本文選題：系統(tǒng)性金融風(fēng)險(xiǎn) + 風(fēng)險(xiǎn)預(yù)警系統(tǒng)　； 參考：《東北財(cái)經(jīng)大學(xué)》2016年碩士論文

【摘要】：自上個(gè)世紀(jì)以來(lái),全球發(fā)生了多次金融危機(jī),對(duì)各國(guó)經(jīng)濟(jì)造成了難以挽回的損失。隨著全球化的推進(jìn),金融危機(jī)的破壞力、傳染性顯著增強(qiáng),任何國(guó)家都無(wú)法再別國(guó)的金融風(fēng)險(xiǎn)中獨(dú)善其身,因此如何有效的監(jiān)控國(guó)內(nèi)外金融風(fēng)險(xiǎn)并準(zhǔn)確的發(fā)出預(yù)警成為各國(guó)學(xué)者研究的熱點(diǎn)。在2001年加入WTO后,中國(guó)在世界經(jīng)濟(jì)中扮演了更為重要的角色,對(duì)外部經(jīng)濟(jì)波動(dòng)的敏感性也逐漸增加。同時(shí)中國(guó)的債務(wù)、房地產(chǎn)等領(lǐng)域均存在著較多不穩(wěn)定因素,隨著經(jīng)濟(jì)增長(zhǎng)的減速,這些問(wèn)題可能進(jìn)一步爆發(fā)出來(lái),給我國(guó)金融體系帶來(lái)風(fēng)險(xiǎn)。因此,建立一個(gè)符合我國(guó)國(guó)情的金融風(fēng)險(xiǎn)預(yù)警系統(tǒng)具有十分重要、深遠(yuǎn)的現(xiàn)實(shí)意義。首先,本文基于國(guó)內(nèi)外金融壓力指數(shù)相關(guān)文獻(xiàn),綜合考慮后續(xù)構(gòu)建模型的需要,選取產(chǎn)出缺口作為金融風(fēng)險(xiǎn)的代理變量以確定金融風(fēng)險(xiǎn)的爆發(fā)時(shí)間點(diǎn)與影響長(zhǎng)度,并以相關(guān)標(biāo)志性金融事件作為金融風(fēng)險(xiǎn)時(shí)間點(diǎn)確定的輔助證據(jù)。其次,本文基于已有文獻(xiàn)分析不同預(yù)警模型的優(yōu)缺點(diǎn)與適應(yīng)性,結(jié)合中國(guó)國(guó)情與數(shù)據(jù)特征選擇Logit模型作為預(yù)警模型。同時(shí),本文依據(jù)國(guó)內(nèi)外預(yù)警模型構(gòu)建經(jīng)驗(yàn),選擇信貸膨脹缺口、房?jī)r(jià)指數(shù)等作為解釋變量。在確定模型與指標(biāo)后,以2000年到2015年的季度數(shù)據(jù)構(gòu)建二元Logit金融風(fēng)險(xiǎn)預(yù)警模型。同時(shí),為了研究政策制定與預(yù)警模型之間的關(guān)系,本文基于前文預(yù)警模型研究了不同政策偏好下預(yù)警模型的表現(xiàn)。再次,本文進(jìn)一步探索了多元Logit模型在金融風(fēng)險(xiǎn)預(yù)警中多作用,以單因素、多因素兩種形式構(gòu)建了不同的多元Logit模型。同時(shí)還比較了二元與多元Logit預(yù)警模型各自的優(yōu)勢(shì)與特點(diǎn)。最后本文總結(jié)了研究結(jié)論,有以下四點(diǎn):使用季度數(shù)據(jù)的多因素Logit預(yù)警模型適合作為中國(guó)金融風(fēng)險(xiǎn)的預(yù)警模型,該模型聯(lián)動(dòng)引入多個(gè)層次的指標(biāo),能適應(yīng)中國(guó)不同時(shí)期不同金融風(fēng)險(xiǎn)的異質(zhì)性對(duì)風(fēng)險(xiǎn)作出更好的預(yù)判。實(shí)證的結(jié)果顯示,多因素Logit預(yù)警模型的預(yù)警正確率以及效率基本滿足了目前國(guó)外早期預(yù)警模型的正確率與效率經(jīng)驗(yàn)要求。本文根據(jù)理論研究選取的信貸、銀行脆弱性、宏觀背景的相關(guān)指標(biāo)作為單獨(dú)按預(yù)警指標(biāo)均有效,但單獨(dú)作為預(yù)警指標(biāo)正確率與效率較差。房?jī)r(jià)作為預(yù)警指標(biāo)本身幾乎沒(méi)有任何預(yù)警能力,但與信貸膨脹缺口指標(biāo)聯(lián)合使用能顯著提高模型預(yù)警能力,顯示信貸膨脹與房地產(chǎn)泡沫間的互相促進(jìn)作用會(huì)帶來(lái)較大的金融風(fēng)險(xiǎn)。實(shí)證顯示多元Logit預(yù)警模型能夠有效識(shí)別風(fēng)險(xiǎn)后的動(dòng)蕩時(shí)期,避免與一般時(shí)期混淆,能夠很好的運(yùn)用于中國(guó)的金融風(fēng)險(xiǎn)預(yù)警之中。但目前的統(tǒng)計(jì)數(shù)據(jù)無(wú)法滿足該模型對(duì)數(shù)據(jù)量的要求,只能建立簡(jiǎn)單,不太穩(wěn)定的預(yù)警模型。不同政策傾向的分情景討論顯示,任何模型的效用在風(fēng)險(xiǎn)權(quán)重比例與正常時(shí)期占全時(shí)期的比例相等時(shí)最高;同時(shí)TPR越高,模型在高風(fēng)險(xiǎn)權(quán)重中的表現(xiàn)越好,FPR越低,模型在低風(fēng)險(xiǎn)權(quán)重中的表現(xiàn)越好。
[Abstract]:Since the last century, the global financial crisis has caused irreparable losses to the economies of various countries. With the advancement of globalization and the destructive power of the financial crisis, the contagion has increased significantly. No country can be left alone in the financial risks of other countries. Therefore, how to effectively monitor financial risks at home and abroad and accurately issue early warning has become a hot spot of scholars all over the world. After China's entry into WTO in 2001, China has played a more important role in the world economy, and its sensitivity to external economic fluctuations has gradually increased. At the same time, there are many unstable factors in China's debt, real estate and other fields. With the deceleration of economic growth, these problems may erupt further and bring risks to our financial system. Therefore, it is of great importance and profound significance to establish a financial risk early warning system in accordance with the national conditions of our country. First of all, based on the domestic and foreign financial pressure index related literature, considering the need of building the model, the output gap is selected as the proxy variable of financial risk to determine the time point and impact length of financial risk. And related landmark financial events as the financial risk in time to determine the supporting evidence. Secondly, this paper analyzes the advantages and disadvantages and adaptability of different early warning models based on the existing literature, and selects Logit model as early warning model in combination with China's national conditions and data characteristics. At the same time, based on the experience of early warning model at home and abroad, this paper chooses credit inflation gap and house price index as explanatory variables. After determining the model and indicators, a binary Logit financial risk early warning model is constructed based on the quarterly data from 2000 to 2015. At the same time, in order to study the relationship between policy making and early warning model, this paper studies the performance of early warning model under different policy preferences based on the previous early warning model. Thirdly, this paper further explores the multi-role of multivariate Logit model in financial risk early warning, and constructs different multivariate Logit model in the form of single factor and multi-factor. At the same time, the advantages and characteristics of binary and multivariate Logit early warning models are compared. Finally, this paper summarizes the research conclusions, there are four points: the multi-factor Logit early-warning model based on quarterly data is suitable for China's financial risk early warning model, and the linkage model introduces multiple levels of indicators. It can adapt to the heterogeneity of different financial risks in different periods in China. The empirical results show that the accuracy and efficiency of the multi-factor Logit early warning model can basically meet the requirements of the foreign early warning model's accuracy and efficiency. According to the theoretical study of credit, banking vulnerability, macro background of the relevant indicators as a separate warning indicators are effective, but as early warning indicators of accuracy and efficiency is poor. As an early warning indicator, house prices have almost no early warning capability in themselves, but the combination of the credit inflation gap index can significantly improve the early warning ability of the model. It shows that the mutual promotion between credit inflation and real estate bubble will bring greater financial risk. The empirical results show that the multivariate Logit early warning model can effectively identify the turbulent period after the risk, avoid confusion with the general period, and can be used in China's financial risk early warning. However, the current statistical data can not meet the requirements of the model for the amount of data, can only build a simple, unstable early warning model. According to the situation discussion of different policy tendencies, the utility of any model is the highest when the proportion of risk weight is equal to the proportion of the whole period in normal period, and the higher the TPR, the better the performance of the model in the high risk weight. The model performs better in low risk weight.
【學(xué)位授予單位】：東北財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：F832.1;F224

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本文編號(hào)：1980023