發(fā)布時間：2018-06-25 00:46

  本文選題：極值理論 + 時變Copula��； 參考：《浙江工商大學》2015年碩士論文

【摘要】：自2014年以來,原油價格發(fā)生了巨幅下跌的狀況,而且白金融危機以來油價從未像現(xiàn)在跌得如此猛烈。而原油價格暴跌受到影響的首當其沖是石油出口大國俄羅斯,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暴跌有著相應的聯(lián)系。面對著原油暴跌以及盧布危機這樣的極端金融事件的發(fā)生,風險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金融市場的極端風險值,然而現(xiàn)實的金融市場數(shù)據(jù)往往不能滿足獨立同分布的前提假設,因此本文首先采用GJR模型、EGARCH模型和GARCH模型結合t分布、GED分布和SKST分布處理布倫特原油及盧布匯率對數(shù)收益率序列,得到收益率序列的標準殘差序列,接下來對服從獨立同分布假設的標準殘差序列運用閾值POT模型計算VaR值和CVaR值,最終計算得到單一資產(chǎn)的風險值�？紤]到在模型回測中通常使用的Kupiec檢驗忽略了數(shù)據(jù)的時間變化特征,本文模型回測采用Christofferson有條件覆蓋模型,其在Kupiec檢驗的基礎上考慮了超出值序列的時間易變性。實證研究表明：在較低置信水平下,各模型對兩資產(chǎn)序列極端風險狀況的測度均失效,而在較高置信水平下,各模型均顯著有效。各模型對兩資產(chǎn)序列上尾部的檢驗值大小均比較接近,而且均在較高置信水平下表現(xiàn)出模型的有效性；而對于兩資產(chǎn)序列下尾部極端風險狀況的測度模型中均為GJR-SKST-POT模型最優(yōu),而且在此模型下的檢驗值均是明顯小于其他模型的檢驗值,說明對于兩序列下尾部風險測度來說,GJR-SKST-POT模型確實優(yōu)于其他模型。為研究原油市場與盧布匯率市場之間相依結構,即盧布危機受到原油暴跌的影響大小,并且考慮到金融市場的相關性總是隨時間變化的,本文采用三種時變Copula模型以及對應的三種常相關Copula模型研究兩市場之間的相關性。由于Copula模型具有不受邊緣分布的限制的優(yōu)點,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,結合Copula模型測度資產(chǎn)相關性。實證研究表明：采用時變SJC Copula模型描述資產(chǎn)序列之間的相依結構最為準確,且時變SJC Copula模型測度的上尾部平均相關系數(shù)也大于下尾部平均相關系數(shù),說明了兩資產(chǎn)市場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。通過得到的相關系數(shù)大小來看,兩資產(chǎn)序列之間的相關性并不如想象中的大,但在其他的諸如西方國家對俄羅斯的制裁以及美元走強等因素的共同影響下,兩資產(chǎn)序列之間的相關性已經(jīng)相當可觀,說明了原油價格的暴跌確實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.
【學位授予單位】：浙江工商大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：F224;F416.22;F835.12

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