The geography of Spanish bank branches

This article analyzes the determinants of bank branch location in Spain taking the role of geography explicitly into account. After a long period of intense territorial expansion, especially by savings banks, many of these firms are now involved in merger processes triggered off by the financial crisis, most of which entail the closing of many branches. However, given the contributions of this type of banks to limit financial exclusion, this process might exacerbate the consequences of the crisis for some disadvantaged social groups. Related problems such as new banking regulation initiatives (Basel III), or the current excess capacity in the sector add further relevance to this problem. We address this issue from a Bayesian perspective, using a Poisson regression model within the framework of generalized linear mixed models. This proposal allows us to assess whether over-branching or under-branching has taken place. Our results suggest, among other findings, that both phenomena are present in the Spanish banking sector, although the implications for the three types of banks in the industry, namely commercial banks, savings banks or credit unions, vary a great deal.     

金融结构影响收入不平等的门限效应研究

一国金融结构决定了实体经济面临的金融服务类型与质量,并通过信贷配置功能影响收入分配。基于1996-2014年省级面板数据,以金融发展规模及其结构特征作为门限变量,实证检验了金融结构与收入不平等的非线性关系。研究结果表明,金融结构对收入不平等的影响在整体和农村层面存在显著的门限效应,但在城镇层面未得到验证。具体而言,随着金融发展规模的扩大,提升直接融资比例对整体居民收入不平等的缩小效应凸显,但在农村层面的积极作用趋于弱化;随着直接融资比例的扩大,金融结构对整体居民收入不平等的缩小效应减弱,但在农村层面得到强化。兼顾金融发展的规模与结构,逐步放开金融市场管制,以提升直接融资比例,有利于降低收入不平等。     

基于贝叶斯方法的信用评级模型构建与违约概率估计

以贝叶斯方法为基础构建了信用评级和违约概率模型,指出金融机构利用已有评级信息提高债务人信用风险评估准确性的途径,并以单个债务人违约概率度量方法和Merton理论为基础,考虑异质性导致的宏观经济冲击对债务人的不同影响,度量资产组合违约风险。利用相关数据对贝叶斯模型应用给出例证,结果表明贝叶斯方法具有更为灵活的框架和较好的预测能力。     

Bivariate Semi-Markov Process for Counterparty Credit Risk

We consider the problem of constructing an appropriate multivariate model to study counterparty credit risk in the credit rating migration problem. For this financial problem different multivariate Markov chain models were proposed. However, the Markovian assumption may be inappropriate for the study of the dynamics of credit ratings, which typically show non Markovian-like behavior. In this article, we develop a semi-Markov approach to study the counterparty credit risk by defining a new multivariate semi-Markov chain model. Methods are given for computing the transition probabilities, reliability functions and the price of a risky Credit Default Swap.     

Quantification of model risk that is caused by model misspecification

In this paper, we suggest a technique to quantify model risk, particularly model misspecification for binary response regression problems found in financial risk management, such as in credit risk modelling. We choose the probability of default model as one instance of many other credit risk models that may be misspecified in a financial institution. By way of illustrating the model misspecification for probability of default, we carry out quantification of two specific statistical predictive response techniques, namely the binary logistic regression and complementary log–log. The maximum likelihood estimation technique is employed for parameter estimation. The statistical inference, precisely the goodness of fit and model performance measurements, are assessed. Using the simulation dataset and Taiwan credit card default dataset, our finding reveals that with the same sample size and very small simulation iterations, the two techniques produce similar goodness-of-fit results but completely different performance measures. However, when the iterations increase, the binary logistic regression technique for balanced dataset reveals prominent goodness of fit and performance measures as opposed to the complementary log–log technique for both simulated and real datasets.     

企业财务风险预测模型实证比较分析

企业财务风险一直是风险管理理论和实务界关心的热点话题。运用判别分析和计量经济方法对重庆市某商业银行的461个样本企业2002-2005年的违约特征进行实证检验和预测。结果发现最重要的决定变量是资产负责率、酸性试验比率、资产净利率等7个财务比率以及企业所处的产业部门,考虑了异方差性的probit模型有更好的预测能力。     

Nonparametric Panel Estimation of Labor Supply

In this article, we estimate structural labor supply with piecewise-linear budgets and nonseparable endogenous unobserved heterogeneity. We propose a two-stage method to address the endogeneity issue that comes from the correlation between the covariates and unobserved heterogeneity. In the first stage, Evdokimov’s nonparametric de-convolution method serves to identify the conditional distribution of unobserved heterogeneity from the quasi-reduced model that uses panel data. In the second stage, the conditional distribution is plugged into the original structural model to estimate labor supply. We apply this methodology to estimate the labor supply of U.S. married men in 2004 and 2005. Our empirical work demonstrates that ignoring the correlation between the covariates and unobserved heterogeneity will bias the estimates of wage elasticities upward. The labor elasticity estimated from a fixed effects model is less than half of that obtained from a random effects model.     

全要素生产率会影响成本函数估计吗

不同于传统的成本函数实证研究,本文关注的是企业间异质的全要素生产率对成本函数估计的影响。为此,本文首次从CES生产函数推导出一个包含全要素生产率的完整的成本函数模型,并以中国大型重化工企业的2003-2007年样本进行回归,同时使用传统模型与SFA模型回归,以进行方法论上的比较分析。结果表明,全要素生产率确实对成本函数估计产生了重要影响,改良前的传统模型可能出现“遗漏变量”问题,使似不相关回归产生内生性问题。三种方法的回归结果表明：非国有企业的规模经济效应是显著的,但国有企业的规模经济检验结果却不稳健——不同方法的测度结果不一致。因此,政府在制定规制、扶持与补贴等政策前,规模经济的测度应该尽量考虑到企业异质性,并使用多种检验方法,以保证政策干预的有效性。     

Do Central Bank Liquidity Facilities Affect Interbank Lending Rates?

In response to the global financial crisis that started in August 2007, central banks provided extraordinary amounts of liquidity to the financial system. To investigate the effect of central bank liquidity facilities on term interbank lending rates near the start of the crisis, we estimate a six-factor arbitrage-free model of U.S. Treasury yields, financial corporate bond yields, and term interbank rates. This model can account for fluctuations in the term structure of credit and liquidity spreads observed in the data. A significant shift in model estimates after the announcement of the liquidity facilities suggests that these central bank actions did help lower the liquidity premium in term interbank rates.     

Heterogeneous skewness in binary choice models: predicting outcomes in the men's NCAA basketball tournament

Several authors have recently explored the estimation of binary choice models based on asymmetric error structures. One such family of skewed models is based on the exponential generalized beta type 2 (EGB2). One model in this family is the skewed logit. Recently, McDonald (1996, 2000) extended the work on the EGB2 family of skewed models to permit heterogeneity in the scale parameter. The aim of this paper is to extend the skewed logit model to allow for heterogeneity in the skewness parameter. By this we mean that, in the model developed, here the skewness parameter is permitted to vary from observation to observation by making it a function of exogenous variables. To demonstrate the usefulness of our model, we examine the issue of the predictive ability of sports seedings. We find that we are able to obtain better probability predictions using the skewed logit model with heterogeneous skewness than can be obtained with logit, probit, or skewed logit.     

Bayesian estimation of a flexible bifactor generalized partial credit model to survey data

Item response theory (IRT) models provide an important contribution in the analysis of polytomous items, such as Likert scale items in survey data. We propose a bifactor generalized partial credit model (bifac-GPC model) with flexible link functions - probit, logit and complementary log-log - for use in analysis of ordered polytomous item scale data. In order to estimate the parameters of the proposed model, we use a Bayesian approach through the NUTS algorithm and show the advantages of implementing IRT models through the Stan language. We present an application to marketing scale data. Specifically, we apply the model to a dataset of non-users of a mobile banking service in order to highlight the advantages of this model. The results show important managerial implications resulting from consumer perceptions. We provide a discussion of the methodology for this type of data and extensions. Codes are available for practitioners and researchers to replicate the application.     

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Predictive modelling provides a way to increase profitability and customer satisfaction in the financial services sector. Following on from Career Story , Omar Mohamed describes the successful use of this technique in recent marketing activities for the M&S "&More" credit card.     

Establishing decision tree-based short-term default credit risk assessment models

ABSTRACT

Traditional credit risk assessment models do not consider the time factor; they only think of whether a customer will default, but not the when to default. The result cannot provide a manager to make the profit-maximum decision. Actually, even if a customer defaults, the financial institution still can gain profit in some conditions. Nowadays, most research applied the Cox proportional hazards model into their credit scoring models, predicting the time when a customer is most likely to default, to solve the credit risk assessment problem. However, in order to fully utilize the fully dynamic capability of the Cox proportional hazards model, time-varying macroeconomic variables are required which involve more advanced data collection. Since short-term default cases are the ones that bring a great loss for a financial institution, instead of predicting when a loan will default, a loan manager is more interested in identifying those applications which may default within a short period of time when approving loan applications. This paper proposes a decision tree-based short-term default credit risk assessment model to assess the credit risk. The goal is to use the decision tree to filter the short-term default to produce a highly accurate model that could distinguish default lending. This paper integrates bootstrap aggregating (Bagging) with a synthetic minority over-sampling technique (SMOTE) into the credit risk model to improve the decision tree stability and its performance on unbalanced data. Finally, a real case of small and medium enterprise loan data that has been drawn from a local financial institution located in Taiwan is presented to further illustrate the proposed approach. After comparing the result that was obtained from the proposed approach with the logistic regression and Cox proportional hazards models, it was found that the classifying recall rate and precision rate of the proposed model was obviously superior to the logistic regression and Cox proportional hazards models.     

Model Assessment for Predictive Classification Models

In this article, we present a novel methodology to assess predictive models for a binary target. In our opinion, the main weakness of the criteria proposed in the literature is not to take the financial costs of a wrong decision into account.

The objective of this article is to derive the optimal cut-off in predictive classification models and to improve model assessment on the basis of a general class of loss functions. We describe how our proposal performs in a real application on credit scoring.     

商业银行外部评级和资本市场评价比较研究

银行风险是当前中国面临的最主要的金融风险。为了准确评价银行的风险,首先介绍了两种最主要的方法——外部评级法和资本市场评价模型,再用默顿和KMV的相关理论计算了资本市场评价模型中最主要的参数d2,并用聚类的方法将d2分类后和外部评级法的结果进行比较,结果表明,市场评价模型的客观性和公正性较好,外部评级法的稳定性较好,两种方法各有优劣,可以互相借鉴。     

Parameter estimation and inference with spatial lags and cointegration

This article studies dynamic panel data models in which the long run outcome for a particular cross-section is affected by a weighted average of the outcomes in the other cross-sections. We show that imposing such a structure implies a model with several cointegrating relationships that, unlike in the standard case, are nonlinear in the coe?cients to be estimated. Assuming that the weights are exogenously given, we extend the dynamic ordinary least squares methodology and provide a dynamic two-stage least squares estimator. We derive the large sample properties of our proposed estimator under a set of low-level assumptions. Then our methodology is applied to US financial market data, which consist of credit default swap spreads, as well as firm-specific and industry data. We construct the economic space using a “closeness” measure for firms based on input–output matrices. Our estimates show that this particular form of spatial correlation of credit default swap spreads is substantial and highly significant.     

利率扭曲、市场分割与深化利率市场化改革

虽然存贷款利率管制已经取消,但在改革过渡期内,目前我国信贷市场中仍然存在一定程度的利率扭曲与市场分割：同时存在商业银行和类银行机构,商业银行利率较低,主要为传统企业等低风险企业贷款,而非传统企业融资更多依赖于利率较高的类银行机构。本文从利率扭曲与市场分割问题入手,构建包含异质性金融部门和企业部门的动态随机一般均衡（DSGE）模型,研究深化利率市场化改革对经济金融的具体效应。结果显示,改革将促使利率回归均衡,消除监管套利,维护金融稳定;同时,优化金融资源配置,降低企业部门杠杆率,推动经济高质量发展。但改革也将对不同类型的金融、企业部门形成差异化影响,在助推产业结构优化升级的同时,驱动商业银行和传统企业加快经营转型。此外,改革后经济体在面对外部冲击时更加稳健。进一步,对比不同改革进程下的政策效果发现,若进程过快,可能增加短期经济金融波动风险。因此,改革应遵循循序渐进的推进原则。     

金融中介发展与经济增长:中国案例研究与国际比较

Using the cointegration analysis technology and causality analysis technology, we discuss the relationship between financial interrediaries development and economic growth in China, Evidences from the multivariate VAR system show that (ⅰ)as far as the relationship between financial intermediaries scale and economic growth is concerned, what Robinson (1952) declared is right, "where enterprise lead finance follows"; (ⅱ) on the relationship between financial intermediaries efficiency and economic growth, the view of many endogenous economic growth models can be supported by China case.     

信息挖掘还是噪声交易:债券特质风险如何影响信用利差?

特质风险向债券市场传递风险的方式,直接关系到债券定价逻辑和系统性金融风险防范。本文选取交易所公司债数据,从投资者信息挖掘行为和非理性交易行为出发,研究债券特质风险对信用利差的传导效应与传导机制,并从违约视角探索特质风险产生传导效应的原因,同时分析投资者对不同类型债券所做反应的异质性。研究结论表明:特质风险会通过信息挖掘机制和噪声交易机制影响 信用利差,且以噪声交易机制为主;违约事件引致了更多噪声交易,是特质风险产生传导效应的重要环境因素;发行人的股票上市或国企背景降低了投资者面临的信息不对称程度,并有效抑制了噪声交易机 制的作用,而债券的低评级或短期限特征则会引发投资者的抛售行为,进而放大了噪声交易机制的作用。     

Fused least absolute shrinkage and selection operator for credit scoring

Credit scoring can be defined as the set of statistical models and techniques that help financial institutions in their credit decision makings. In this paper, we consider a coarse classification method based on fused least absolute shrinkage and selection operator (LASSO) penalization. By adopting fused LASSO, one can deal continuous as well as discrete variables in a unified framework. For computational efficiency, we develop a penalization path algorithm. Through numerical examples, we compare the performances of fused LASSO and LASSO with dummy variable coding.