企业信用风险与宏观经济变量的相关性研究

本文利用Logit模型和多元回归模型.分析了宏观经济变量对企业总体和个体信用风险的影响,认为相对于个体信用风险而言,宏观经济变量对总体信用风险的影响更加显著.     

基于VAR模型的商业银行信用风险与经济周期关系的实证研究

本文以不良贷款率作为评估商业银行信用风险的指标,选取对银行不良贷款率构成冲击的反映经济周期的宏观经济变量构建VAR模型,进行脉冲响应和方差分解分析。结果显示,CPI、GDP增长率比M2增长率更显著地影响到商业银行的信用风险水平,影响呈现周期性;信用风险水平与GDP增长率的滞后变量呈负相关关系,与CPI的滞后变量呈正相关关系;数量关系方面GDP增长率增加1个百分点,不良贷款率约降低1.7个百分点。     

宏观经济因素对企业债券信用风险的影响

在企业债券融资规模不断增大的同时,不能忽视企业债券的信用风险。本文着重分析了宏观经济因素对企业债券信用风险的影响。其中,宏观经济因素包括利率、通货膨胀、股票市场波动率。在理论分析的基础上,本文通过建立面板回归模型实证分析了各个宏观经济因素对企业债券信用风险的影响。     

基于时变参数模型的银行信用风险与宏观经济形势的动态关系研究

压力测试是用于评价金融机构在极端冲击下的系统风险承担能力的一种风险量化分析工具,对金融机构的资产组合和风险管理具有重要意义。本文运用带随机波动的时变参数向量自回归模型,以CQ地区为例,以银行体系整体信用风险和房地产贷款信用风险作为研究对象,观察宏观经济因素与信用风险水平的动态关系,并对其进行宏观压力测试。研究结果表明,CQ地区银行体系的整体不良率在宏观经济增速下降和利率上升的情况下,依然能保持稳定;房贷不良率对加权贷款平均利率的上升也不敏感;CQ地区银行体系信用风险对宏观经济冲击的抗风险能力和对宏观经济冲击的缓释能力较强,总体运行较为稳健。     

宏观经济数据影响下的信用风险压力测试研究

本文开展了宏观经济数据影响下的信用风险的压力测试。首先分析了影响信用风险相关的宏观数据与财务指标，比较了Logit模型和多元线性模型，提出了GDP产出缺口、贷款利率、汇率等宏观经济指标及有关财务比率。其次在考察宏观经济指标变化的基础上，设定了重度、特重压力指标。根据对江苏省金融数据的分析，本文认为在宏观经济重度压力指标下，商业银行信用风险压力测试结果表明形势严峻，会出现亏损，将给商业银行经营管理带来冲击和考验。     

宏观经济波动与信用风险:结构模型

信用风险与宏观经济周期性波动之间的关系大致上可归纳为两种典型情况.但实践中无论是商业银行还是监管者都没有走向这两种极端情形.通过将宏观经济周期性波动考虑到模型中去,可以对结构模型计量影响信用风险的这种周期性因素做出评价.     

我国商业银行房地产信贷风险相关因素分析——基于CPV信用风险度量模型

本文基于CPV模型,选取2001年至2007年我国四个宏观经济变量的季度数据,通过回归分析研究我国商业银行房地产信贷风险与宏观经济变量之间的相关关系。实证结果表明,我国房地产贷款信用风险与宏观经济状况紧密相连,与宏观经济一致指数、企业景气指数呈负向变动关系,与国房景气指数、居民消费价格指数呈正向变动关系,据此提出了我国商业银行控制房地产信贷风险的几点对策建议。     

生存分析、宏观经济变量与违约概率

在研究个人贷款违约风险中,传统的研究往往只单纯地将宏观经济指标作为协变量,并未考虑宏观经济指标的时变交互特征(vandell,1993;Zandi,1998)。国外研究达成共识的是以Logistic回归的传统模型不能给出违约概率的动态预测值,且反映经济形势的宏观经济变量也不能纳入模型中。论文论述了生存分析与Logistic模型的理论机理,试图在借贷违约风险中加入系统性风险对违约的影响因素,克服了以Logistic回归模型为代表的传统模型在度量信贷违约概率时仅考虑个体非系统性风险的局限。研究结论说明宏观经济变量确实对违约风险有影响,对网贷违约风险来说,Cox模型更优于Logistic函数。     

我国上市公司信用风险度量及影响因素分析

首先利用KMV模型度量了样本公司的信用风险,接着以资产规模、股权结构、成长能力、流动性、行业前景、公司经营状况作为解释变量、公司违约距离作为被解释变量,建立了多元线性回归模型。实证分析表明,资产规模、股权集中度、成长能力与信用风险正相关;国有股比重、流动性与信用风险负相关;行业前景和公司经营状况对信用风险也有显著影响。     

风险联动视角下的中美货币政策考察北大核心CSSCISSCI

本文运用TVFAVAR模型,研究了中美基准利率调整所引发的市场风险和信用风险联动。研究发现,在繁荣时期,短期利率与市场风险和信用风险之间的联动存在"离散效应",在不景气时期存在"复合效应",宏观经济条件的恶化放大了利率的不利冲击,且美国市场间存在的市场风险因子与信用风险核心变量间的联动性在样本内趋于增强,忽视风险间的动态相互作用会造成对整体风险估计的偏误。本文对认识实体经济与金融市场间复杂的相互依赖关系也具有重要意义。     

Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics

Understanding if credit risk is driven mostly by idiosyncratic firm characteristics or by systematic factors is an important issue for the assessment of financial stability. By exploring the links between credit risk and macroeconomic developments, we observe that in periods of economic growth there may be some tendency towards excessive risk-taking. Using an extensive dataset with detailed information for more than 30 000 firms, we show that default probabilities are influenced by several firm-specific characteristics. When time-effect controls or macroeconomic variables are also taken into account, the results improve substantially. Hence, though the firms’ financial situation has a central role in explaining default probabilities, macroeconomic conditions are also very important when assessing default probabilities over time.     

Credit variance risk premiums

This paper studies variance risk premiums in the credit market using a novel data set of swaptions quotes on the CDX North America Investment Grade and High-Yield indices. The returns of credit variance swaps are negative and economically large, irrespective of the credit rating class. They are robust to transaction costs and cannot be explained by established risk factors and structural model variables. We also dissect the overall variance risk premium into receiver and payer variance risk premiums. We show that credit variance risk premiums are mainly driven by the payer corridor, which is associated with worsening macroeconomic conditions.     

The Relevance of Accounting Data in the Measurement of Credit Risk

Abstract

Option pricing theory provides a robust and theoretically sound framework for the measurement of credit risk. Assuming perfect market conditions, information relevant to the measurement of a firm’s credit risk is reflected in its equity price, with no role for accounting data. This hypothesis is tested using UK data and credit ratings as a proxy for credit risk. It is found that Merton’s distance-to-default measure is the most significant variable in the measurement of credit risk. However, it is also found that accounting variables are incrementally informative when added to a model that contains only the distance-to-default measure. The incremental informativeness of accounting data varies across industries and depends on firm size. Although it is found that the general level of credit risk depends on the state of the economy, there is no evidence to suggest that the incremental informativeness of the accounting variables depends upon macroeconomic conditions.     

Credit Risk in Two Institutional Regimes: Spanish Commercial and Savings Banks

Although credit risk is an important factor that financial institutions must cope with, the determinants of bank problem loans have been little studied. Using panel data, we compare the determinants of problem loans of Spanish commercial and savings banks in the period 1985–1997, taking into account both macroeconomic and individual bank level variables. The GDP growth rate, firms, and family indebtedness, rapid past credit or branch expansion, inefficiency, portfolio composition, size, net interest margin, capital ratio, and market power are variables that explain credit risk. However, there are significant differences between commercial and savings banks, which confirm the relevance of the institutional form in the management of credit risk. Our findings raise important bank supervisory policy issues: the use of bank level variables as early warning indicators, the advantages of bank mergers from different regions, and the role of banking competition and ownership in determining credit risk.     

Market conditions,default risk and credit spreads

This study empirically examines the impact of the interaction between market and default risk on corporate credit spreads. Using credit default swap (CDS) spreads, we find that average credit spreads decrease in GDP growth rate, but increase in GDP growth volatility and jump risk in the equity market. At the market level, investor sentiment is the most important determinant of credit spreads. At the firm level, credit spreads generally rise with cash flow volatility and beta, with the effect of cash flow beta varying with market conditions. We identify implied volatility as the most significant determinant of default risk among firm-level characteristics. Overall, a major portion of individual credit spreads is accounted for by firm-level determinants of default risk, while macroeconomic variables are directly responsible for a lesser portion.     

Credit risk,managerial behaviour and macroeconomic equilibrium within dual banking systems: Interest-free vs. interest-based banking industries

In this paper, an attempt has been made to explore the determinants of credit risk in the banking system with a particular interest toward the Islamic banking industry. We analyze the link between credit risk and a set of bank-specific and macroeconomic along with institutional variables using two complementary approaches. First, we investigate the factors of credit risk using one-step generalized method of moments (GMM) system estimator. Then, we explore the feedback between credit risk and its determinants in a panel vector autoregressive (PVAR) model. We have used a sample of Middle Eastern, North African (MENA) and Asian countries to apply our model. The major purpose of this paper is to find factors that could explain credit risk within the interest-free banking system relative to the interest-based one.     

Predicting credit spreads

Predictions of firm-level credit spreads based on the current spot and forward credit spreads can be significantly improved upon by using the information contained in the shape of the credit-spread curve. However, the current credit-spread curve is not a sufficient statistic for predicting future out-of-sample credit spreads; predictions can be significantly improved upon by exploiting the information contained in the shape of the riskless yield curve. In the presence of credit-spread and riskless factors, other macroeconomic, marketwide, and firm-specific risk variables do not significantly improve predictions of credit spreads. These results have important implications for credit-spreads modeling as well as for better understanding corporate capital structure and risk management policies.     

Re-Examining the Determinants of Islamic Bank Performance: New Evidence from Dynamic GMM,Quantile Regression,and Wavelet Coherence Approaches

This study is the first attempt to conduct a comparative analysis of the internal and external determinants of the Islamic banks’ profitability in the GCC region applying dynamic GMM, quantile regression, and wavelet coherence approaches. The dynamic GMM tends to indicate that equity financing and operating efficiency and macroeconomic variables such as money supply, and inflation are significantly related to Islamic banks’ performance. The bank-specific variables such as credit risk, equity ratio, and cost-efficiency ratios are not significant at different percentiles. ROA is driven by credit risk, equity ratio, and cost-efficiency ratios (as evidenced in wavelet coherence analysis).     

Credit cycles and macro fundamentals

We use an intensity-based framework to study the relation between macroeconomic fundamentals and cycles in defaults and rating activity. Using Standard and Poor's U.S. corporate rating transition and default data over the period 1980–2005, we directly estimate the default and rating cycle from micro data. We relate this cycle to the business cycle, bank lending conditions, and financial market variables. In line with earlier studies, the macro variables appear to explain part of the default cycle. However, we strongly reject the correct dynamic specification of these models. The problem is solved by adding an unobserved dynamic component to the model, which can be interpreted as an omitted systematic credit risk factor. By accounting for this latent factor, many of the observed macro variables loose their significance. There are a few exceptions, but the economic impact of the observed macro variables for credit risk remains low. We also show that systematic credit risk factors differ over transition types, with risk factors for downgrades being noticeably different from those for upgrades. We conclude that portfolio credit risk models based only on observable systematic risk factors omit one of the strongest determinants of credit risk at the portfolio level. This has obvious consequences for current modeling and risk management practices.     

‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables

Aggregate stock return volatility is both persistent and countercyclical. This paper tests whether it is possible to improve volatility forecasts at monthly and quarterly horizons by conditioning on additional macroeconomic variables. I find that several variables related to macroeconomic uncertainty, time-varying expected stock returns, and credit conditions Granger cause volatility. It is more difficult to find evidence that forecasts exploiting macroeconomic variables outperform a univariate benchmark out-of-sample. The most successful approaches involve simple combinations of individual forecasts. Predictive power associated with macroeconomic variables appears to concentrate around the onset of recessions.