On Loss Distributions from Installment-Repaid Loans

The banks have been accumulating huge data bases for many years and are increasingly turning to statistics to provide insight into customer behaviour, among other things. Credit risk is an important issue and certain stochastic models have been developed in recent years to describe and predict loan default. Two of the major models currently used in the industry are considered here, and various ways of extending their application to the case where a loan is repaid in installments are explored. The aspect of interest is the probability distribution of the total loss due to repayment default at some time. Thus, the loss distribution is determined by the distribution of times to default, here regarded as a discrete-time survival distribution. In particular, the probabilities of large losses are to be assessed for insurance purposes.     

资信评估中违约率的度量及应用

首先讨论了违约率在资信评估中的意义,接着提出了违约率的度量方法以及考虑其他因素时的违约率调整,最后给出了违约率在资信评估中的应用。     

债券流动性与违约风险相关性溢价及实证研究

债券的流动性与违约风险都是影响债券溢价的重要因素,然而以往在违约风险最为突出的公司债券定价中却很少考虑两种风险相关性关系的影响,这与发达市场实证经验不符.在已有研究的基础上同时引入两种风险相关性,通过对Copula函数刻画的不同相关性结构情况下公司债券的收益率和风险变化分析,以及对中国短、中、长期公司债券市场数据的实证检验均发现,流动性与违约风险的相关性之间存在显著的正相关性,且对债券利差具有显著的影响和交互作用.     

商业银行集团客户统一授信额度的优化配置研究

企业集团是商业银行重要的贷款客户,商业银行面临企业集团的授信业务风险尤为突出。在结构化模型的框架下,考虑统一授信额度的约束,基于对违约风险控制和贷款收益管理的多目标决策,构建了企业集团成员企业授信额度优化配置模型。示例分析表明,在考虑不同目标重要性的前提下,使用如遗传算法等最优化求解方法,可得到对成员企业授信额度的优化配置方案,从而有助于商业银行积极主动的防范集团客户的信贷风险。     

违约风险、银行信贷筛选与货币政策非对称效应

本文构建一个包含企业违约风险与银行信贷筛选的动态随机一般均衡模型,研究银行信贷筛选对货币政策宏观经济效应产生的影响。在参数校准的基础上,分析了货币政策冲击的长期效应与短期效应。研究结果显示：(1)无论是基于长期还是短期效应的角度,银行信贷筛选均显著抑制了扩张性货币政策对宏观经济产生的积极影响;(2)基于货币政策冲击的短期效应可以发现,银行信贷筛选降低了扩张性货币政策的实际效应,并且放大了货币政策对通胀的影响;(3)通过比较扩张性与紧缩性货币政策的脉冲响应可以发现,银行信贷筛选导致货币政策的宏观经济效应存在非对称性特征。福利分析的结果表明,相比无银行信贷筛选的情形而言,银行信贷筛选导致了整体社会福利的恶化。     

手机App列表信息在信用风险评价中的应用——基于互联网借贷平台的实证研究

随着互联网的发展和智能手机的普及,用户手机数据被用来评估借款人的信用风险,使用到的数据有通讯记录、短信息接发、移动轨迹、用户行为数据等,而本文研究了手机上所安装的App列表和借款人信用风险之间的关系。通过对某大型互联网借贷平台上的个人借贷数据以及借款人手机上安装的App列表数据的分析发现,手机上安装的App和借款人的信用状况存在关联关系。安装生活类、金融类和买房买车类App的借款人比没有安装这些App的借款人信用风险低;其中,记账类App、外卖类App、股票类App和买房类App对借款人的信用风险有较强的识别能力。把手机App列表信息加入信用风险评价模型之后,信用风险评价模型的区分能力得到显著提高。     

美国次贷危机的形成机理及其启示

从探究美国次贷危机的源头入手分析其形成原因,理清次贷危机被过度放大的环节所在,进而认为近期金融市场的动荡和流动性不足主要是市场的恐慌情绪和投资者避险行为的结果,最后指出次贷危机对我国房贷市场的启示。     

刑事自诉中止审理研究

2006年笔者选择了四川五个区县进行刑事自诉案件的调查，调查采取查阅档案和访谈等方式进行。通过调查，发现自诉案件立案后在遇到被告人下落不明的时候，法庭裁定中止审理的案件越来越多。但是法庭中止审理之后。自诉案件就被搁置起来，原告的权利得不到维护，附带民事诉讼事实上被终止，由纠纷带来的社会秩序的裂痕无法平复。一些基层法院变通的处理办法是劝说原告撤诉，并告之将来被告出现后可再行起诉，但是这样的变通在法律上又是行不通的。因此，要解决目前在刑事自诉案件中止审理程序中的不足之处，就有必要建立起我国的刑事缺席审判制度，从而有效的解决纠纷。保护公民的合法权利。     

The epistemic structure of a theory of a game

This paper is a contribution to the systematic study of alternative axiom-sets for theories of (normal-form, complete-information) games. It provides an introduction to epistemic logic, describes a formulation in epistemic logic of the structure of a theory of a game (the broad theory of that game), and applies methods of epistemic logic to define strategies for dealing with two disturbing features of game theory, its hyperrationality assumptions and its indeterminacy. The analysis of these problems is conducted in terms of two principles which impregnate much game theory, Cleverness and Cloisteredness (the principles that players know respectively all, and only, the logical consequences of their assumed knowledge). Broad theories allow us to formulate and revise these principles despite their metatheoretical character. It is shown how Cleverness may be weakened by using logics which restrict the Rule of Epistemization, and Cloisteredness by using default logic or autoepistemic logic; the latter is used to characterize Nash equilibrium beliefs as parts of certain autoepistemic extensions of players' knowledge bases, but these particular extensions are rejected as ungrounded.I am grateful for most helpful comments to Robin Cubitt, Joe Halpern, Ernest Geffner, Philippe Mongin, David Squires, Elias Thijsse and Tim Williamson.     

Approximate reference priors for Gaussian random fields

Reference priors are theoretically attractive for the analysis of geostatistical data since they enable automatic Bayesian analysis and have desirable Bayesian and frequentist properties. But their use is hindered by computational hurdles that make their application in practice challenging. In this work, we derive a new class of default priors that approximate reference priors for the parameters of some Gaussian random fields. It is based on an approximation to the integrated likelihood of the covariance parameters derived from the spectral approximation of stationary random fields. This prior depends on the structure of the mean function and the spectral density of the model evaluated at a set of spectral points associated with an auxiliary regular grid. In addition to preserving the desirable Bayesian and frequentist properties, these approximate reference priors are more stable, and their computations are much less onerous than those of exact reference priors. Unlike exact reference priors, the marginal approximate reference prior of correlation parameter is always proper, regardless of the mean function or the smoothness of the correlation function. This property has important consequences for covariance model selection. An illustration comparing default Bayesian analyses is provided with a dataset of lead pollution in Galicia, Spain.