發(fā)布時(shí)間：2019-04-25 18:47

【摘要】：波動(dòng)性普遍存在于各種金融時(shí)間序列中,是金融研究領(lǐng)域一個(gè)核心的問(wèn)題。目前用于研究金融時(shí)間序列波動(dòng)的模型主要有兩類：一類是自回歸條件異方差(ARCH)模型,另外就是隨機(jī)波動(dòng)(Sv)模型。這兩類模型在近年的實(shí)證研究中得到了進(jìn)一步的發(fā)展,例如ARCH類模型的擴(kuò)展模型GARCH類模型,sv模型的擴(kuò)展模型厚尾sv模型等。目前我國(guó)已有一些研究用這兩類模型模擬我國(guó)的金融市場(chǎng),其中主要包括股票市場(chǎng),期權(quán)市場(chǎng)。但是,對(duì)于債券市場(chǎng)的研究很少,因此,本文用這兩類模型以及其相應(yīng)的擴(kuò)展模型對(duì)我國(guó)的債券市場(chǎng)進(jìn)行模擬,并通過(guò)引入了一系列的評(píng)價(jià)指標(biāo),客觀的比較了GARCH類模型和SV類模型的波動(dòng)性預(yù)測(cè)能力。 本文先運(yùn)用TGARCH模型和EGARCH模型對(duì)國(guó)債、企業(yè)債、金融債指數(shù)收益率數(shù)據(jù)進(jìn)行實(shí)證分析,然后根據(jù)MCMC方法對(duì)Sv類模型中的SV-N、SV-T、SV-MN、SV-MT SV-Leverage模型進(jìn)行了貝葉斯分析。實(shí)證結(jié)果發(fā)現(xiàn),國(guó)債市場(chǎng)、企業(yè)債市場(chǎng)、金融債市場(chǎng)都表現(xiàn)出一定的波動(dòng)集群性、尖峰厚尾性和非對(duì)稱性。 本文最后引入了RMSE、MAE、LL等評(píng)價(jià)指標(biāo)對(duì)本文選用的模型進(jìn)行了樣本外預(yù)測(cè)能力的比較。結(jié)果發(fā)現(xiàn)SV類模型對(duì)我國(guó)債券市場(chǎng)的預(yù)測(cè)能力明顯強(qiáng)于GARCH類模型。因此,本文得到結(jié)論,SV類模型比GARCH類模型能夠更好的模擬我國(guó)債券市場(chǎng)的波動(dòng)性特征。
[Abstract]:Volatility widely exists in various financial time series, is a core issue in the field of financial research. At present, there are two main models used to study the fluctuation of financial time series: one is the autoregressive conditional heteroscedasticity (ARCH) model, the other is the stochastic fluctuation (Sv) model. These two types of models have been further developed in recent years, such as the extended model of ARCH class model, the extended model of GARCH class model, the extended model of sv model, the thick tail sv model, and so on. At present, there have been some studies using these two models to simulate the financial market of our country, including stock market and option market. However, there is little research on bond market, so this paper uses these two kinds of models and their corresponding extended models to simulate the bond market of our country, and introduces a series of evaluation indexes. The volatility prediction ability of GARCH model and SV class model is compared objectively. In this paper, the TGARCH model and EGARCH model are used to analyze the yield data of national debt, enterprise bond and financial bond index. Then, according to the MCMC method, the SV-N,SV-T,SV-MN, in the Sv class model is analyzed. Bayesian analysis is performed on the SV-MT SV-Leverage model. The empirical results show that the bond market, the corporate bond market and the financial bond market all show some volatility clustering, peak thick tail and asymmetry. At the end of this paper, RMSE,MAE,LL and other evaluation indexes are introduced to compare the out-of-sample prediction ability of the model selected in this paper. The results show that the forecasting ability of the SV model is stronger than that of the GARCH model in the bond market of our country. Therefore, this paper draws a conclusion that the SV model can simulate the volatility of China's bond market better than the GARCH model.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F832.51;F224

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