基于Copula-SV模型的LPM套期保值研究
發(fā)布時間：2018-07-23 19:04
【摘要】：期貨市場的一個主要功能就是套期保值，通過在期貨市場的對沖操作可以實現對現貨市場上風險的轉移，從而更加有效的管理投資組合的市場風險。2010年4月16日，我國首次推出了滬深300指數期貨，為我國機構投資者和中大的個人投資者提供了更加靈活的管理資產組合風險的途徑。但是，由于衍生工具往往具有很大的杠桿效應，如果不能正確的使用套期保值頭寸，結果不但不能有效減少風險，還可能擴大風險。因此本文從研究如何計算最優(yōu)套期保值比率這一問題入手，以期對投資者的套期保值決策提供建議。 套期保值問題研究的核心是如何確定一個最優(yōu)的套期保值比率使得能夠最大程度的減少套期保值組合的風險。這一問題涉及到兩個方面：1）用什么來衡量套期保值組合的風險？2）確定風險的度量方法之后用什么方法去計算在這個風險度量方法下的最優(yōu)套期保值比率？本文首先回顧了目前廣泛使用的四種套期保值風險度量的方法：方差、VaR、ES以及下偏矩LPM。根據套期保值的特征，我們認為LPM是最適合的度量套期保值組合風險的方法。但是在現實中，由于很難確定期貨現貨的聯合分布，利用LPM計算最優(yōu)套期比率存在非常大的計算難度。正是由于計算上的復雜和困難，制約了LPM方法在套期保值上的廣泛應用。1959年Sklar提出了一種新的估計聯合分布的方法：Copula函數方法。用一個Copula函數去描述變量間的相關性關系，通過將一個聯合分布分解為k個邊緣分布和一個Copula函數來描述多個變量的聯合分布情況。本文選擇LPM方法來度量套期保值組合的市場風險，用Copula函數方法建立數學模型來計算運用股指期貨進行套期保值的最優(yōu)套期保值比率。 本文以滬深300股指期貨和滬深300指數現貨為研究對象對基于LPM的最優(yōu)套期保值比率模型進行實證研究。首先，分別使用GARCH、EGARCH及SV三類備選模型去擬合股指期貨和現貨收益率的邊緣分布。通過對擬合結果的標準殘差序列進行卡方檢驗我們發(fā)現，SV-T模型的KS檢驗概率值最大，，最能夠刻畫股指期貨現貨風險收益率的邊緣分布。因此根據邊緣分布擬合優(yōu)度檢驗的結果，本文選擇SV-T模型作為邊緣分布模型來刻畫兩組金融時間收益率的分布情況。對單個的風險資產收益率的邊緣分布進行建模之后，我們考察五類常用的二元Copula函數對整個聯合分布的擬合情況。通過對擬合結果的卡方檢驗證明t-Copula是最能刻畫樣本數據間相關關系的連接函數形式。結合t-Copula和SV-t模型我們得到的Copula-SV模型。將Copula-SV模型的估計結果帶入到LPM最優(yōu)套期保值比率的模型中去，我們得到了在不同目標收益率和風險厭惡程度下的LPM最優(yōu)套期保值比率。為了更好的考察Copula函數方法計算LPM套期保值比率的優(yōu)劣，本文同時也給出了兩類非參數方法的最優(yōu)套期保值比率的計算結果。根據上面得到最優(yōu)套期保值比率，我們對樣本外數據進行模擬套期保值，并計算相應的套期保值效率指標H值和R/SV。通過對比模擬套期保值結果的效率指標，我們發(fā)現Copula方法具有比較明顯的優(yōu)勢，是一種比較符合市場實際的計算方法。
[Abstract]:One of the main functions of the futures market is hedging. Through the hedging operation in the futures market, the risk of the spot market can be transferred to the spot market. Thus the market risk of the portfolio is more effectively managed in April 16th. The Shanghai and Shenzhen 300 index futures were first introduced in China for the first time in China, for the institutional investors and the big individual investment in China. It provides a more flexible way to manage portfolio risk. However, because derivatives often have a great leverage effect, if the hedging position can not be used correctly, the result can not effectively reduce the risk, but also may expand the risk. Therefore, this paper studies how to calculate the optimal hedging ratio. In order to provide suggestions for hedging decisions of investors.
The core of the study of hedging is how to determine an optimal hedging ratio to minimize the risk of hedging portfolio. This problem involves two aspects: 1) what is the risk to measure the hedging portfolio? 2) what is the method to calculate the wind after the measurement of the risk? The optimal hedging ratio under the risk measurement method? This paper first reviews the four widely used hedging risk measures at present: variance, VaR, ES and lower moment LPM. according to hedging characteristics, we think that LPM is the most suitable measure of hedging portfolio risk. But in reality, because it is difficult to determine. The joint distribution of futures spot is very difficult to calculate by using LPM to calculate the optimal hedging ratio. It is precisely because of the complexity and difficulty of the calculation, which restricts the extensive application of LPM method on hedging..1959 Sklar proposed a new method of estimating joint distribution: Copula function method. A Copula function is used to describe the change. The correlation between the quantity is divided into K edge distribution and a Copula function to describe the joint distribution of multiple variables. In this paper, we choose the LPM method to measure the market risk of hedging portfolio, and use the Copula function method to establish the mathematical model to calculate the optimal hedging with the stock index futures. Hedging ratio.
This article takes the Shanghai and Shenzhen 300 stock index futures and the Shanghai and Shenzhen 300 index spot as the research object to carry on the empirical study to the optimal hedging ratio model based on LPM. First, we use the GARCH, EGARCH and SV three kinds of alternative models to fit the edge distribution of stock index futures and spot returns respectively. We found that the SV-T model has the largest KS test probability value and can describe the edge distribution of stock index futures spot risk yield. Therefore, according to the result of edge distribution fitting goodness test, the SV-T model is selected as the edge distribution model to describe the distribution of two groups of financial time returns. After the edge distribution of the rate is modeled, we examine the fitting of the five kinds of common two element Copula functions for the whole joint distribution. Through the chi square test of the fitting results, we prove that t-Copula is the most capable connection function that characterizations of the correlation between sample data. The Copula-SV model we have obtained by combining the t-Copula and SV-t models. The estimation results of the a-SV model are brought into the LPM optimal hedging ratio model. We get the LPM optimal hedging ratio under the different target returns and risk aversion. In order to better investigate the Copula function method to calculate the LPM hedging ratio, this paper also gives the two kinds of non parametric methods. According to the optimal hedging ratio, we carry out the simulated hedging of the data from the above sample, and calculate the corresponding hedging efficiency index H and R/SV. by comparing the efficiency indexes of the simulated hedging results, we find that the Copula method has a more obvious advantage, which is a kind of ratio. The calculation method which is more in line with the actual market.
【學位授予單位】：浙江財經學院
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F832.51;F224
本文編號：2140364
[Abstract]:One of the main functions of the futures market is hedging. Through the hedging operation in the futures market, the risk of the spot market can be transferred to the spot market. Thus the market risk of the portfolio is more effectively managed in April 16th. The Shanghai and Shenzhen 300 index futures were first introduced in China for the first time in China, for the institutional investors and the big individual investment in China. It provides a more flexible way to manage portfolio risk. However, because derivatives often have a great leverage effect, if the hedging position can not be used correctly, the result can not effectively reduce the risk, but also may expand the risk. Therefore, this paper studies how to calculate the optimal hedging ratio. In order to provide suggestions for hedging decisions of investors.
The core of the study of hedging is how to determine an optimal hedging ratio to minimize the risk of hedging portfolio. This problem involves two aspects: 1) what is the risk to measure the hedging portfolio? 2) what is the method to calculate the wind after the measurement of the risk? The optimal hedging ratio under the risk measurement method? This paper first reviews the four widely used hedging risk measures at present: variance, VaR, ES and lower moment LPM. according to hedging characteristics, we think that LPM is the most suitable measure of hedging portfolio risk. But in reality, because it is difficult to determine. The joint distribution of futures spot is very difficult to calculate by using LPM to calculate the optimal hedging ratio. It is precisely because of the complexity and difficulty of the calculation, which restricts the extensive application of LPM method on hedging..1959 Sklar proposed a new method of estimating joint distribution: Copula function method. A Copula function is used to describe the change. The correlation between the quantity is divided into K edge distribution and a Copula function to describe the joint distribution of multiple variables. In this paper, we choose the LPM method to measure the market risk of hedging portfolio, and use the Copula function method to establish the mathematical model to calculate the optimal hedging with the stock index futures. Hedging ratio.
This article takes the Shanghai and Shenzhen 300 stock index futures and the Shanghai and Shenzhen 300 index spot as the research object to carry on the empirical study to the optimal hedging ratio model based on LPM. First, we use the GARCH, EGARCH and SV three kinds of alternative models to fit the edge distribution of stock index futures and spot returns respectively. We found that the SV-T model has the largest KS test probability value and can describe the edge distribution of stock index futures spot risk yield. Therefore, according to the result of edge distribution fitting goodness test, the SV-T model is selected as the edge distribution model to describe the distribution of two groups of financial time returns. After the edge distribution of the rate is modeled, we examine the fitting of the five kinds of common two element Copula functions for the whole joint distribution. Through the chi square test of the fitting results, we prove that t-Copula is the most capable connection function that characterizations of the correlation between sample data. The Copula-SV model we have obtained by combining the t-Copula and SV-t models. The estimation results of the a-SV model are brought into the LPM optimal hedging ratio model. We get the LPM optimal hedging ratio under the different target returns and risk aversion. In order to better investigate the Copula function method to calculate the LPM hedging ratio, this paper also gives the two kinds of non parametric methods. According to the optimal hedging ratio, we carry out the simulated hedging of the data from the above sample, and calculate the corresponding hedging efficiency index H and R/SV. by comparing the efficiency indexes of the simulated hedging results, we find that the Copula method has a more obvious advantage, which is a kind of ratio. The calculation method which is more in line with the actual market.
【學位授予單位】：浙江財經學院
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F832.51;F224
【參考文獻】
相關期刊論文 前10條
1 鄒慶忠;李金林;王貝貝;;基于時變相關Copula的最小方差套期保值研究[J];北京理工大學學報;2011年11期
2 李萌,余思勤,袁象;計算股指期貨套期保值比率的新方法——LPM法[J];當代經濟;2005年04期
3 韋艷華,張世英;金融市場的相關性分析——Copula-GARCH模型及其應用[J];系統(tǒng)工程;2004年04期
4 安俊英;蔣祥林;張衛(wèi)國;;基于下方風險的期貨套期保值[J];系統(tǒng)工程;2007年10期
5 甘斌;;最小平均VaR套期保值比率計算模型及實證研究[J];經濟管理;2010年09期
6 周怡;;以VaR為目標函數的期貨最優(yōu)套期保值比率估計[J];統(tǒng)計與決策;2008年18期
7 包衛(wèi)軍;徐成賢;;基于SV-Copula模型的相關性分析[J];統(tǒng)計研究;2008年10期
8 陳蓉;蔡宗武;陳妙瓊;;最小下偏矩套期保值比率估計研究——基于混合copula方法[J];廈門大學學報(哲學社會科學版);2009年03期
9 遲國泰;余方平;劉軼芳;;基于VaR的期貨最優(yōu)套期保值模型及應用研究[J];系統(tǒng)工程學報;2008年04期
10 梁建峰;陳健平;劉京軍;;基于Copula-GARCH方法的LPM套期保值研究[J];系統(tǒng)工程學報;2011年05期
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