【摘要】：流動(dòng)性是商業(yè)銀行的生命線,流動(dòng)性管理對(duì)于商業(yè)銀行的穩(wěn)健運(yùn)營(yíng)至關(guān)重要。商業(yè)銀行在進(jìn)行流動(dòng)性管理時(shí)必然將流動(dòng)性風(fēng)險(xiǎn)作為重點(diǎn)考察對(duì)象。只有將流動(dòng)性風(fēng)險(xiǎn)控制在一定的范圍內(nèi),同時(shí)保障商業(yè)銀行較高的盈利性,商業(yè)銀行的運(yùn)營(yíng)才能得益安全、穩(wěn)定、持久進(jìn)行。商業(yè)銀行的流動(dòng)性風(fēng)險(xiǎn)可以量化成一些具體的指標(biāo),在發(fā)生流動(dòng)性危機(jī)時(shí)其相應(yīng)的指標(biāo)數(shù)據(jù)通常會(huì)有所反映,故而我們可以在流動(dòng)性危機(jī)發(fā)生之前就對(duì)這些指標(biāo)進(jìn)行監(jiān)控,實(shí)現(xiàn)流動(dòng)性危機(jī)的預(yù)警。本文第一章闡述了我國(guó)商業(yè)銀行流動(dòng)性風(fēng)險(xiǎn)預(yù)警研究的背景及意義,同時(shí)梳理了國(guó)內(nèi)外已有的關(guān)于商業(yè)銀行流動(dòng)性風(fēng)險(xiǎn)預(yù)警的文獻(xiàn)。第二章對(duì)商業(yè)銀行流動(dòng)性風(fēng)險(xiǎn)預(yù)警的相關(guān)理論基礎(chǔ)進(jìn)行了闡述。第三章進(jìn)行了商業(yè)銀行流動(dòng)性風(fēng)險(xiǎn)預(yù)警模型的需求分析和可行性分析。第四章在前文理論分析的基礎(chǔ)上進(jìn)行了商業(yè)銀行流動(dòng)性風(fēng)險(xiǎn)預(yù)警模型的設(shè)定。首先,對(duì)商業(yè)銀行流動(dòng)性風(fēng)險(xiǎn)預(yù)警機(jī)制做了統(tǒng)籌設(shè)計(jì),對(duì)整體機(jī)制進(jìn)行了構(gòu)建;然后,在整體機(jī)制的基礎(chǔ)上對(duì)具體模塊進(jìn)行了細(xì)化設(shè)計(jì),其中包括構(gòu)建商業(yè)銀行流動(dòng)性風(fēng)險(xiǎn)評(píng)價(jià)體系,以此確定輸入模塊和輸出模塊,構(gòu)建BP神經(jīng)網(wǎng)絡(luò)預(yù)警模型,確定網(wǎng)絡(luò)結(jié)構(gòu)和激發(fā)函數(shù)。第五章采用樣本銀行的指標(biāo)數(shù)據(jù)對(duì)BP神經(jīng)網(wǎng)絡(luò)預(yù)警模型進(jìn)行了應(yīng)用。首先采用樣本銀行數(shù)據(jù)對(duì)BP神經(jīng)網(wǎng)絡(luò)模型進(jìn)行了訓(xùn)練,訓(xùn)練結(jié)果表明BP神經(jīng)網(wǎng)絡(luò)預(yù)警模型模擬效果較為理想,然后采用樣本銀行數(shù)據(jù)對(duì)BP神經(jīng)網(wǎng)絡(luò)模型的預(yù)警結(jié)果進(jìn)行了檢測(cè),檢測(cè)結(jié)果正確率為100%,說明該預(yù)警模型可以用于商業(yè)銀行流動(dòng)性風(fēng)險(xiǎn)預(yù)警。第六章進(jìn)行結(jié)論總結(jié),進(jìn)一步闡述了本研究的解決的主要問題,并分析了研究中存在的不足,明確了下一步的研究方向。本文建立的BP神經(jīng)網(wǎng)絡(luò)預(yù)警模型在商業(yè)銀行流動(dòng)性風(fēng)險(xiǎn)預(yù)警方面構(gòu)思較為完整,預(yù)測(cè)準(zhǔn)確度較高,對(duì)我國(guó)現(xiàn)有商業(yè)銀行流動(dòng)性風(fēng)險(xiǎn)管理具有重要的現(xiàn)實(shí)意義。
[Abstract]:Liquidity is the lifeline of commercial banks. Liquidity management is very important to the steady operation of commercial banks. Commercial banks must focus on liquidity risk in liquidity management. Only by controlling the liquidity risk within a certain range and ensuring the commercial bank's higher profitability can the operation of the commercial bank benefit from safety stability and lasting. The liquidity risk of commercial banks can be quantified into some specific indicators, and when liquidity crisis occurs, the corresponding index data will usually be reflected, so we can monitor these indicators before the liquidity crisis occurs. Realize the warning of liquidity crisis. The first chapter expounds the background and significance of the research on liquidity risk early warning of commercial banks in China, and combs the literature on liquidity risk early warning of commercial banks at home and abroad. The second chapter expounds the theoretical basis of liquidity risk warning of commercial banks. The third chapter analyzes the demand and feasibility of the liquidity risk warning model of commercial banks. The fourth chapter sets the liquidity risk warning model of commercial banks on the basis of previous theoretical analysis. First of all, the liquidity risk warning mechanism of commercial banks is designed as a whole, and the overall mechanism is constructed. Then, on the basis of the overall mechanism, the detailed design of specific modules is carried out. It includes setting up the liquidity risk evaluation system of commercial banks, determining the input module and output module, constructing the early warning model of BP neural network, determining the network structure and excitation function. In the fifth chapter, the BP neural network early warning model is applied with the index data of the sample bank. First, the BP neural network model is trained with sample bank data. The result shows that the simulation effect of BP neural network early warning model is satisfactory, and then the early warning result of BP neural network model is detected by sample bank data. The accuracy of the detection results is 100, which indicates that the model can be used for the liquidity risk warning of commercial banks. In the sixth chapter, the conclusion is summarized, the main problems of this study are discussed, and the shortcomings of the research are analyzed, and the research direction of the next step is clarified. The BP neural network early-warning model established in this paper has a relatively complete conception and high prediction accuracy in the aspect of commercial bank liquidity risk warning. It has important practical significance for the liquidity risk management of commercial banks in China.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：F832.33;TP183

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