therefore, this paper introduces Copula model and CVaR technology to the return rate of a single financial asset. The CVaR method takes into account the mean value of its tail loss and is more suitable to measure risk. For the joint return rate of portfolio, the Copula model is based on the nonlinear correlation of financial assets, and the corresponding joint distribution profit and loss function is constructed without limiting the edge distribution. This paper first discusses the application of Copula model to the measurement of tail correlation in financial markets. Then the risk measurement method based on VaR and CVaR, using historical simulation method, variance covariance method in turn. Monte Carlo simulation and extreme value theory are used to measure the VaR and Cvar of the Dow Jones Industrial Index and the Hang Seng Index in Hong Kong. The results show that the VaR calculated by the four methods underestimate the risk of the actual stock index. The failure rate of CVaR is greatly reduced, which improves the accuracy of predicting future changes, considering the asymmetric time-varying characteristics of the time series of asset return. This paper establishes the GJR model for the return sequence of a single stock index, and on the basis of this, establishes the GJR-Copula model to measure the VaR and CVaR of the portfolio by using the Copula method. The results show that the CVaR of GJR-Copula model can accurately measure the risk value of assets with asymmetric return rate and the risk value of portfolio. Provide information support for risk-managed investment decisions.
【學(xué)位授予單位】：中央民族大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：F224;F830.59;O211.67

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