本文關(guān)鍵詞：基于ARCH模型族的VaR方法在商業(yè)銀行利率風(fēng)險管理中的應(yīng)用　出處：《山東大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 利率風(fēng)險 風(fēng)險度量 GARCH模型 EGRCH模型 VaR

【摘要】：自從1996年我國正式啟動利率市場化改革以來,我國的利率市場化改革穩(wěn)步推進(jìn),先后放開了同業(yè)拆借市場利率、債券市場利率、銀行間市場國債、政策性金融債的發(fā)行利率、境內(nèi)外幣貸款等各種利率。利率放開后,利率對金融環(huán)境波動的敏感性增加,波動的頻率和幅度都顯著提高。商業(yè)銀行的資產(chǎn)主要是金融資產(chǎn),利率變動會導(dǎo)致其資產(chǎn)價值的變動,商業(yè)銀行在利率的波動中承受的利率風(fēng)險會增加。而此前我國商業(yè)銀行的風(fēng)險管理重點(diǎn)主要集中與信用風(fēng)險,對利率管理的經(jīng)驗(yàn)不足,研究利率風(fēng)險管理對我國商業(yè)銀行在利率市場化背景下積極應(yīng)對利率風(fēng)險、建立全面完善的風(fēng)險管理體系至關(guān)重要。 本文共分為五章。第一章主要介紹利率市場化背景下本文的研究意義、國內(nèi)外的研究現(xiàn)狀和本文的研究思路與框架。第二章從外在宏觀因素和內(nèi)在微觀因素兩大方面詳細(xì)分析了商業(yè)銀行的利率風(fēng)險的原因；簡要介紹了敏感性缺口分析法、久期缺口分析法、凸度缺口分析法等利率風(fēng)險的度量方法,并重點(diǎn)介紹了VaR方法包括其原理、計(jì)算方法、優(yōu)缺點(diǎn)等。第三章從金融序列的波動性入手,簡要介紹了ARCH模型及其變形GARCH模型、GARCH-M模型、EGARCH模型和IGARCH模型,并分析了他們的模型特征、適用條件和優(yōu)缺點(diǎn)。另外由于正態(tài)分布的在描述尖峰厚尾性方面的局限性,在模型中引入了兩種分布：t分布和GED分布。第四章以2008年10月8日至2013年12月31日間上海銀行間隔夜拆借利率的1812個數(shù)據(jù)為樣本,用Eviews、Excel等數(shù)據(jù)軟件分析了隔夜拆借利率對數(shù)收益率的平穩(wěn)性、自相關(guān)性、正態(tài)性和尖峰厚尾性、ARCH效應(yīng)等基本統(tǒng)計(jì)特征。第五章分別使用AR(1)-GARCH(1,1), AR(1)-GARCH-M(1,1)-EGARCH(1,1).AR(1)-IGARCH(1,1)等模型對數(shù)據(jù)進(jìn)行了擬合,每種模型均基于正態(tài)分布、t分布、GED分布三種情況作了比較分析；根據(jù)極大似然函數(shù)值、AIC、SC值及模型系數(shù)顯著性檢驗(yàn)結(jié)果等因素,本文確定了擬合最優(yōu)的為基于GED分布的模型；在95%、99%兩種置信水平下計(jì)算了基于各模型的VaR值并對模型擬合效果和VaR值進(jìn)行了結(jié)果分析。 結(jié)果表明,基于GED分布的AR(1)-GARCH(1,1).AR(1)-GARCH-M(1,1).. AR(1)-EGARCH(1,1).AR(1)-IGARCH(1,1)模型能較好地?cái)M合上海銀行間隔夜拆借利率對數(shù)收益率,反映數(shù)據(jù)的尖峰厚尾性。且模型擬合結(jié)果顯示,隔夜拆借利率具有顯著地反杠桿效應(yīng)和長記憶性。95%的置信水平下,只有基于AR(1)-EGARCH(1,1)一GED模型的VaR值通過了Kupiec檢驗(yàn),說明能顯著反映同業(yè)隔夜拆借利率不對稱波動的EARCH模型也能準(zhǔn)確可靠的計(jì)算VaR值且優(yōu)于其他模型。在99%的置信水平下除AR(1)-IGARCH(1,1)模型外,其他三個模型計(jì)算出的VaR值都能通過Kupiec檢驗(yàn),風(fēng)險度量效果良好。
[Abstract]:Since 1996, our country officially launched the interest rate marketization reform, our country interest rate marketization reform has promoted steadily, has successively released the interbank borrowing market interest rate, the bond market interest rate, the interbank market national debt. Interest rates on the issuance of policy-oriented financial bonds, domestic foreign currency loans and other interest rates. After the liberalization of interest rates, the sensitivity of interest rates to fluctuations in the financial environment has increased. The frequency and amplitude of volatility have increased significantly. The assets of commercial banks are mainly financial assets, and the change of interest rate will lead to changes in the value of their assets. The interest rate risk that commercial banks bear in the interest rate fluctuation will increase. But the risk management of our country commercial bank mainly focuses on the credit risk, and the experience of interest rate management is not enough. The study of interest rate risk management is very important for Chinese commercial banks to actively deal with interest rate risk under the background of interest rate marketization and to establish a comprehensive and perfect risk management system. This paper is divided into five chapters. The first chapter mainly introduces the research significance of this paper under the background of interest rate marketization. The second chapter analyzes the reasons of interest rate risk of commercial banks from two aspects of external macro factors and internal and micro factors. This paper briefly introduces the measurement methods of interest rate risk, such as sensitivity gap analysis, duration gap analysis and convexity gap analysis, and focuses on the VaR method, including its principle and calculation method. The third chapter introduces the ARCH model and its modified GARCH model, starting with the volatility of financial series, and introduces the GARCH-M model. EGARCH model and IGARCH model are analyzed, and their characteristics, applicable conditions, advantages and disadvantages are analyzed. In addition, due to the limitations of normal distribution in the description of peak thick tail. Two distributions are introduced into the model:. T distribution and GED distribution. Chapter 4th is based on 1 812 data of Shanghai inter-bank overnight offered rate from October 8th 2008 to December 31st 2013. The stability, autocorrelation, normality and peak and thick tail of the logarithmic yield of overnight interest rate are analyzed by means of Eview Excel and other data software. ARCH effect and other basic statistical characteristics. In Chapter 5th, we used ARGARCH1, ARGARCH-MU 1, ARP1GARCH-MU 1 and EGARCH1, respectively. (1) the data were fitted by the models such as (1). The data were fitted with each model. Each model was compared and analyzed based on the normal distribution and the GED distribution. Based on the maximum likelihood function (MLF) SC and the significance of the model coefficients, the optimal fitting model based on GED distribution is determined in this paper. The VaR values based on each model are calculated at 95% and 99% confidence levels, and the results of model fitting and VaR are analyzed. The results show that the GED distribution is based on the GED distribution. The model can fit the logarithmic yield of Shanghai inter-bank overnight offered interest rate and reflect the peak and thick tail of data. The fitting results show that the model can fit well the logarithmic yield of Shanghai inter-bank interest rate. The overnight lending rate has a significant anti-leverage effect and long memory. 95% confidence level, only based on AR(1)-EGARCH(1. 1) the VaR value of a GED model passed the Kupiec test. It shows that the EARCH model, which can reflect the asymmetric fluctuation of interbank interest rate, can calculate the VaR value accurately and reliably and is superior to other models. -IGARCH1. 1) except the model, the VaR calculated by the other three models can pass the Kupiec test, and the effect of risk measurement is good.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F224;F832.33;F822.0

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