發(fā)布時(shí)間：2018-11-17 08:07

【摘要】：金融市場(chǎng)波動(dòng)率的統(tǒng)計(jì)特征研究一直是金融領(lǐng)域里一個(gè)十分重要的課題。由于金融波動(dòng)率特殊的研究背景和本身所具有的非線性特征，對(duì)它進(jìn)行估計(jì)和預(yù)測(cè)顯得越來(lái)越重要。 本文建立了ARFIMA-EGARCH-GED模型(擾動(dòng)誤差服從廣義誤差分布，thegeneralizederror distribution，GED)，用來(lái)分析金融波動(dòng)率的非對(duì)稱(chēng)性和長(zhǎng)記憶性這兩個(gè)非線性特征。在該模型擾動(dòng)誤差服從廣義誤差分布的條件下，利用極大似然估計(jì)方法對(duì)模型進(jìn)行估計(jì)，證明了平方誤差高階矩的有界性，得出了極大似然估計(jì)量的漸近正態(tài)性。 為了分析資產(chǎn)收益波動(dòng)的“尖峰厚尾”、長(zhǎng)記憶性和非對(duì)稱(chēng)性特征，，本文提出了ARFIMA-EGARCH-GED波動(dòng)率模型。以滬深綜指為例，在擾動(dòng)誤差分別服從T分布、正態(tài)分布和GED分布的前提下進(jìn)行模型擬合分析，得出ARFIMA-EGARCH-GED模型擬合波動(dòng)率效果最佳。同EGARCH、FIGARCH模型對(duì)比，ARFIMA-EGARCH-GED模型能較好的解決滬深股市收益率波動(dòng)的“尖峰厚尾”、長(zhǎng)記憶性和非對(duì)稱(chēng)性特征，且對(duì)波動(dòng)率擬合效果較好，并對(duì)波動(dòng)率作了短期的預(yù)測(cè)。 鑒于ARFIMA-EGARCH-GED模型對(duì)波動(dòng)率的擬合效果較好，利用模型擬合的波動(dòng)率來(lái)分析波動(dòng)率和收益率的關(guān)系以及不同市場(chǎng)波動(dòng)率之間的關(guān)系。采用局部多項(xiàng)式估計(jì)和N-W(Nadaraya-Watson)核估計(jì)這兩種非參數(shù)方法，得出收益率的絕對(duì)值越大，波動(dòng)率越大；收益率為零時(shí)，波動(dòng)率最小。滬深兩市波動(dòng)率之間整體存在正相關(guān)，局部存在負(fù)相關(guān)。局部多項(xiàng)式估計(jì)優(yōu)于N-W核估計(jì)。
[Abstract]:The study of the statistical characteristics of volatility in financial markets has been a very important subject in the field of finance. Because of the special research background and nonlinear characteristics of financial volatility, it is more and more important to estimate and predict financial volatility. In this paper, the ARFIMA-EGARCH-GED model (generalized error distribution of disturbance error, thegeneralizederror distribution,GED) is established to analyze the asymmetric and long memory characteristics of financial volatility. Under the condition that the model perturbs the generalized error distribution, the maximum likelihood estimation method is used to estimate the model. The boundedness of the high-order moments of the square error is proved, and the asymptotic normality of the maximum likelihood estimator is obtained. In order to analyze the "peak and thick tail", long memory and asymmetric characteristics of asset return volatility, a ARFIMA-EGARCH-GED volatility model is proposed in this paper. Taking Shanghai and Shenzhen Composite Index as an example, under the premise of T distribution, normal distribution and GED distribution respectively, the model fitting analysis is carried out, and the result of ARFIMA-EGARCH-GED model fitting volatility is the best. Compared with the EGARCH,FIGARCH model, the ARFIMA-EGARCH-GED model can solve the "peak and thick tail", long memory and asymmetric characteristics of the volatility of Shanghai and Shenzhen stock market, and the effect of the volatility fitting is better, and the volatility is predicted in the short term. In view of the good fitting effect of ARFIMA-EGARCH-GED model on volatility, the relationship between volatility and yield and the relationship between volatility and market volatility are analyzed by using the volatility fitted by the model. Two nonparametric methods, local polynomial estimation and N-W (Nadaraya-Watson) kernel estimation, are used to obtain that the higher the absolute value of the yield is, the greater the volatility is, and the less the volatility is when the return rate is 00:00. The volatility of Shanghai and Shenzhen stock markets has positive correlation and negative correlation. Local polynomial estimation is superior to N-W kernel estimation.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F224;O212.1;F832.51

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