基于GARCH-POT模型的中國外匯市場投資組合研究
發(fā)布時間:2018-01-23 17:47
本文關(guān)鍵詞: 外匯投資 風(fēng)險度量 外匯投資組合 出處:《哈爾濱工業(yè)大學(xué)》2015年碩士論文 論文類型:學(xué)位論文
【摘要】:2008年美國金融危機爆發(fā)后,各國資產(chǎn)開始重新配置,全球外匯市場波動的加劇。在后金融危機這一背景下研究處于匯改攻堅階段的中國外匯市場的風(fēng)險,并對其進行度量對于國內(nèi)的投資者意義重大。通過對2008年1月2日到2015年3月9日美元、歐元、日元以及港幣對人民幣的匯率中間價的對數(shù)收益率進行統(tǒng)計檢驗研究,可以看出4組外匯數(shù)據(jù)收益率序列都存在“有偏尖峰且薄尾”的特征,均存在自相關(guān)與偏自相關(guān)效應(yīng)以及異方差效應(yīng),基于以上特征建立的GARCH模型可以較好地反映出中國外匯市場收益率的波動性。通過實證檢驗可以發(fā)現(xiàn),GARCH模型在描述數(shù)據(jù)波動性方面效果良好,但是對序列的尾部擬合不是很好。因此,將極值理論引入到描述模型的殘差中來彌補GARCH模型的不足,以對殘差尾部的點進行有效描述。通過Hill估計法確定POT模型的閾值u,進而估計出POT閾值模型的上下尾參數(shù),將POT模型與Va R、CVa R風(fēng)險度量方法聯(lián)立,計算出單支外匯的風(fēng)險值Va R和條件風(fēng)險值CVa R。可以對30天的單支外匯風(fēng)險值進行較準(zhǔn)確地預(yù)測。在建立GATCH-POT模型的基礎(chǔ)上,通過引入多元正態(tài)Copula模型、多元t-Copula模型以及多元時變Copula模型,聯(lián)立美元、歐元、日元以及港幣4組外匯殘差序列的邊緣分布進而形成一個統(tǒng)一的聯(lián)合分布,可以計算出中國外匯投資組合的風(fēng)險價值。并在風(fēng)險最小原則下,度量出4種外匯的最優(yōu)投資比例,進而計算出4組外匯投資的最佳比例,為投資者提供了投資決策的重要依據(jù)。
[Abstract]:In 2008, after the outbreak of the American financial crisis, the assets of various countries began to be redistributed, and the volatility of the global foreign exchange market intensified. Under the background of the post-financial crisis, the risk of China's foreign exchange market in the stage of foreign exchange reform was studied. And to measure it is significant for domestic investors. Through the January 2nd 2008 to March 9th 2015 dollar, the euro. The logarithmic rate of return of the exchange rate of yen and Hong Kong dollar to RMB is statistically tested, and it can be seen that the series of four groups of foreign exchange data rate of return all have the characteristics of "biased peak and thin tail". There are autocorrelation, partial autocorrelation and heteroscedasticity effects. The GARCH model based on the above characteristics can well reflect the volatility of China's foreign exchange market returns. The GARCH model is effective in describing the data volatility, but the tail fitting of the sequence is not very good. Therefore, the extreme value theory is introduced into the residual of the description model to compensate for the shortage of the GARCH model. In order to describe the point of residual tail effectively, the threshold value u of POT model is determined by Hill estimation method, and then the upper and lower tail parameters of POT threshold model are estimated. CVa R risk measurement method is simultaneous. The risk value V a R and the conditional risk value CVa R of a single foreign exchange can be calculated. The risk value of a single foreign exchange for 30 days can be accurately predicted. Based on the establishment of GATCH-POT model. By introducing the multivariate normal Copula model, the multivariate t-Copula model and the multivariate time-varying Copula model, the United States dollar and euro are established. The marginal distribution of the four groups of foreign exchange residuals in yen and Hong Kong dollars forms a unified joint distribution, which can calculate the risk value of China's foreign exchange portfolio, and under the principle of minimum risk. The optimal investment proportion of four kinds of foreign exchange is measured, and the best proportion of four groups of foreign exchange investment is calculated, which provides an important basis for investors to make investment decisions.
【學(xué)位授予單位】:哈爾濱工業(yè)大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2015
【分類號】:F832.6;F224
【參考文獻】
相關(guān)期刊論文 前7條
1 楊湘豫;崔迎媛;;基于Copula-GARCH-EVT的中國開放式基金投資組合風(fēng)險度量[J];財經(jīng)理論與實踐;2009年05期
2 羅曉艷;田新時;;CVaR風(fēng)險度量法在銀行信用風(fēng)險管理中的應(yīng)用[J];當(dāng)代經(jīng)濟;2005年12期
3 韋艷華,張世英;金融市場的相關(guān)性分析——Copula-GARCH模型及其應(yīng)用[J];系統(tǒng)工程;2004年04期
4 朱新玲;黎鵬;;基于GARCH-CVaR的人民幣匯率風(fēng)險測度研究[J];區(qū)域金融研究;2011年04期
5 劉玨;;基于CVaR-GARCH-M的中國股指期貨風(fēng)險研究[J];北方經(jīng)貿(mào);2014年09期
6 閆素仙;張建強;;中國外匯儲備匯率結(jié)構(gòu)風(fēng)險研究——基于VaR-GARCH模型的實證研究[J];河北經(jīng)貿(mào)大學(xué)學(xué)報;2012年01期
7 封建強;滬、深股市收益率風(fēng)險的極值VaR測度研究[J];統(tǒng)計研究;2002年04期
,本文編號:1457866
本文鏈接:http://www.sikaile.net/guanlilunwen/huobilw/1457866.html
最近更新
教材專著