發(fā)布時(shí)間：2018-04-12 16:18

  本文選題：銀行間國(guó)債市場(chǎng) + 動(dòng)態(tài)Nelson-Siegel期限結(jié)構(gòu)�。� 參考：《西南財(cái)經(jīng)大學(xué)》2013年碩士論文

【摘要】：在利率市場(chǎng)化的大環(huán)境下,金融機(jī)構(gòu)對(duì)于利率風(fēng)險(xiǎn)的控制和對(duì)資產(chǎn)配置的需求使得關(guān)于利率期限結(jié)構(gòu)的研究變得越來(lái)越有意義。利率期限結(jié)構(gòu)不僅能為投資者提供組合管理的建議,還能為監(jiān)管機(jī)構(gòu)提供宏觀調(diào)控的思路。在實(shí)際應(yīng)用中,對(duì)于利率期限結(jié)構(gòu)的擬合效果和預(yù)測(cè)能力的研究更是利率類模型的核心。 現(xiàn)代利率期限結(jié)構(gòu)研究主要包含靜態(tài)模型、無(wú)套利模型、均衡模型、宏觀金融模型、混合模型。靜態(tài)模型對(duì)某個(gè)時(shí)點(diǎn)上的利率期限結(jié)構(gòu)進(jìn)行估計(jì),主要的相關(guān)研究包括McCulloch (1971)、Vasicek等(1982)的樣條法和Fama等(1987)的息票剝離法；無(wú)套利模型和均衡模型從利率的隨機(jī)過(guò)程出發(fā),主要的模型包括Vasicek模型,CIR模型和HJM模型；宏觀金融模型將宏觀變量與利率期限結(jié)構(gòu)相結(jié)合,主要的研究包括Wu(2003)實(shí)證了宏觀經(jīng)濟(jì)沖擊收益率曲線的影響、Ang等(2003)將宏觀經(jīng)濟(jì)變量引入均衡模型；混合模型對(duì)以上模型取長(zhǎng)補(bǔ)短,主要的研究包括Diebold等(2002)(2006)建立的動(dòng)態(tài)Nelson-Siegel模型。國(guó)內(nèi)對(duì)于利率期限結(jié)構(gòu)主要研究包括朱世武等(2003)和余文龍等(2010)通過(guò)Nelson-Siegel模型對(duì)交易所利率期限結(jié)構(gòu)的研究。 靜態(tài)模型和動(dòng)態(tài)模型的存在的共同問(wèn)題是,盡管對(duì)利率期限結(jié)構(gòu)擬合效果較好,但相對(duì)于宏觀金融模型和混合模型,其預(yù)測(cè)能力較差；國(guó)內(nèi)學(xué)者嘗試采用宏觀金融模型結(jié)合國(guó)內(nèi)經(jīng)濟(jì)變量對(duì)利率期限結(jié)構(gòu)進(jìn)行解釋和預(yù)測(cè),但這些研究主要采用交易所或銀行間債券收盤價(jià)格擬合利率期限結(jié)構(gòu),沒(méi)有針對(duì)銀行間報(bào)價(jià)的研究。 本文主要采用銀行間雙邊報(bào)價(jià)數(shù)據(jù),基于動(dòng)態(tài)Nelson-Siegel模型,計(jì)算出對(duì)利率期限結(jié)構(gòu)具有代表作用的三個(gè)因子,并通過(guò)誤差修正模型(VECM)與宏觀經(jīng)濟(jì)變量相結(jié)合,構(gòu)建出適合我國(guó)利率期限的混合模型。在對(duì)模型各變量提出有意義的經(jīng)濟(jì)解釋的同時(shí),發(fā)現(xiàn)其樣本外預(yù)測(cè)能力高于普通動(dòng)態(tài)Nelson-Siegel模型。 全文共6個(gè)部分,各部分內(nèi)容安排如下。第一部分是引言,介紹論文研究的背景和意義、研究方法和研究工具、實(shí)證主要內(nèi)容及詳細(xì)步驟,最后指明了文章的創(chuàng)新之處。 第二部分是對(duì)國(guó)內(nèi)外利率期限結(jié)構(gòu)研究進(jìn)行文獻(xiàn)綜述。從傳統(tǒng)的利率期限結(jié)構(gòu)理論出發(fā),到現(xiàn)代利率期限結(jié)構(gòu)模型包括期限結(jié)構(gòu)靜態(tài)估計(jì)方法、動(dòng)態(tài)模型、混合模型和宏觀金融模型等。對(duì)國(guó)內(nèi)利率期限結(jié)構(gòu)也從靜態(tài)模型,動(dòng)態(tài)模型,Nelson-Siegel模型、銀行間利率期限結(jié)構(gòu)等不同角度進(jìn)行了綜述。 第三部分是介紹模型與檢驗(yàn)的原理和實(shí)現(xiàn)方法。首先介紹了Nelson-Siegel模型的模型設(shè)定以及水平因子,斜率因子,曲率因子的數(shù)學(xué)含義。然后給出了狀態(tài)空間模型的概念以及卡爾曼濾波器的概念、濾波的過(guò)程以及如何對(duì)模型中的參數(shù)進(jìn)行估計(jì)。再介紹了本文所采用的的數(shù)值計(jì)算方法。最后介紹了文中使用的一系列時(shí)間序列模型,包括向量自回歸模型,向量誤差修正模型,Johansen協(xié)整檢驗(yàn)的基本思想。 第四部分是數(shù)據(jù)來(lái)源及處理。首先對(duì)選擇銀行間債券市場(chǎng)雙邊報(bào)價(jià)數(shù)據(jù)作為本文研究的對(duì)象的原因進(jìn)行說(shuō)明。然后對(duì)該數(shù)據(jù)進(jìn)行預(yù)處理,并且通過(guò)Fama-Bliss方法將銀行間國(guó)債雙邊價(jià)格數(shù)據(jù)轉(zhuǎn)化為零息票債券收益率日數(shù)據(jù)。 第五部分是實(shí)證研究,主要工作包括對(duì)模型參數(shù)的估計(jì)以及預(yù)測(cè)能力的研究。第一步是在前序工作的基礎(chǔ)上對(duì)NS模型三因子以及規(guī)模參數(shù)λ進(jìn)行估計(jì)。第二步對(duì)三因子是建立向量自回歸模型并檢驗(yàn)預(yù)測(cè)能力。第四步將三因子與宏觀經(jīng)濟(jì)變量結(jié)合起來(lái)建立誤差修正模型考察因子與宏觀經(jīng)濟(jì)數(shù)據(jù)的關(guān)系并改進(jìn)了模型的預(yù)測(cè)能力。 最后,對(duì)研究得到的結(jié)論進(jìn)行總結(jié),給出建議,提出不足之處并對(duì)進(jìn)一步研究的方向進(jìn)行展望。 本文通過(guò)研究發(fā)現(xiàn),第一,水平、斜率、曲率三因子與居民消費(fèi)價(jià)格指數(shù)(CPI)和工業(yè)增加值(IP)存在一個(gè)長(zhǎng)期穩(wěn)定的協(xié)整關(guān)系。第二,發(fā)現(xiàn)加入宏觀變量后的誤差修正模型(VECM)在短、中期限的預(yù)測(cè)能力得到了明顯改善,強(qiáng)于無(wú)約束的向量自回歸模型(UVAR)模型。 本文的創(chuàng)新之處在于以下三個(gè)方面。首先,本文利用Nelson-Siegel系列模型估計(jì)出來(lái)的三個(gè)因子與宏觀經(jīng)濟(jì)變量通過(guò)誤差修正模型建立起穩(wěn)定的模型并提高了普通向量自回歸模型的預(yù)測(cè)能力。其次,本文不同于一般的文章選擇交易所債券數(shù)據(jù)對(duì)利率期限結(jié)構(gòu)進(jìn)行研究,轉(zhuǎn)而選擇了銀行間市場(chǎng)雙邊報(bào)價(jià)數(shù)據(jù)進(jìn)行研究。最后,文章采用數(shù)值方法結(jié)合最小二乘法估計(jì)的三因子的平均值作為卡爾曼濾波器三因子的初值,提高了估計(jì)的準(zhǔn)確性。 本文未來(lái)發(fā)展方向有以下兩個(gè)方面。第一,從本文中構(gòu)建的模型出發(fā),沿著文中的建模思路繼續(xù)尋找合適的宏觀經(jīng)濟(jì)因子提高模型的預(yù)測(cè)能力。第二,參照Koopman(2010)中的方法,首先將規(guī)模參數(shù)λ考慮為第四個(gè)可變因子進(jìn)入到狀態(tài)空間模型之中,通過(guò)擴(kuò)展的卡爾曼濾波(Extended Kalman)對(duì)模型參數(shù)進(jìn)行估計(jì),然后采用本文中的方法,將規(guī)模參數(shù)也引入整體模型設(shè)計(jì)之中,提高模型的擬合效果和預(yù)測(cè)能力。
[Abstract]:In the interest rate market environment, financial institutions have become more and more important for the control of interest rate risk and asset allocation on demand makes the research on the term structure of interest rates. The term structure of interest rates can not only provide portfolio management recommendations for investors, but also provide the macro-control ideas for supervision. In practical application. Study on interest rate term structure fitting effect and prediction ability is the core interest rate model.
Study on the modern term structure mainly includes the static model, no arbitrage model, the equilibrium model, the macro finance model, hybrid model. The static model to estimate the term structure of interest rates a point in time, related research mainly include McCulloch (1971), Vasicek (1982) of the spline method and Fama (1987). Bootstrap method; no arbitrage model and equilibrium model starting from the stochastic process of interest rate, the main models include Vasicek model, CIR model and HJM model; the combination of macro financial model of macroeconomic variables and the term structure of interest rate, the main research include Wu (2003) positive impact of macroeconomic shocks, the yield curve, Ang etc. (2003) the macroeconomic variables into the equilibrium model; hybrid model of the above models complement each other, the main research including Diebold (2002) Nelson-Siegel (2006) dynamic model. For the domestic interest rate period The main studies of the limited structure include the study of the term structure of the exchange rate through the Nelson-Siegel model (2003) and Yu Wenlong (2010), including (2003) and Yu Wenlong.
There is a common problem of static model and dynamic model is better, although on the interest rate term structure fitting effect, but compared to the macro financial model and mixed model, the prediction ability is poor; domestic scholars try to use the macro financial model combined with the domestic economic variables of interest rate term structure to explain and predict, but these studies mainly by the exchange or bank the bond between the closing price fit the term structure of interest rates, not on the inter-bank offer.
This paper mainly uses bilateral quotation data between banks, based on dynamic Nelson-Siegel model, calculate the three factor has effect on the term structure of interest rate, and the error correction model (VECM) combined with macroeconomic variables, build a hybrid model for China's interest rates. In the interpretation of meaning of the economy at the same time the variable model, the sample forecasting ability is higher than the general dynamic model of Nelson-Siegel.
The full text is divided into 6 parts. The contents of each part are arranged as follows. The first part is the introduction, which introduces the background and significance of the research, research methods and research tools, main contents and detailed steps. Finally, it points out the innovation of the article.
The second part is the literature review on the structure of domestic and foreign interest rate term. Starting from the traditional theory of term structure of interest rates, to the modern interest rate term structure model including static term structure estimation method, dynamic model, hybrid model and macro finance model. On the domestic interest rate term structure also from the static model, dynamic model, Nelson-Siegel model, bank the term structure of interest rates between different angles are reviewed.
The third part is to introduce the principle and test model and realization method. Firstly introduces the model of the Nelson-Siegel model and the level factor, slope factor and the curvature factor. Then the mathematical meaning gives the concept of state space model and the concept of Calman filter, filter process and how to estimate the parameters of the model are introduced. The numerical calculation method used in this paper. Finally introduced a series of time series model used in this paper, including the vector auto regression model, vector error correction model, the Johansen cointegration test base.
The fourth part is the data sources and processing. The choice of the inter-bank bond market quotations data as the research object of the paper reasons are explained. Then the data were preprocessed by Fama-Bliss method and bilateral inter-bank bond price data into zero coupon bond yields on the data.
The fifth part is the empirical research, the research work mainly includes the estimation of model parameters and prediction ability. The first step is based on pre order on the three factor NS model and scale parameter estimation. The second step of the three factor is the establishment of vector autoregressive model and test the predictive ability. The fourth step will be the three factor and the macroeconomic variables are combined to set up error correction model to examine factors and macroeconomic data and improve the prediction ability of the model.
Finally, the conclusions of the research are summarized, the suggestions are given, the shortcomings are put forward and the direction of further research is prospected.
The study found that, first, the level, slope, curvature factor three and the consumer price index (CPI) and industrial added value (IP) there is a long-term stable cointegration relationship. Second, found that the error correction model after adding macro variables (VECM) in the short and medium-term prediction ability are significantly improved, stronger than the unconstrained vector autoregressive model (UVAR model).
The innovation of this paper lies in the following three aspects. Firstly, the three factors and macroeconomic variables by Nelson-Siegel model estimated model to establish a stable model and improve the prediction ability of ordinary vector autoregressive model through error correction. Secondly, this paper from the general choice of the exchange bond data of interest rate the term structure is different, in favor of the inter-bank market quotations data for research. Finally, the numerical method combined with the average value of three factor least squares method to estimate the initial value as Calman filter three factors, to improve the estimation accuracy.
The future development direction of this paper are the following two aspects. First, starting from the construction of the model in this paper, to find appropriate macroeconomic factors improve the prediction ability of the model along the modeling method in this paper. Second, according to Koopman (2010) in the first method, the scale parameter is considered as the fourth variable factors into the state the space model, the extended Calman filter (Extended Kalman) to estimate the parameter of the model, and then using the method in this paper, the scale parameter is also introduced to the whole model design, improve the model fitting effect and prediction ability.

【學(xué)位授予單位】：西南財(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;F812.5

【共引文獻(xiàn)】

相關(guān)會(huì)議論文 前3條

1 何晨;張強(qiáng);;我國(guó)利率期限結(jié)構(gòu)擬合估計(jì)[A];第三屆（2008）中國(guó)管理學(xué)年會(huì)論文集[C];2008年

2 余文龍;王安興;;基于動(dòng)態(tài)Nelson-Siegel模型的國(guó)債管理策略分析[A];經(jīng)濟(jì)學(xué)（季刊）第9卷第4期[C];2010年

3 何晨;張強(qiáng);;我國(guó)利率期限結(jié)構(gòu)擬合估計(jì)[A];第三屆（2008）中國(guó)管理學(xué)年會(huì)——信息管理分會(huì)場(chǎng)論文集[C];2008年

相關(guān)博士學(xué)位論文 前10條

1 康書(shū)隆;國(guó)債利率的風(fēng)險(xiǎn)特征、變化規(guī)律及風(fēng)險(xiǎn)管理研究[D];東北財(cái)經(jīng)大學(xué);2010年

2 張蕊;中國(guó)債券市場(chǎng)流動(dòng)性問(wèn)題研究[D];天津大學(xué);2010年

3 蘇云鵬;利率期限結(jié)構(gòu)理論、模型及應(yīng)用研究[D];天津大學(xué);2010年

4 林海;中國(guó)利率期限結(jié)構(gòu)及應(yīng)用研究[D];廈門大學(xué);2003年

5 劉小坤;企業(yè)債券：信用風(fēng)險(xiǎn)與市場(chǎng)監(jiān)管研究[D];復(fù)旦大學(xué);2005年

6 黃佐敇;利率衍生產(chǎn)品的套期保值研究[D];河海大學(xué);2006年

7 胡海鵬;利率期限結(jié)構(gòu)理論與應(yīng)用研究[D];中國(guó)科學(xué)技術(shù)大學(xué);2006年

8 焦志常;固定收益證券組合投資與風(fēng)險(xiǎn)管理研究[D];吉林大學(xué);2006年

9 張文剛;利率期限結(jié)構(gòu)模型與應(yīng)用[D];吉林大學(xué);2006年

10 陳暉;利率期限結(jié)構(gòu)的最優(yōu)估計(jì)及其應(yīng)用研究[D];湖南大學(xué);2006年

相關(guān)碩士學(xué)位論文 前10條

1 王進(jìn)勇;人民幣利率互換之定價(jià)方法與應(yīng)用研究[D];江南大學(xué);2010年

2 吳強(qiáng);中國(guó)城市土地證券化研究[D];中南林業(yè)科技大學(xué);2009年

3 賀暢達(dá);中國(guó)利率期限結(jié)構(gòu)動(dòng)態(tài)估計(jì)[D];東北財(cái)經(jīng)大學(xué);2010年

4 張玉倩;中國(guó)市場(chǎng)利率期限結(jié)構(gòu)及其影響因素的實(shí)證研究[D];東北財(cái)經(jīng)大學(xué);2010年

5 馮爾捷;證券市場(chǎng)變量是否可以預(yù)測(cè)實(shí)體經(jīng)濟(jì)[D];浙江工商大學(xué);2011年

6 曹晶晶;幾類利率模型的參數(shù)估計(jì)和偏差分析[D];東華大學(xué);2011年

7 滿志福;基于光順B樣條的利率期限結(jié)構(gòu)擬合[D];吉林大學(xué);2011年

8 李珍;基于單因素HJM模型的利率衍生品定價(jià)研究[D];大連理工大學(xué);2011年

9 韓俊萌;我國(guó)國(guó)債利率期限結(jié)構(gòu)研究[D];蘭州理工大學(xué);2011年

10 陸倩;資金約束下的多階段套期保值及投資組合研究[D];華南理工大學(xué);2011年

，

本文編號(hào)：1740487