基于相依违约的上市公司担保风险度量研究

本文运用基于相依违约的混合模型度量上市公司担保风险,并进行了实证研究。结果表明:此模型能很好的预测上市公司对外担保的违约概率,可对上市公司信用进行评级;在敏感性分析中,违约概率对波动率、无风险利率和相依结构比较敏感,这能为风险管理提供一定的参考。     

利用KMV仿真模型动态监控科技型中小上市公司违约风险北大核心

国内外目前尚没有KMV模型的简单计算方法,许多学者只能通过复杂编程来实现KMV的运行。利用Simulink功能模块选择及模块设置可对复杂的KMV模型进行系统仿真集成,通过友好界面利用KMV模型快速计算中小上市公司违约距离和违约概率。选取沪深两市14家ST科技型中小上市公司以及20家非ST科技型中小上市公司进行实验研究,结果表明:发生财务危机后ST科技型中小上市公司与非ST科技型中小上市公司信用风险违约距离存在显著区别;但一般情况下,它们的信用风险违约概率都较低;金融机构可以利用MKV仿真模型对科技型上市公司进行实时动态风险监控。     

我国制造业上市公司信用风险度量:基于KMV模型

目前对上市公司信用风险度量模型进行研究,及依据行业特征构造行业信用风险度量模型,对于商业银行信用风险管理显得尤为重要。本文基于现代信用风险度量模型——KMV模型,通过对违约点、股权价值波动性率、违约距离计算方式进行修正,构建了制造业行业KMV模型。研究结果表明,当违约点选取长期债务和短期债务之和时,制造业KMV模型可以提前两年将信用好与不好的上市公司甄别开,具有较强的适用性,符合我国目前信用风险管理水平的需要。同时,运用制造业KMV模型还可以判断上市公司是否具有"壳"资源的价值。     

基于Naive模型的违约风险测度

随着美国次贷危机的诞生、扩散及深化,违约风险的度量技术和管理方法成为研究者关注的焦点。传统的KMV模型也获得了新发展,文章就介绍了KMV的一种最新变体:Naive模型,并运用它对中国A股上市公司过去6年间的违约概率和违约距离进行了分析,结果发现,该模型的测量结果 0.0777,较好地度量了我国上市公司的违约概率。     

关于我国上市公司违约虱险评价效率的研究

上市公司违约风险评价是目前我国在信用风险评估上较为薄弱的环节。中国商业银行和评级公司应该积极创造条件,加强客户违约概率测度．以有效提升信用风险管理水平。笔者拟着重对KMV模型评估违约风险的基本原理、KMV模型在国内外的运用现状进行探讨．并对该模型在现实中的运用提出若干建议。     

修正的KMV模型及其在我国上市公司信用风险度量中的应用

文章在主要阐述KMV模型基本原理的基础上,根据我国经济市场的特殊性对KMV模型进行了修正。通过对5个行业共10家上市公司的信用风险进行分析表明,采用修正后的KMV模型在中国的适用性和准确性都有所提高,能够较明显地看出信用较好的公司的违约距离比ST公司的要大,证明了KMV在预测信用风险上的作用,即可以比较准确地把握上市公司信用质量的变化趋势。     

基于KMV模型的信用风险实证分析——以食品行业上市公司为例

食品行业作为我国的"民心行业",安全问题日益凸显。作为我国食品行业领域最具代表性的企业——食品行业上市公司,诸如食品安全等多种违规现象带来的巨大投资风险严重困扰着广大投资者。本文从如何规避上市公司信用风险入手,通过分析利用KMV模型型,运用MATLAB软件,对我国食品行业4家上市公司6年的财务数据进行了违约距离和违约率的实证分析。结果显示,KMV模型能较好地识别上市公司的信用风险能力力,能够为金融机构、投资者和普通消费者揭示食品类上市公司的信用风险。     

修正的KMV模型及其在我国上市公司信用风险度量中的应用

文章在主要阐述KMV模型基本原理的基础上,根据我国经济市场的特殊性对KMV模型进行了修正。通过对5个行业共10家上市公司的信用风险进行分析表明,采用修正后的KMV模型在中国的适用性和准确性都有所提高,能够较明显地看出信用较好的公司的违约距离比ST公司的要大,证明了KMV在预测信用风险上的作用,即可以比较准确地把握上市公司信用质量的变化趋势。     

基于KMV模型的信贷违约风险探析

本文采用KMV模型,结合相关的财务特征指标,对我国上市公司的信用状况进行实证检验.检验结果表明,KMV模型能在上市公司财务困境发生前三年对上市公司的违约风险进行预警,在我国具有较好的适用性.根据实证检验结果,提出将不同预警模型相结合,层次递进建立我国信贷风险预警机制的建议.     

预期财务困境成本在债务违约预测模型中的信息含量

本文首先对预期财务困境成本、财务政策与违约概率之关系进行理论分析,模型显示,预期财务困境成本愈高,债务违约概率愈小。在此基础上,作者选择2002~2004年间首次出现债务违约的97家A股上市公司为违约样本,并予以配对,应用二元Logistic回归模型实证考察预期财务困境成本与违约概率之关系。实证结果表明,预期财务困境成本在债务违约预测模型中既具有相对信息含量,也具有增量信息含量,即在基准模型的基础上引入预期财务困境成本的代理变量能够显著降低模型的误判率。     

参数不稳定下利差在宏观经济与货币政策中的应用

本文在参数不稳定的情况下考察利差对通货膨胀、经济增长的信息作用,并分析包含利差的混合货币政策规则。基于贝叶斯区制转移模型（MSBVAR）的分析发现：期限利差和信用利差对通货膨胀、经济增长有影响,宏观经济波动使得利差的信息作用存在参数不稳定性,在经济下行时对经济增长有负向信号作用。信用利差对通胀的预测作用比期限利差对通胀的预测作用更强。期限利差和信用利差都在货币政策规则中有参数不稳定性的信息作用。     

Endogenous credit standards and aggregate fluctuations

Widespread empirical evidence shows that credit standards fluctuate over the business cycle. We build a macroeconomic model in which countercyclical lending standards emerge as an equilibrium outcome. In the model, banks compete on lending rates as well as collateral requirements. The presence of lending relationships between firms and banks gives rise to endogenous fluctuations in interest rate margins and collateral requirements. We demonstrate that endogenous credit standards amplify business cycles, driving up output volatility by around 25% when compared to a model without lending relationships. Finally, we show that in order to combat the effects of endogenous credit standards on macroeconomic volatility, a countercyclical loan-to-value ratio is an effective macroprudential policy tool.     

Volatility spillovers among the U.S. and Asian stock markets: A comparison between the periods of Asian currency crisis and subprime credit crisis

This paper examines the changing nature of volatility spillovers among the U.S. and eight East Asian stock markets between two financial crises: the Asian currency crisis and the U.S. subprime credit crisis. Our empirical results suggest that volatility is not always spilled over from the directly affected markets to surrounding markets in crisis periods. The East Asian markets who directly suffered from the Asian currency crisis are the ones to which volatility is spilled over from other markets during the Asian currency crisis period, whereas uni-directional volatility spillovers from the U.S. market to other markets are observed during both crisis periods. This difference can be explained by a pre-determined hierarchy in which volatility spillovers tend to start from the U.S. market regardless of the geographical origin of the crisis. Furthermore, our results reveal that the markets in three major Asian financial hubs, i.e., Japan, Hong Kong and Singapore, are the markets to which volatility is spilled over uni-directionally from several other countries during the subprime credit crisis period, but not during the Asian currency crisis period. We attribute this difference to crisis-specific (currency or credit crisis), market-specific (credit derivatives market participation and foreign currency reserves), and time-specific (more integrated global market) factors.     

Corporate credit risk prediction under stochastic volatility and jumps

This paper examines the impact of allowing for stochastic volatility and jumps (SVJ) in a structural model on corporate credit risk prediction. The results from a simulation study verify the better performance of the SVJ model compared with the commonly used Merton model, and three sources are provided to explain the superiority. The empirical analysis on two real samples further ascertains the importance of recognizing the stochastic volatility and jumps by showing that the SVJ model decreases bias in spread prediction from the Merton model, and better explains the time variation in actual CDS spreads. The improvements are found particularly apparent in small firms or when the market is turbulent such as the recent financial crisis.     

商业银行总体经济资本测度方法比较研究

总体经济资本测度是商业银行实施《新巴塞尔资本协议》的关键。本文比较研究了商业银行总体经济资本的两种测度方法：资产波动法和收益波动法,分别介绍了两种测度方法的基本原理、计算步骤以及各自的应用范围和侧重点,并对两种方法相对应的经济资本加总技术,如搭积木法、椭圆分布法、Copula连接函数方法、多因子模型和混合多因子模型等进行了比较研究。     

Asymmetric determinants of corporate bond credit spreads in China: Evidence from a nonlinear ARDL model

This study utilizes the nonlinear ARDL (NARDL) model proposed by Shin, Yu, and Greenwood-Nimmo (2014) to quantify the potentially asymmetric transmission of positive and negative changes in each of the possible determinants of industry-level corporate bond credit spreads in China. The determinants we consider include the corresponding industry stock price, China’s stock market volatility, the level and slope of the yield curve (i.e., the interest rate), the industrial production growth rate, and the inflation rate. The empirical results suggest substantial asymmetric effects of these determinants on credit spreads, with the positive changes in the determinants showing larger impacts than the negative changes for most industries we consider. Moreover, the corresponding industry stock prices, the interest rate, and the industrial production growth rate negatively drive the industry credit spreads for many industries. In turn, China’s stock market volatility and the inflation rate positively affect the credit spreads at each industry level. These findings may be helpful to investors, bond issuers and policymakers in understanding the dynamics of credit risks and corporate bond rates at the industry level.     

The effect of banking market structure on the volatility of growth of manufacturing sectors in developing countries

I explore the effect of banking concentration and banking competition on the volatility of the growth of value added of manufacturing sectors in the developing countries. In this paper, I bring together two strands of literature, one that discusses the effect of financial intermediation on volatility of growth and another one that discusses the effect of banking concentration and competition on credit access. Following the industrial organization literature, I look at the effect of banking competition and banking concentration on the volatility of manufacturing sectors separately. I find that banking concentration has a dampening effect on the volatility of growth of the industries. On the other hand, I find that as banking competition increases, the volatility of the growth of industries increases, also.     

Economic growth,volatility and their interaction: What’s the role of finance?

This paper examines the relation between financial depth and the interaction of economic growth and its volatility. We use a sample of 52 countries for the period 1980–2011, and our main finding is that, at moderate levels of financial depth, further deepening increases the ratio of average growth to volatility; however, as financial depth increases, this relation reverts, and the rise in volatility overcomes that of economic growth. This result is obtained both in the medium and long run; however, the peak of the relation seems to be lower in the medium run (around 40%–55% of domestic credit/GDP) than in the long run (around 75%–99%). This suggests that increasing the level of domestic credit may intensify relative volatility in the medium term, but still raise relative long-term growth before the long-run threshold is achieved.     

Realising the future: forecasting with high‐frequency‐based volatility (HEAVY) models

This paper studies in some detail a class of high‐frequency‐based volatility (HEAVY) models. These models are direct models of daily asset return volatility based on realised measures constructed from high‐frequency data. Our analysis identifies that the models have momentum and mean reversion effects, and that they adjust quickly to structural breaks in the level of the volatility process. We study how to estimate the models and how they perform through the credit crunch, comparing their fit to more traditional GARCH models. We analyse a model‐based bootstrap which allows us to estimate the entire predictive distribution of returns. We also provide an analysis of missing data in the context of these models. Copyright © 2010 John Wiley & Sons, Ltd.