本文關(guān)鍵詞：金融時(shí)間序列的波動(dòng)率分析　出處：《華東師范大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 波動(dòng)率 均值模型 ARCH模型 GARCH模型族

【摘要】：本文對(duì)中國股市的波動(dòng)率進(jìn)行了研究,分別選取上證指數(shù)和深圳綜指的對(duì)數(shù)收益率序列為樣本序列。結(jié)合假設(shè)檢驗(yàn)和統(tǒng)計(jì)推斷的分析方法,在正文中驗(yàn)證了樣本數(shù)據(jù)的自相關(guān)性,平穩(wěn)性等統(tǒng)計(jì)特征,并發(fā)現(xiàn)數(shù)據(jù)并非是正態(tài)分布的,而是表現(xiàn)出一種高峰厚尾的性質(zhì)。在第五章中具體選取了兩組樣本數(shù)據(jù),分別對(duì)其擬合七組模型,挖掘波動(dòng)率的條件異方差,非對(duì)稱性等性質(zhì)。結(jié)果發(fā)現(xiàn)隨著模型的改進(jìn),擬合結(jié)果有所改善。就方差模型而言,GARCH模型的擬合效果要好于ARCH模型；TGARCH模型和EGARCH模型的擬合效果較為接近,且均優(yōu)于GARCH模型；進(jìn)一步,在均值模型中加入分?jǐn)?shù)差分的概念,使用ARFIMA模型,在此基礎(chǔ)上再建立方差模型,其結(jié)果要優(yōu)于均值模型為ARMA模型的情況。最后發(fā)現(xiàn)ARFIMA-TGARCH模型是所有模型中擬合效果最好的,這是因?yàn)槟Ｐ统浞痔崛×诵蛄械拈L(zhǎng)記憶性,杠桿效應(yīng)等信息。
[Abstract]:In this paper, the volatility of Chinese stock market is studied. The logarithmic return series of Shanghai Stock Exchange Index and Shenzhen Composite Index are selected as sample series, combined with hypothesis test and statistical inference analysis method. The autocorrelation and stationarity of the sample data are verified in the text, and it is found that the data are not normal distribution. In Chapter 5th, two groups of sample data were selected to fit seven groups of models respectively, and the conditional heteroscedasticity of volatility was mined. The results show that with the improvement of the model, the fitting results are improved. In terms of variance model, the fitting effect of GARCH model is better than that of ARCH model. The fitting effect of TGARCH model and EGARCH model is close and better than that of GARCH model. Furthermore, the concept of fractional difference is added to the mean value model, and then the variance model is established by using ARFIMA model. The result is better than the mean model is ARMA model. Finally, it is found that the ARFIMA-TGARCH model is the best fit among all the models. This is because the model fully extracts the information of long memory, leverage effect and so on.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F832.51;F224;O212.1

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本文編號(hào)：1417794