利率市場化背景下商業(yè)銀行利率風(fēng)險的測度
發(fā)布時間：2018-07-20 14:55
【摘要】：中國利率市場化改革開始于20世紀90年代中期,最近一次具有突破性的改革舉措是2013年7月20日金融機構(gòu)貸款利率的放開,這標志著我國利率市場化改革進入了實質(zhì)性的階段。利率實行市場化后,將會加劇商業(yè)銀行之間的競爭、加大經(jīng)營和管理受利率波動的影響,進而商業(yè)銀行面臨的最主要市場風(fēng)險將會是利率風(fēng)險,因此商業(yè)銀行應(yīng)當(dāng)重視識別與防范利率風(fēng)險,然而識別與防范利率風(fēng)險最關(guān)鍵的就是對利率風(fēng)險進行有效測度,本文基于利率市場化背景下,探究商業(yè)銀行利率風(fēng)險的有效測度方法,為我國商業(yè)銀行利率風(fēng)險的測度與管理提供一定的理論依據(jù)。為此,本文分別構(gòu)建了兩種不同的利率風(fēng)險測度模型,通過比較分析選出較好的一種作為利率風(fēng)險測度模型。第一種模型是在構(gòu)建不同GARCH類參數(shù)、非參數(shù)的VaR模型基礎(chǔ)上得到的APGARCH-GED-MC非參數(shù)VaR模型。通過比較隨機變量分別服從正態(tài)、t和GED分布的GARCH(1,1)、EGARCH(1,1)和APGARCH(1,1)這9個模型,選出最優(yōu)的APGARCH(1,1)-GED模型進行樣本外預(yù)測,將得到的條件標準差σ作為MC模擬中利率波動的標準差,模擬出不同分布下的利率,將預(yù)測的利率差作為利率風(fēng)險的VaR值。由Kupiec失敗率檢驗法可知APGARCH-GED-MC VaR模型較參數(shù)法和非參數(shù)法計算的利率風(fēng)險VaR值與實際收益率的差異小,失敗率較GARCH類參數(shù)、非參數(shù)的VaR模型有所降低。第二種模型是從VaR定義(VaR是一定置信水平下的分位數(shù))、分位數(shù)回歸可應(yīng)用在金融風(fēng)險領(lǐng)域以及在構(gòu)建第一種模型時發(fā)現(xiàn)收益率服從何種分布形式對計算VaR的影響較大角度出發(fā),采用分位數(shù)回歸的方法構(gòu)建測度利率風(fēng)險的VaR模型。分別構(gòu)建兩類分位數(shù)回歸VaR模型：分位數(shù)回歸的非遞歸和遞歸VaR模型。其中分位數(shù)回歸的非遞歸VaR模型記為：QR.APGARCH-GED模型(非對稱的PGARCH-GED分位數(shù)回歸VaR模型),模型的解釋變量σt+1,和σt+12由APGARCH(1,1)-GED進行樣本外預(yù)測得到；分位數(shù)回歸的遞歸VaR模型記為：AAVS-CAViaR模型(不對稱絕對值、斜率的條件分位數(shù)自回歸),該模型將VaR的自相關(guān)性和收益率的不對稱性綜合考慮在解釋變量中。仍采用Kupiec失敗率檢驗法對這兩個基于分位數(shù)回歸的VaR模型進行回測檢驗,發(fā)現(xiàn)QR.APGARCH-GED VaR模型計算的利率風(fēng)險VaR值不僅對實際收益率有較好的覆蓋性,而且降低了預(yù)測的失敗率。綜上所述,本文是從兩種不同的角度分別構(gòu)建利率風(fēng)險測度的VaR模型。首先,通過Kupiec失敗率檢驗發(fā)現(xiàn)在構(gòu)建的第一種VaR模型過程中,由PGARCH-GED-MC VaR模型預(yù)測的利率計算的利率風(fēng)險VaR值與實際收益率較GARCH類參數(shù)、非參數(shù)的VaR模型接近,失敗率也有所降低,因此選擇APGARCH-GED-MC VaR模型作為本文第一種測度利率風(fēng)險的VaR模型；其次,在構(gòu)建分位數(shù)回歸VaR模型過程中,根據(jù)QR.APGARCH-GED VaR模型預(yù)測計算的利率風(fēng)險VaR值對實際收益率有較好的區(qū)間覆蓋,失敗率比AAVS-CAViaR模型低,因此選擇QR.APGARCH-GED VaR模型作為本文構(gòu)建的第二種測度利率風(fēng)險的VaR模型；最后,通過比較APGARCH-GED-MC VaR模型與QR.APGARCH-GED VaR模型的失敗天數(shù),發(fā)現(xiàn)根據(jù)QR.APGARCH-GED VaR模型計算的VaR值,不僅大大降低了模型預(yù)測的失敗天數(shù),而且對實際收益率有較好的區(qū)間覆蓋,在實際收益率大幅波動的某些時間段,計算的利率風(fēng)險VaR值也能隨之劇烈波動�；诖吮疚淖罱K選擇QR.APGARCH-GED VaR模型作為當(dāng)前利率市場化水平下商業(yè)銀行利率風(fēng)險度量模型。該模型不僅為我國現(xiàn)階段商業(yè)銀行利率風(fēng)險的測度與管理提供了理論依據(jù),同時為市場化程度更高階段的利率風(fēng)險測度提供了參考。
[Abstract]:The reform of China's interest rate marketization began in the middle of the 1990s. The recent breakthrough of the reform was the release of the loan interest rate of the financial institutions in July 20, 2013, which marks the substantive stage of the reform of the interest rate marketization in China. After the interest rate is marketed, the competition between commercial banks will be intensified and the operation will be increased. The most important market risk faced by commercial banks will be interest rate risk. Therefore, commercial banks should pay attention to the recognition and prevention of interest rate risk. However, the key to identify and prevent interest rate risk is to measure the interest rate risk effectively. Based on the background of interest rate marketization, this paper explores commercial banks. The effective measure method of bank interest rate risk provides a certain theoretical basis for the measurement and management of interest rate risk of commercial banks in China. This paper constructs two different interest rate risk measurement models respectively, and selects a better model of interest rate risk measurement by comparison and analysis. The first model is to construct different GARCH classes. The APGARCH-GED-MC nonparametric VaR model is obtained on the basis of the non parametric VaR model. By comparing the 9 models of normal, t and GED distribution GARCH (1,1), EGARCH (1,1) and APGARCH (1,1), the optimal APGARCH model is selected for the external prediction, and the obtained conditional standard difference Sigma is used as the interest rate in the simulation. The standard deviation of the fluctuation is used to simulate the interest rate under the different distribution, and the interest rate difference is predicted as the VaR value of the interest rate risk. The Kupiec failure rate test shows that the difference between the VaR value of the APGARCH-GED-MC VaR model and the actual rate of return calculated by the parameter method and the non parameter method is smaller than the GARCH parameter and the non parameter VaR model. The second model is from the VaR definition (VaR is the quantile under a certain confidence level). The quantile regression can be applied to the financial risk field and in the construction of the first model of the distribution of the rate of return on the calculation of the impact of the VaR, the method of quantile regression is used to construct the VaR model for measuring the risk of interest rate. Two types of quantile regression VaR models are constructed, respectively, the non recursive and recursive VaR models of quantile regression. The non recursive VaR model of quantile regression is recorded as the QR.APGARCH-GED model (asymmetric PGARCH-GED quantile regression VaR model), the explanatory variable of the model, sigma t+1, and the sigma t+12 from APGARCH (1,1) -GED. The recursive VaR model of quantile regression is recorded as the AAVS-CAViaR model (asymmetric absolute value, the conditional quantile autoregression of the slope). The model considers the autocorrelation of VaR and the asymmetry of the rate of return in the explanatory variables. The Kupiec failure rate test is still used to retest the two VaR models based on the quantile regression. It is found that the interest rate risk VaR calculated by the QR.APGARCH-GED VaR model not only has a good coverage of the actual rate of return, but also reduces the failure rate of the forecast. In summary, this paper constructs the VaR model of the interest rate risk measurement from two different angles. First, the first VaR model is found by the Kupiec failure rate test. In the process, the interest rate risk VaR calculated by the PGARCH-GED-MC VaR model is close to the GARCH class parameter, the non parametric VaR model and the failure rate. Therefore, the APGARCH-GED-MC VaR model is selected as the first VaR model of the interest rate risk measurement in this paper. Secondly, the quantile regression VaR model is constructed. In the process, the interest rate risk VaR calculated according to the QR.APGARCH-GED VaR model has a better interval coverage and the failure rate is lower than that of the AAVS-CAViaR model. Therefore, the QR.APGARCH-GED VaR model is selected as the second VaR model to measure the interest rate risk of this paper. Finally, the APGARCH-GED-MC VaR model and QR are compared by comparing the APGARCH-GED-MC VaR model and QR. The number of failure days of the.APGARCH-GED VaR model shows that the VaR value calculated by the QR.APGARCH-GED VaR model not only greatly reduces the number of failure days of the model prediction, but also has a better interval coverage for the actual rate of return, and the calculated interest rate risk VaR can also fluctuate sharply in some time periods of the substantial fluctuation of real returns. Based on this In this paper, the QR.APGARCH-GED VaR model is selected as the current interest rate risk measurement model of commercial banks under the current market level of interest rate. This model not only provides a theoretical basis for the measurement and management of interest rate risk of commercial banks in China at present, but also provides a reference for the measurement of interest rate risk in a higher stage of marketization.
【學(xué)位授予單位】：南京財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F832.33
本文編號：2133894
[Abstract]:The reform of China's interest rate marketization began in the middle of the 1990s. The recent breakthrough of the reform was the release of the loan interest rate of the financial institutions in July 20, 2013, which marks the substantive stage of the reform of the interest rate marketization in China. After the interest rate is marketed, the competition between commercial banks will be intensified and the operation will be increased. The most important market risk faced by commercial banks will be interest rate risk. Therefore, commercial banks should pay attention to the recognition and prevention of interest rate risk. However, the key to identify and prevent interest rate risk is to measure the interest rate risk effectively. Based on the background of interest rate marketization, this paper explores commercial banks. The effective measure method of bank interest rate risk provides a certain theoretical basis for the measurement and management of interest rate risk of commercial banks in China. This paper constructs two different interest rate risk measurement models respectively, and selects a better model of interest rate risk measurement by comparison and analysis. The first model is to construct different GARCH classes. The APGARCH-GED-MC nonparametric VaR model is obtained on the basis of the non parametric VaR model. By comparing the 9 models of normal, t and GED distribution GARCH (1,1), EGARCH (1,1) and APGARCH (1,1), the optimal APGARCH model is selected for the external prediction, and the obtained conditional standard difference Sigma is used as the interest rate in the simulation. The standard deviation of the fluctuation is used to simulate the interest rate under the different distribution, and the interest rate difference is predicted as the VaR value of the interest rate risk. The Kupiec failure rate test shows that the difference between the VaR value of the APGARCH-GED-MC VaR model and the actual rate of return calculated by the parameter method and the non parameter method is smaller than the GARCH parameter and the non parameter VaR model. The second model is from the VaR definition (VaR is the quantile under a certain confidence level). The quantile regression can be applied to the financial risk field and in the construction of the first model of the distribution of the rate of return on the calculation of the impact of the VaR, the method of quantile regression is used to construct the VaR model for measuring the risk of interest rate. Two types of quantile regression VaR models are constructed, respectively, the non recursive and recursive VaR models of quantile regression. The non recursive VaR model of quantile regression is recorded as the QR.APGARCH-GED model (asymmetric PGARCH-GED quantile regression VaR model), the explanatory variable of the model, sigma t+1, and the sigma t+12 from APGARCH (1,1) -GED. The recursive VaR model of quantile regression is recorded as the AAVS-CAViaR model (asymmetric absolute value, the conditional quantile autoregression of the slope). The model considers the autocorrelation of VaR and the asymmetry of the rate of return in the explanatory variables. The Kupiec failure rate test is still used to retest the two VaR models based on the quantile regression. It is found that the interest rate risk VaR calculated by the QR.APGARCH-GED VaR model not only has a good coverage of the actual rate of return, but also reduces the failure rate of the forecast. In summary, this paper constructs the VaR model of the interest rate risk measurement from two different angles. First, the first VaR model is found by the Kupiec failure rate test. In the process, the interest rate risk VaR calculated by the PGARCH-GED-MC VaR model is close to the GARCH class parameter, the non parametric VaR model and the failure rate. Therefore, the APGARCH-GED-MC VaR model is selected as the first VaR model of the interest rate risk measurement in this paper. Secondly, the quantile regression VaR model is constructed. In the process, the interest rate risk VaR calculated according to the QR.APGARCH-GED VaR model has a better interval coverage and the failure rate is lower than that of the AAVS-CAViaR model. Therefore, the QR.APGARCH-GED VaR model is selected as the second VaR model to measure the interest rate risk of this paper. Finally, the APGARCH-GED-MC VaR model and QR are compared by comparing the APGARCH-GED-MC VaR model and QR. The number of failure days of the.APGARCH-GED VaR model shows that the VaR value calculated by the QR.APGARCH-GED VaR model not only greatly reduces the number of failure days of the model prediction, but also has a better interval coverage for the actual rate of return, and the calculated interest rate risk VaR can also fluctuate sharply in some time periods of the substantial fluctuation of real returns. Based on this In this paper, the QR.APGARCH-GED VaR model is selected as the current interest rate risk measurement model of commercial banks under the current market level of interest rate. This model not only provides a theoretical basis for the measurement and management of interest rate risk of commercial banks in China at present, but also provides a reference for the measurement of interest rate risk in a higher stage of marketization.
【學(xué)位授予單位】：南京財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F832.33
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