本文關(guān)鍵詞：帶多個(gè)跳躍因子GARCH模型對(duì)中國(guó)股市捕捉能力研究　出處：《南京財(cái)經(jīng)大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： GARCH-JUMP HongLi 多個(gè)跳躍因子 VaR

【摘要】：金融風(fēng)險(xiǎn)的主要來(lái)源是金融資產(chǎn)價(jià)格的波動(dòng)，即金融資產(chǎn)收益率的不確定性，而有關(guān)金融資產(chǎn)的波動(dòng)性研究則一直是國(guó)內(nèi)外學(xué)者研究金融風(fēng)險(xiǎn)問題的重點(diǎn)。近年來(lái)，受新一輪國(guó)際金融危機(jī)的影響，金融風(fēng)險(xiǎn)變得越來(lái)越復(fù)雜，這便推動(dòng)國(guó)內(nèi)外學(xué)者提出更優(yōu)秀的模型來(lái)完美刻畫實(shí)際金融市場(chǎng)價(jià)格波動(dòng)的動(dòng)態(tài)變化趨勢(shì)�；诖耍恼陆Y(jié)合國(guó)內(nèi)外金融時(shí)間序列的最新成果，提出一種新型的時(shí)間序列模型來(lái)捕捉中國(guó)股票市場(chǎng)的收益率與波動(dòng)率變化特征。 本文將雙指數(shù)跳躍因子引入傳統(tǒng)的GARCH族模型，從而利用雙指數(shù)跳躍因子來(lái)擬合中國(guó)股票市場(chǎng)收益率序列呈現(xiàn)的非對(duì)稱性和波動(dòng)杠桿效應(yīng)等特征，實(shí)證結(jié)果顯示，引入雙指數(shù)跳躍因子的GARCH模型與一般GARCH-JUMP模型相比，能夠更好的擬合中國(guó)股票市場(chǎng)收益率與波動(dòng)率的動(dòng)態(tài)變化過程，此外模型的HongLi檢驗(yàn)結(jié)果也表明前者的模型設(shè)定更準(zhǔn)確。但是，HongLi檢驗(yàn)結(jié)果表明帶雙指數(shù)跳躍因子的GARCH模型仍然沒有通過檢驗(yàn)，所以我們?cè)谛履Ｐ偷幕A(chǔ)上引入方差跳躍因子，從而提出帶多個(gè)跳躍因子GARCH模型。實(shí)證結(jié)果表明，新模型能夠更好的擬合中國(guó)股票市場(chǎng)收益率與波動(dòng)率變化過程中存在的尖峰厚尾性、波動(dòng)聚集性、波動(dòng)的杠桿效應(yīng)以及非對(duì)稱性等特征；模型的HongLi檢驗(yàn)結(jié)果也表明帶多個(gè)跳躍因子GARCH模型要比帶雙指數(shù)跳躍因子GARCH模型更準(zhǔn)確，能夠更好的刻畫中國(guó)股票市場(chǎng)收益率與波動(dòng)率的動(dòng)態(tài)變化趨勢(shì)。 本文還基于兩類新的模型對(duì)中國(guó)股市作了VaR風(fēng)險(xiǎn)度量分析，結(jié)果顯示帶多個(gè)跳躍因子的GARCH模型預(yù)測(cè)的VaR值的準(zhǔn)確性要高于帶雙指數(shù)跳躍因子GARCH模型的預(yù)測(cè)結(jié)果，前者的失敗率更接近于我們選取的置信水平所對(duì)應(yīng)的失敗率，更能夠準(zhǔn)確的反映兩類綜合指數(shù)的風(fēng)險(xiǎn)情況。我們認(rèn)為雙指數(shù)跳躍因子GARCH模型預(yù)測(cè)VaR沒有通過檢驗(yàn)的原因可能是其一定程度上高估了風(fēng)險(xiǎn)，從而導(dǎo)致其預(yù)測(cè)VaR的預(yù)測(cè)區(qū)間更廣，，預(yù)測(cè)誤差更大，進(jìn)而一定程度上減少了預(yù)測(cè)的失敗率。
[Abstract]:The main source of financial risk is the fluctuation of financial asset price, that is, the uncertainty of financial asset yield. The volatility of financial assets has been the focus of domestic and foreign scholars on financial risk. In recent years, due to the impact of a new round of international financial crisis, financial risk has become more and more complex. This prompted scholars at home and abroad to put forward a better model to describe the dynamic trends of the real financial market price volatility. Based on this, the article combines the latest results of domestic and foreign financial time series. A new time series model is proposed to capture the characteristics of volatility and yield in Chinese stock market. In this paper, the double index jump factor is introduced into the traditional GARCH family model, and the double index jump factor is used to fit the characteristics of asymmetric and volatility leverage effect in the return series of Chinese stock market. The empirical results show that the GARCH model with double index jump factor can better fit the dynamic process of the return and volatility of Chinese stock market compared with the general GARCH-JUMP model. In addition, the HongLi test results of the model also show that the model setting of the former is more accurate, but the results of the HongLi test show that the GARCH model with double exponential jump factor has not passed the test. So we introduce the variance jump factor on the basis of the new model, and then we put forward the GARCH model with multiple jump factors. The empirical results show that. The new model can better fit the characteristics of the Chinese stock market in the process of the change of return and volatility, such as peak and thick tail, volatility aggregation, the leverage effect of volatility and asymmetry. The results of HongLi test also show that the GARCH model with multiple jump factors is more accurate than the GARCH model with double exponential jump factors. It can better depict the dynamic trend of the return and volatility in Chinese stock market. This paper also makes the VaR risk measurement analysis of Chinese stock market based on two new models. The results show that the accuracy of VaR predicted by GARCH model with multiple jump factors is higher than that of GARCH model with double exponential jump factors. The failure rate of the former is closer to the failure rate corresponding to the confidence level we selected. We think that the double index jump factor GARCH model can not pass the test of VaR may be because it overestimates the risk to a certain extent. Therefore, the prediction range of VaR is wider, the prediction error is larger, and the failure rate of prediction is reduced to a certain extent.
【學(xué)位授予單位】：南京財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F832.51;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 余素紅,張世英;SV與GARCH模型對(duì)金融時(shí)間序列刻畫能力的比較研究[J];系統(tǒng)工程;2002年05期

2 孟利鋒,張世英,何信;具有杠桿效應(yīng)SV模型的貝葉斯分析及其應(yīng)用[J];系統(tǒng)工程;2004年03期

3 劉小茂,李楚霖,王建華;風(fēng)險(xiǎn)資產(chǎn)組合的均值—CVaR有效前沿(Ⅰ)[J];管理工程學(xué)報(bào);2003年01期

4 王春峰,蔣祥林,李剛;基于隨機(jī)波動(dòng)性模型的中國(guó)股市波動(dòng)性估計(jì)[J];管理科學(xué)學(xué)報(bào);2003年04期

5 曹輝;李忠民;;基于Copula理論的VaR算法與實(shí)證分析[J];遼寧工程技術(shù)大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2006年01期

6 孟利鋒,張世英,何信;厚尾SV模型的貝葉斯分析及其應(yīng)用研究[J];西北農(nóng)林科技大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2003年06期

7 王春峰,萬(wàn)海暉,張維;金融市場(chǎng)風(fēng)險(xiǎn)測(cè)量模型——VaR[J];系統(tǒng)工程學(xué)報(bào);2000年01期

8 余素紅,張世英;SV和GARCH模型擬合優(yōu)度比較的似然比檢驗(yàn)[J];系統(tǒng)工程學(xué)報(bào);2004年06期

9 王春峰,蔣祥林,吳曉霖;隨機(jī)波動(dòng)性模型的比較分析[J];系統(tǒng)工程學(xué)報(bào);2005年02期

10 陳金龍,張維;CVaR與投資組合優(yōu)化統(tǒng)一模型[J];系統(tǒng)工程理論方法應(yīng)用;2002年01期

本文編號(hào)：1406073