基于GARCH類和SV類模型的中國債券市場實證分析
發(fā)布時間：2019-04-25 18:47
【摘要】：波動性普遍存在于各種金融時間序列中,是金融研究領(lǐng)域一個核心的問題。目前用于研究金融時間序列波動的模型主要有兩類：一類是自回歸條件異方差(ARCH)模型,另外就是隨機波動(Sv)模型。這兩類模型在近年的實證研究中得到了進一步的發(fā)展,例如ARCH類模型的擴展模型GARCH類模型,sv模型的擴展模型厚尾sv模型等。目前我國已有一些研究用這兩類模型模擬我國的金融市場,其中主要包括股票市場,期權(quán)市場。但是,對于債券市場的研究很少,因此,本文用這兩類模型以及其相應(yīng)的擴展模型對我國的債券市場進行模擬,并通過引入了一系列的評價指標(biāo),客觀的比較了GARCH類模型和SV類模型的波動性預(yù)測能力。 本文先運用TGARCH模型和EGARCH模型對國債、企業(yè)債、金融債指數(shù)收益率數(shù)據(jù)進行實證分析,然后根據(jù)MCMC方法對Sv類模型中的SV-N、SV-T、SV-MN、SV-MT SV-Leverage模型進行了貝葉斯分析。實證結(jié)果發(fā)現(xiàn),國債市場、企業(yè)債市場、金融債市場都表現(xiàn)出一定的波動集群性、尖峰厚尾性和非對稱性。 本文最后引入了RMSE、MAE、LL等評價指標(biāo)對本文選用的模型進行了樣本外預(yù)測能力的比較。結(jié)果發(fā)現(xiàn)SV類模型對我國債券市場的預(yù)測能力明顯強于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債券市場的波動性特征。
[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)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F832.51;F224
本文編號：2465369
[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)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F832.51;F224
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