【摘要】：作為國民經(jīng)濟中最具活力和創(chuàng)新力的組成部分,小微企業(yè)在拉動經(jīng)濟增長、保持經(jīng)濟穩(wěn)定并擴大就業(yè)中扮演了重要的角色。企業(yè)發(fā)展離不開金融的支持,然而,與小微企業(yè)重要地位相矛盾的是作為其最重要外部融資來源的商業(yè)銀行信貸卻未能有效地滿足其融資需求。盡管在大型客戶金融服務(wù)市場競爭激烈,利率市場化和金融脫媒等多重壓力下,小微信貸成為了銀行業(yè)未來的“藍�！睒I(yè)務(wù),但現(xiàn)實中,由于小微信貸違約風險大,單筆成本高,造成其實際風險不可控,盈利能力弱,使得這片“藍海業(yè)務(wù)”既難以簡單地與銀行盈利劃上等號,又難以實現(xiàn)監(jiān)管達標。因此,各家銀行在真正投放小微信貸時往往慎之又慎。小微信貸之所以面臨如今的困境,其根本原因在于銀行缺乏相應(yīng)的風險管理能力。銀行的本質(zhì)是經(jīng)營風險的企業(yè),通過對風險進行有效地識別、計量、緩釋、對沖和定價賺取風險調(diào)整后的利潤。在傳統(tǒng)的信貸業(yè)務(wù)中,銀行基于對貸款的違約風險進行管理和估計并確定相應(yīng)的風險溢價,從而賺取存貸利差。然而,小微企業(yè)由于生產(chǎn)經(jīng)營規(guī)模小、財務(wù)報表不健全,缺乏有效的抵質(zhì)押物,導致與銀行之間的信息不對稱嚴重,銀行無法有效識別其真實的違約概率,緩釋違約風險造成的損失并進行合理的定價。結(jié)果造成市場實踐中的兩個極端:或是通過非價格手段抑制金融需求(如信貸配給);或是放任小微信貸風險失控,最終難以實現(xiàn)可持續(xù)發(fā)展。所以,小微信貸難破局的關(guān)鍵在于銀行如何建立適用于小微企業(yè)的違約風險管理方法和技術(shù),實現(xiàn)對小微信貸違約風險的有效識別和計量,進而支撐合理風險定價的實現(xiàn)。其中,對違約概率的準確估計是提升違約風險管控能力的核心。盡管許多學者對中小微信貸違約風險的成因,管控策略,及道德風險(Moral Hazard),逆向選擇(Adverse Selection)和信貸配給(Credit Rationing)行為等相關(guān)議題進行了廣泛研究。但目前還少有研究專注于對小微企業(yè)違約風險的特征及相應(yīng)的違約管控機理進行系統(tǒng)分析與總結(jié),更缺乏基于這類客戶風險管控機理的違約概率建模研究。因此,本文基于相關(guān)理論、文獻的梳理與借鑒及對小微信貸運營與管理實踐經(jīng)驗的提煉、總結(jié),研究了適用于小微企業(yè)的違約風險管理機制,對小微企業(yè)的違約管控機理進行了深入刻畫與量化,分別構(gòu)建了基于理想數(shù)據(jù)條件下,適用于小微企業(yè)的不完全信息違約估計模型、統(tǒng)計學與機器學習類違約估計模型和基于現(xiàn)實數(shù)據(jù)缺失條件下的違約估計模型。本文的研究成果將有助于加深對小微企業(yè)違約風險特征、管控機理的了解與認識;改變傳統(tǒng)的依賴于資產(chǎn)和抵質(zhì)押物這類不適合于小微企業(yè)的風險管控模式,提升對這類客戶違約概率估計和預測的準確性、有效性和可行性;幫助建立并完善適用于小微企業(yè)的內(nèi)部評級體系,并在客戶準入,授信審批,風險監(jiān)測與經(jīng)濟資本配置等方面發(fā)揮重要作用。全文共分為七章,包含三部分主要內(nèi)容:第一部分,在文獻綜述、相關(guān)理論回顧和成功實踐梳理的基礎(chǔ)上對小微企業(yè)的違約風險特征和違約管控機制進行研究、提煉和總結(jié)。首先分析了小微企業(yè)的四大風險特征:缺乏有效的抵質(zhì)押物;信息不透明程度較為嚴重;單筆規(guī)模小、資產(chǎn)池規(guī)模大以及對外部環(huán)境變化更為敏感。然后結(jié)合業(yè)界實踐和前人研究成果提出了適用于小微企業(yè)的三大違約管控機理:基于現(xiàn)金流的違約觸發(fā)機制;基于關(guān)系信貸減少信息不對稱和分池管控,定量與定性相結(jié)合。并據(jù)此提煉了適用于這類客戶的違約風險模型所需具備的特征。本部分的研究成果,為后文模型的建立提供了理論基礎(chǔ)和建模思路。第二,基于小微企業(yè)違約風險管控機理,分別構(gòu)建了理想數(shù)據(jù)條件下,適用于小微企業(yè)的不完全信息類模型與統(tǒng)計學和機器學習類模型。應(yīng)用不完全信息模型的框架,通過對基于現(xiàn)金流的違約觸發(fā)機制中包含的核心要素:違約邊界和真實現(xiàn)金流分布進行提煉、抽象與刻畫,構(gòu)建了適用于對信息不對稱程度不斷變化條件下小微企業(yè)違約概率進行有效估計的理論模型,并通過逐步放松:銀行可以完全觀測到客戶的初始信息和客戶新發(fā)生的借貸金額對違約概率估計沒有影響這兩個假設(shè),構(gòu)建了具有實際應(yīng)用價值,可以有效刻畫小微企業(yè)違約風險的理論模型�；谡鎸嵉目蛻暨`約數(shù)據(jù),構(gòu)建了適用于小微企業(yè)的統(tǒng)計學和機器學習類模型。首先,建立了系統(tǒng)的小微企業(yè)違約風險評價指標體系。其次,利用真實數(shù)據(jù),對適用于小微企業(yè)的違約風險評價指標進行擬合檢驗,獲得了以客戶現(xiàn)金流類指標和關(guān)系信貸類指標為核心的最具有預測效力的違約預測指標,驗證了前文的理論分析結(jié)論。第三,通過對不同模型的預測效力進行實證分析,發(fā)現(xiàn)整合Logistic回歸模型和支持向量機方法的混合違約概率預測模型是最適用于本文小微企業(yè)數(shù)據(jù)樣本的統(tǒng)計學和機器學習類模型,這一模型不僅具有最高的預測精度而且綜合誤差成本最低,預測穩(wěn)定性最好。第三,針對現(xiàn)實中存在的小微企業(yè)信貸違約數(shù)據(jù)缺失,模型估計有效性難以保證的問題,通過運用貝葉斯估計,整合專家先驗信息和數(shù)據(jù)信息,獲得更為有效的后驗估計結(jié)果。結(jié)論表明,后驗估計結(jié)果既可以彌補由于歷史數(shù)據(jù)信息不足帶來的傳統(tǒng)估計結(jié)果不可信問題,又可以平滑極端歷史數(shù)據(jù)對真實違約概率估計的沖擊,從而有效提升違約概率估計的準確性和有效性,魯棒性檢驗的結(jié)果也證明了上述結(jié)論。在此基礎(chǔ)上,進一步加入基于現(xiàn)金流觸發(fā)機制的單因素違約相關(guān)性模型,t-copula違約相關(guān)性模型和多期條件以提升估計有效性,并設(shè)計了基于Proper Scoring Rules的評分規(guī)則,以對專家經(jīng)驗的有效性進行評價,對專家的權(quán)重進行動態(tài)設(shè)計。
[Abstract]:As the most dynamic and innovative component of the national economy, small and micro enterprises have played an important role in stimulating economic growth, maintaining economic stability and expanding employment. The development of enterprises can not be separated from financial support. However, it is in contradiction with the important position of small and micro enterprises to be the commercial bank credit for its most important source of external financing. However, small credit has become the "blue sea" business of the banking industry in the future, despite the fierce competition in the large customer's financial services market, interest rate marketization and financial disintermediation. But in reality, because of the large risk of default and high cost, the actual risk is uncontrollable and the profit is uncontrollable. Because of its weak ability, this "blue sea business" is difficult to be equated with the bank's profit simply, but it is difficult to realize the regulation. Therefore, each bank is often cautious and cautious when it is really put in small and micro credit. The reason why the small micro credit is facing today's dilemma is that the bank lacks the corresponding risk management ability. Quality is an enterprise that manages risk by effectively identifying, measuring, sustained-release, hedging, and pricing a risk adjusted profit. In the traditional credit business, banks manage and estimate the risk of default on loans and determine the corresponding risk premium, thus earning the loan spreads. However, small micro enterprises are produced due to production. Small business scale, incomplete financial statements and lack of effective impawns cause serious information asymmetry between banks and banks. Banks can not effectively identify their true default probability, slow down the losses caused by default risks and make reasonable pricing. The results are two extremes in market practice: or by non price means. Therefore, the key to the hard break of small micro credit is how to establish a method and technology for the management of default risk for small micro enterprises, to realize the effective identification and measurement of the risk of small and micro credit, and to support the reasonable risk. The accurate estimation of the probability of default is the core of improving the ability to control the risk of default, although many scholars have carried out a wide range of related issues such as the causes of the default risk, the control strategy, the Moral Hazard, the reverse selection (Adverse Selection) and the Credit Rationing behavior. But at present, few studies have focused on the systematic analysis and summary of the characteristics of small and micro enterprises' default risk and the corresponding mechanism of default management, and lack the research on default probability modeling based on this kind of customer risk management mechanism. Therefore, this paper is based on the relevant theories, and the literature is combed and used for reference and the operation and management of small and micro credit. The mechanism of default risk management for small and micro enterprises is studied and the mechanism of default risk management is studied. The mechanism of default management for small and micro enterprises is deeply depicted and quantified. The incomplete information default estimation model for small micro enterprises based on ideal data is constructed, and the model of statistics and machine learning default estimation model are established. The research results of this paper will help to deepen the understanding and understanding of the characteristics of default risk and control mechanism of small and micro enterprises, and change the traditional risk management model which is not suitable for the small and medium-sized enterprises, such as assets and collateral, and improve the estimation of default probability of these customers. And the accuracy, effectiveness and feasibility of the prediction, help to establish and improve the internal rating system for small micro enterprises, and play an important role in customer access, credit examination and approval, risk monitoring and economic capital allocation. The full text is divided into seven chapters, including three parts of the main content: the first part, in the literature review, the related theory review On the basis of the successful practice, this paper studies the characteristics of the risk of breach of contract and the mechanism of control and control of the small and micro enterprises. Firstly, it analyzes the four characteristics of the risk of small and micro enterprises: the lack of effective collateral; the information opaque degree is more serious; the single scale is small, the scale of the asset pool is large and the external environment changes more. Then, combining with the industry practice and the previous research results, we put forward the three major mechanism of default management for small micro enterprises: the trigger mechanism of default based on cash flow; based on the relationship credit reduction information asymmetry and the pool management, the quantitative and qualitative combination. The research results of this part provide theoretical basis and modeling ideas for the establishment of the later model. Second, based on the mechanism of small and micro enterprises' default risk management and control, the incomplete information model and statistics and machine learning model for small micro enterprises are constructed under the ideal data conditions. By refining, abstracting and depicting the core elements of default triggering mechanism based on cash flow: default boundary and real cash flow distribution, the framework builds a theoretical model for effective estimation of default probability for small and micro enterprises under the condition of continuous change of information asymmetry. The total observation of the customer's initial information and the customer's new loan amount has no effect on the probability of default, and constructs a theoretical model that has practical application value and can effectively depict the default risk of small and micro enterprises. Based on the real customer default data, the statistics and machinery for small micro enterprises are constructed. First, the system is established to evaluate the default risk evaluation index system of small and micro enterprises. Secondly, using real data, the evaluation index of default risk applicable to small micro enterprises is fitted and tested, and the most predictive effective default prediction index, which is the core of customer cash flow index and relation credit index, is obtained. Third. Third, through the empirical analysis of the predictive effectiveness of different models, it is found that the mixed default probability prediction model of the integrated Logistic regression model and the support vector machine method is the most suitable statistical and machine learning model which is most suitable for the data samples of small and micro enterprises. This model is not only the highest. The prediction accuracy and the comprehensive error cost are the lowest and the prediction stability is the best. Third, in view of the lack of credit default data in the small micro enterprises and the problem that the validity of the model estimation is difficult to guarantee, the expert prior information and data information are integrated by using Bayesian estimation, and the results of the more effective posterior estimation are obtained. The conclusion shows that The results of the posterior estimation can not only compensate for the unbelievable problem of the traditional estimation results due to the insufficient historical data information, but also smooth the impact of the extreme historical data on the real default probability estimation, thus effectively improving the accuracy and effectiveness of the default probability estimation. The results of the robust test also prove the above conclusion. On the other hand, we further add the single factor default correlation model based on the cash flow trigger mechanism, the t-copula default correlation model and the multi term conditions to improve the effectiveness of the estimation, and design the scoring rules based on the Proper Scoring Rules to evaluate the effectiveness of the expert experience, and to dynamically design the weight of the experts.
【學位授予單位】：南京大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：F832.4

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