發(fā)布時(shí)間：2018-06-25 00:46

  本文選題：極值理論 + 時(shí)變Copula��； 參考：《浙江工商大學(xué)》2015年碩士論文

【摘要】：自2014年以來,原油價(jià)格發(fā)生了巨幅下跌的狀況,而且白金融危機(jī)以來油價(jià)從未像現(xiàn)在跌得如此猛烈。而原油價(jià)格暴跌受到影響的首當(dāng)其沖是石油出口大國(guó)俄羅斯,作為俄羅斯貨幣的盧布也出現(xiàn)異乎尋常的暴跌,盧布貶值與近期的油價(jià)暴跌有著相應(yīng)的聯(lián)系。面對(duì)著原油暴跌以及盧布危機(jī)這樣的極端金融事件的發(fā)生,風(fēng)險(xiǎn)狀況的精確度量顯得尤為必要和緊迫。已有研究表明基于極值理論的VaR模型能夠較好地估計(jì)金融市場(chǎng)的極端風(fēng)險(xiǎn)值,然而現(xiàn)實(shí)的金融市場(chǎng)數(shù)據(jù)往往不能滿足獨(dú)立同分布的前提假設(shè),因此本文首先采用GJR模型、EGARCH模型和GARCH模型結(jié)合t分布、GED分布和SKST分布處理布倫特原油及盧布匯率對(duì)數(shù)收益率序列,得到收益率序列的標(biāo)準(zhǔn)殘差序列,接下來對(duì)服從獨(dú)立同分布假設(shè)的標(biāo)準(zhǔn)殘差序列運(yùn)用閾值POT模型計(jì)算VaR值和CVaR值,最終計(jì)算得到單一資產(chǎn)的風(fēng)險(xiǎn)值。考慮到在模型回測(cè)中通常使用的Kupiec檢驗(yàn)忽略了數(shù)據(jù)的時(shí)間變化特征,本文模型回測(cè)采用Christofferson有條件覆蓋模型,其在Kupiec檢驗(yàn)的基礎(chǔ)上考慮了超出值序列的時(shí)間易變性。實(shí)證研究表明：在較低置信水平下,各模型對(duì)兩資產(chǎn)序列極端風(fēng)險(xiǎn)狀況的測(cè)度均失效,而在較高置信水平下,各模型均顯著有效。各模型對(duì)兩資產(chǎn)序列上尾部的檢驗(yàn)值大小均比較接近,而且均在較高置信水平下表現(xiàn)出模型的有效性；而對(duì)于兩資產(chǎn)序列下尾部極端風(fēng)險(xiǎn)狀況的測(cè)度模型中均為GJR-SKST-POT模型最優(yōu),而且在此模型下的檢驗(yàn)值均是明顯小于其他模型的檢驗(yàn)值,說明對(duì)于兩序列下尾部風(fēng)險(xiǎn)測(cè)度來說,GJR-SKST-POT模型確實(shí)優(yōu)于其他模型。為研究原油市場(chǎng)與盧布匯率市場(chǎng)之間相依結(jié)構(gòu),即盧布危機(jī)受到原油暴跌的影響大小,并且考慮到金融市場(chǎng)的相關(guān)性總是隨時(shí)間變化的,本文采用三種時(shí)變Copula模型以及對(duì)應(yīng)的三種常相關(guān)Copula模型研究?jī)墒袌?chǎng)之間的相關(guān)性。由于Copula模型具有不受邊緣分布的限制的優(yōu)點(diǎn),可以將邊緣分布與Copula模型分開研究,本文利用前文得到的綜合最優(yōu)模型GJR-SKST-POT模型作為邊緣分布,結(jié)合Copula模型測(cè)度資產(chǎn)相關(guān)性。實(shí)證研究表明：采用時(shí)變SJC Copula模型描述資產(chǎn)序列之間的相依結(jié)構(gòu)最為準(zhǔn)確,且時(shí)變SJC Copula模型測(cè)度的上尾部平均相關(guān)系數(shù)也大于下尾部平均相關(guān)系數(shù),說明了兩資產(chǎn)市場(chǎng)在牛市階段比在熊市階段更容易出現(xiàn)聯(lián)合極值現(xiàn)象。通過得到的相關(guān)系數(shù)大小來看,兩資產(chǎn)序列之間的相關(guān)性并不如想象中的大,但在其他的諸如西方國(guó)家對(duì)俄羅斯的制裁以及美元走強(qiáng)等因素的共同影響下,兩資產(chǎn)序列之間的相關(guān)性已經(jīng)相當(dāng)可觀,說明了原油價(jià)格的暴跌確實(shí)是盧布暴跌的主要原因之一。
[Abstract]:Crude oil prices have fallen sharply since 2014, and oil prices have not fallen as hard since the financial crisis. Russia, the major oil exporter, has been the first to be hit by the collapse in crude oil prices. The ruble, the Russian currency, has also suffered an unusual collapse, with a corresponding link between the devaluation of the ruble and the recent collapse in oil prices. In the face of the collapse of crude oil and extreme financial events such as the rouble crisis, the accuracy of the risk situation is particularly necessary and urgent. Previous studies have shown that VaR model based on extreme value theory can estimate the extreme risk value of financial market well. However, the actual financial market data often can not meet the premise of independent co-distribution. Therefore, in this paper, GJR model EGARCH model and GARCH model combined with t distribution GED distribution and SKST distribution are used to deal with the logarithmic yield series of Brent crude oil and rouble exchange rate, and the standard residuals of the return series are obtained. Then the VaR value and Cvar value are calculated by using threshold pot model for the standard residual sequence with independent co-distribution hypothesis, and the risk value of a single asset is finally calculated. Considering that the Kupiec test, which is usually used in model retrieval, neglects the time variation of data, the model retesting adopts Christofferson conditional covering model, which considers the time variability of the value series on the basis of Kupiec test. The empirical study shows that under the lower confidence level, each model fails to measure the extreme risk of the two asset series, but at the higher confidence level, each model is effective. Each model is similar to the test value on the tail of the two asset sequences, and shows the validity of the model at a higher confidence level, while the GJR-SKST-POT model is optimal for the extreme risk situation of the tail in the two asset sequences. Moreover, the test values under this model are obviously smaller than those of other models, which indicates that the GJR-SKST-POT model is indeed superior to other models for tail risk measurement under two sequences. In order to study the structure of dependence between the crude oil market and the rouble exchange rate market, that is, the magnitude of the rouble crisis affected by the collapse in crude oil, and taking into account that the correlation of financial markets always changes over time, In this paper, three kinds of time-varying Copula models and three corresponding frequent correlation copula models are used to study the correlation between the two markets. Because the Copula model has the advantage of not being restricted by the edge distribution, the edge distribution can be studied separately from the Copula model. In this paper, the GJR-SKST-POT model is used as the edge distribution and the Copula model is used to measure the asset correlation. The empirical study shows that the time-varying SJC Copula model is the most accurate method to describe the dependence structure between asset sequences, and the upper tail average correlation coefficient of time-varying SJC Copula model is larger than the lower tail average correlation coefficient. It shows that the two asset markets are more prone to joint extremum in bull market than in bear market. Based on the magnitude of the correlation coefficient obtained, the correlation between the two asset sequences is not as large as expected, but under the combined influence of other factors such as Western sanctions against Russia and the strengthening of the dollar, The correlation between the two asset series is already considerable, suggesting that the collapse in crude oil prices was indeed one of the main reasons for the ruble's collapse.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：F224;F416.22;F835.12

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉雪梅;樊明方;;極值理論在巨災(zāi)損失擬合中的應(yīng)用[J];統(tǒng)計(jì)與決策;2008年20期

2 李鋒;劉澄;;基于極值理論的金融風(fēng)險(xiǎn)研究[J];商業(yè)研究;2010年05期

3 全登華;利用極值理論計(jì)量銀行操作風(fēng)險(xiǎn)[J];統(tǒng)計(jì)與決策;2002年03期

4 柳會(huì)珍;;統(tǒng)計(jì)極值理論及其應(yīng)用研究進(jìn)展[J];統(tǒng)計(jì)與決策;2006年16期

5 曹中;陶愛元;沈?qū)W楨;;應(yīng)用極值理論度量金融市場(chǎng)風(fēng)險(xiǎn)[J];商業(yè)研究;2006年23期

6 李娟;趙選民;;二元極值理論在滬深股市尾部風(fēng)險(xiǎn)度量中的應(yīng)用[J];系統(tǒng)管理學(xué)報(bào);2007年01期

7 梁q;張三寶;;基于極值理論和貝葉斯估計(jì)的金融風(fēng)險(xiǎn)度量[J];時(shí)代經(jīng)貿(mào)(中旬刊);2008年S8期

8 李文華;;基于極值理論的商業(yè)銀行同業(yè)拆借利率風(fēng)險(xiǎn)度量[J];統(tǒng)計(jì)與決策;2012年08期

9 常呈云;;極值理論在經(jīng)濟(jì)決策中的應(yīng)用[J];河南財(cái)經(jīng)學(xué)院學(xué)報(bào);1992年03期

10 王瑗;;廠商行為中的極值理論[J];河南財(cái)經(jīng)學(xué)院學(xué)報(bào);1993年02期

相關(guān)會(huì)議論文 前1條

1 陳倩;;基于極值理論的商業(yè)銀行操作風(fēng)險(xiǎn)度量研究[A];第十四屆中國(guó)管理科學(xué)學(xué)術(shù)年會(huì)論文集(上冊(cè))[C];2012年

相關(guān)博士學(xué)位論文 前4條

1 張相賢;基于極值理論的金融資產(chǎn)配置研究[D];東華大學(xué);2011年

2 花擁軍;極值理論在中國(guó)股市風(fēng)險(xiǎn)度量中的應(yīng)用研究[D];重慶大學(xué);2009年

3 余為麗;基于極值理論的VaR及其在中國(guó)股票市場(chǎng)風(fēng)險(xiǎn)管理中的應(yīng)用[D];華中科技大學(xué);2006年

4 紀(jì)比拉;基于極值理論的國(guó)際權(quán)益資產(chǎn)組合下側(cè)風(fēng)險(xiǎn)測(cè)量[D];天津大學(xué);2005年

相關(guān)碩士學(xué)位論文 前10條

1 王慶曉;基于極值理論的動(dòng)態(tài)風(fēng)險(xiǎn)價(jià)值的研究[D];山東大學(xué);2009年

2 賀壬癸;基于極值理論的我國(guó)銀行間同業(yè)拆借利率的風(fēng)險(xiǎn)度量[D];蘭州大學(xué);2012年

3 曹丹;基于極值理論的動(dòng)態(tài)極端風(fēng)險(xiǎn)度量及其應(yīng)用研究[D];浙江財(cái)經(jīng)學(xué)院;2011年

4 晏玉香;極值理論中的極值指標(biāo)以及上端點(diǎn)的性質(zhì)研究[D];南京師范大學(xué);2007年

5 龔維維;基于極值理論的巨災(zāi)風(fēng)險(xiǎn)管理研究[D];西南財(cái)經(jīng)大學(xué);2014年

6 付連軍;極值理論與商業(yè)銀行重大損失研究[D];首都經(jīng)濟(jì)貿(mào)易大學(xué);2005年

7 崔素娟;基于動(dòng)靜態(tài)極值理論的人民幣匯率風(fēng)險(xiǎn)測(cè)度[D];東北財(cái)經(jīng)大學(xué);2010年

8 張靜;基于極值理論的我國(guó)商業(yè)銀行匯率風(fēng)險(xiǎn)管理研究[D];重慶理工大學(xué);2011年

9 王悅;基于極值理論的我國(guó)開放式基金業(yè)績(jī)?cè)u(píng)價(jià)的實(shí)證研究[D];浙江大學(xué);2012年

10 李惟佳;基于極值理論的股票的流動(dòng)性風(fēng)險(xiǎn)度量及其應(yīng)用研究[D];南京航空航天大學(xué);2011年

，

本文編號(hào)：2063730