本文關(guān)鍵詞：不同類型商業(yè)銀行不良貸款率影響因素實(shí)證研究　出處：《云南財(cái)經(jīng)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 不良貸款率 混合效應(yīng)模型 基于正交化技術(shù)的SCAD型固定效應(yīng)選擇 隨機(jī)效應(yīng)檢驗(yàn)

【摘要】：近年我國商業(yè)銀行的不良貸款率持續(xù)攀升,截至2016年第3季度,已連漲19個(gè)季度。對影響商業(yè)銀行不良貸款率的因素進(jìn)行研究,能夠夯實(shí)其經(jīng)營基礎(chǔ),有利于抵御潛在風(fēng)險(xiǎn)。同時(shí),通過對不同類型商業(yè)銀行不良貸款率影響因素的分析,可以為金融監(jiān)管部門決策提供一些參考。為全面地考慮不良貸款率的影響因素,本文選擇含有固定效應(yīng)和隨機(jī)效應(yīng)的混合效應(yīng)模型。由于相對于樣本量而言,初始解釋變量的個(gè)數(shù)較多,故運(yùn)用基于正交化技術(shù)的SCAD型固定效應(yīng)選擇方法進(jìn)行固定效應(yīng)的選擇和估計(jì),并對隨機(jī)效應(yīng)進(jìn)行檢驗(yàn)。由于數(shù)據(jù)不滿足正態(tài)性假設(shè),故進(jìn)行固定效應(yīng)的選擇和估計(jì)以及隨機(jī)效應(yīng)的檢驗(yàn)時(shí),選擇的是不依賴于正態(tài)性假設(shè)的新方法。納入混合效應(yīng)模型的初始自變量,既有銀行風(fēng)險(xiǎn)類指標(biāo),也有宏觀經(jīng)濟(jì)指標(biāo)。實(shí)證結(jié)果表明,不同類型商業(yè)銀行不良貸款率受不同協(xié)變量的影響,同一協(xié)變量對不同類型商業(yè)銀行不良貸款率的影響或正或負(fù)。具體到宏觀經(jīng)濟(jì)指標(biāo),國內(nèi)生產(chǎn)總值對所有類型商業(yè)銀行不良貸款率的影響都是正向的;消費(fèi)者物價(jià)指數(shù)只影響城市商業(yè)銀行、農(nóng)村商業(yè)銀行和外資銀行,且為負(fù)方向;廣義貨幣供應(yīng)量對商業(yè)銀行總體、股份制商業(yè)銀行和外資銀行的影響是正向的,對城市商業(yè)銀行的影響為負(fù);社會(huì)消費(fèi)品零售總額僅從正向影響商業(yè)銀行總體、國有大型商業(yè)銀行和股份制商業(yè)銀行。在所選的三個(gè)銀行風(fēng)險(xiǎn)類指標(biāo)中,撥備覆蓋率對商業(yè)銀行總體、國有大型商業(yè)銀行、股份制商業(yè)銀行和外資銀行均有負(fù)向影響,而核心資本負(fù)債率和流動(dòng)性比率僅對外資銀行起著正向影響。對于隨機(jī)效應(yīng)來說,年度和季節(jié)效應(yīng)對不同類型商業(yè)銀行不良貸款率均影響顯著,說明經(jīng)濟(jì)周期性在不良貸款率影響因素的研究中不容忽視。進(jìn)一步地,為說明年度和季節(jié)效應(yīng)對每一類型商業(yè)銀行的影響差異,根據(jù)相應(yīng)的隨機(jī)效應(yīng)檢驗(yàn)結(jié)果,計(jì)算出了P值,并給出了比較結(jié)論。
[Abstract]:In recent years, the non-performing loan rate of commercial banks in our country has been rising continuously. As of in the third quarter of 2016, the non-performing loan rate of commercial banks has risen for 19 consecutive quarters. The research on the factors affecting the non-performing loan ratio of commercial banks can strengthen the operating foundation of commercial banks. At the same time, through the different types of commercial banks through the impact of non-performing loan ratio factors. In order to comprehensively consider the influence factors of non-performing loan ratio, this paper chooses the mixed effect model with fixed effect and random effect, because of the sample size. The number of initial explanatory variables is large, so the SCAD fixed effect selection method based on orthogonalization technique is used to select and estimate the fixed effect. The random effect is tested. Because the data do not satisfy the hypothesis of normality, the selection and estimation of the fixed effect and the test of the random effect are carried out. We choose a new method which does not depend on the hypothesis of normality. The initial independent variables of the mixed effect model include both bank risk indicators and macroeconomic indicators. The empirical results show that. The non-performing loan ratio of different types of commercial banks is affected by different covariables. The same covariable has a positive or negative effect on the non-performing loan ratio of different types of commercial banks. The impact of GDP on the non-performing loan ratio of all types of commercial banks is positive; The consumer price index only affects urban commercial banks, rural commercial banks and foreign banks, and is negative; The influence of broad money supply on commercial banks, joint-stock commercial banks and foreign banks is positive and negative on urban commercial banks. Total retail sales of consumer goods only positively affect the overall commercial banks, state-owned large commercial banks and joint-stock commercial banks. In the selected three bank risk indicators, the provision coverage of commercial banks as a whole. Large state-owned commercial banks, joint-stock commercial banks and foreign banks have a negative impact, while the core capital debt ratio and liquidity ratio only have a positive impact on foreign banks. The annual and seasonal effects have a significant impact on the non-performing loan rate of different types of commercial banks, indicating that economic periodicity can not be ignored in the study of the factors affecting the non-performing loan rate. In order to explain the difference between the annual and seasonal effects on each type of commercial bank, the P value is calculated according to the results of the random effect test, and the comparative conclusions are given.
【學(xué)位授予單位】：云南財(cái)經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F832.4

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