KMV模型在我国股票市场的应用研究

本文将随机选取了18家上市公司,利用其开财务数据和市场数据,建立KMV模型,依次算出公司的股权价值以及股权价值的波动率,资产的市场价值以及资产价值波动率,确定违约点,然后在此基础上求出各公司的违约距离.通过最终得出的公司违约概率度量出上市公司的信用风险水平,实证KMV模型在评估上市公司信用风险时所具有的优势性、可行性、有效性和准确性.     

基于修正的KMV模型对我国上市公司信用风险的度量和适应性分析

本文采用修正的KMV模型对上市公司的信用风险进行度量,摒弃传统的历史波动率方法,采用GARCH模型进行来对收益率进行拟合从而更加准确的估计出股权价值的波动率,并且依照我国的实际情况修正了违约点和股权价值的设定。选取房地产行业10家公司的数据,通过对违约距离的对比和比较,认为KMV模型能较好的适应我国上市公司风险的度量。     

KMV模型对上市公司信用风险预测能力研究

本文利用GARCH模型估计股权价值波动率,用迭代程序估算资产价值及其波动率,修改了模型中非流通股的计价方法,对KMV模型进行了修正。文中对2009年沪深两市30家上市公司的信用风险进行评估检验,结果表明修正后的KMV模型不仅能够较好地分辨出ST公司和非ST公司信用风险的差异,而且能准确地把握上市公司信用质量的变化趋势,至少可提前1年识别上市公司信用质量的恶化。     

基于KMV模型的中小上市公司信用风险实证研究

为了解决中小企业的融资难问题,商业银行在向企业提供贷款时面临对企业的信用风险进行考察的问题。本文在上海证券交易所中小板选取了7家在2013年之前曾经被特别处理的制造业公司作为研究样本。利用KMV模型将这7家样本公司违约前后的违约距离进行对比,实证结果表明KMV模型能够动态地对中小上市企业的信贷风险进行度量和判别。     

基于KMV模型上市公司违约点的选择

对我国上市公司数据进行混合模型回归得到KMV模型违约点,并与KMV公司提出的经验违约点公式以及流动负债加75%长期负债的违约点进行对比,运用KMV模型评价*ST公司和正常公司的信用风险,并检验模型识别上市公司信用风险的能力。结果表明,利用混合面板回归得到违约点的KMV模型能够更好的识别信用风险差异。     

我国上市公司信用风险度量及影响因素分析

1.我国上市公司信用风险度量KMV模型是由KMV公司于1993年开发的现代信用风险评估模型。该模型利用公司资产预期的市场价值、波动率以及负债的帐面价值来预测公司的信用风险。由于数据获取相对容易,计算操作比较简便,KMV模型在国外上市公司信用风险评估中得到了广泛的应用。     

基于KMV模型的资本市场信用风险动态管理实证分析

随着经济发展的进一步深化,上市公司的信用风险更加突出。运用KMV模型,根据资本市场信息能有效地估计公司的信用风险。KMV模型能够根据资本市场企业股价的波动快速的观察公司的理论预期违约率变化情况,从而提供信用风险预警,实现对信用风险的动态观测。弥补目前主要是根据公司是否为ST对公司信用水平进行一个定性的区别的不足。     

基于Matlab计算的KMV模型在中国上市公司信用风险的度量研究

通过对KMV模型与Credit Metrics模型进行比较,选择KMV模型对我国商业银行信用风险进行度量,并在KMV模型中应用Matlab微分方程对我国上市公司的信用风险进行实证计算。最后提出KMV模型反映企业违约风险时的局限性以及对于信用风险管理的相关建议。     

期权理论在公司信用风险定价模型中应用研究

在KMV的模型基础上,运用期权理论介绍和推导银行面临的信用风险度量方法,并对公司价值和波动率计算提出了改进,随后对违约距离的计算进行修正使之更加准确。     

基于LOGIT模型的中小型上市公司信用风险度量研究

信用风险是商业银行面临的主要风险之一。因资本结构的特殊性、高收益高风险性和信息不对称等因素,度量和规避中小型上市公司的信用风险对商业银行来说尤为重要。文章选取中小板块上市公司作为研究样本,提取相关财务指标对样本进行LOGIT分析,测度样本公司的信用风险。其中拟利用因子分析和主成分分析的方法对众多财务指标进行降维处理,便于LOGIT模型的构建。实证结果表明LOGIT模型能够有效测度我国中小型上市公司的信用风险。     

What explains default risk premium during the financial crisis? Evidence from Japan

As is well documented, subprime mortgage markets carried significant default risk. This paper investigates the relationship between default risk premium, stock market conditions and macroeconomic variables during the financial crisis. Using iTraxx Japan Credit Default Swap (CDS) index spreads covering the period from March 2006 to November 2009, we employ a time-varying dynamic factor model with Markov regime switching to generate regime probabilities for default risk. We analyze the sensitivity of default risk premium changes to stock market conditions and macroeconomic variables by using two-state Markov switching models: a crisis regime sparked by rising loan defaults in the sub-prime mortgage market, and a non-crisis regime. We found strong evidence that the relationship between default risk premium changes, stock market and macroeconomic variables is regime-dependent. Our results suggest that during periods of crisis, CDS indices behave as a higher-risk indicator and become more sensitive to stock market conditions and macroeconomic variables. This paper examines the effects of the financial crisis in explaining the default risk premium. Understanding the determinants of default risk premium is important for financial analysts, economic policy makers and credit risk management.     

RATING BASED LÉVY LIBOR MODEL

In this paper, we consider modeling of credit risk within the Libor market models. We extend the classical definition of the default‐free forward Libor rate and develop the rating based Libor market model to cover defaultable bonds with credit ratings. As driving processes for the dynamics of the default‐free and the predefault term structure of Libor rates, time‐inhomogeneous Lévy processes are used. Credit migration is modeled by a conditional Markov chain, whose properties are preserved under different forward Libor measures. Conditions for absence of arbitrage in the model are derived and valuation formulae for some common credit derivatives in this setup are presented.     

信贷资产证券化的违约风险分析

识别和控制资产证券化过程中的风险是金融监管的客观需要。以信贷资产支持证券为例探讨资产证券化的特有风险—违约风险,并运用KMV模型测度个案违约风险,在此基础上提出一些控制违约风险的策略。     

资本资产定价模型的扩展及其在保险中的应用

传统的资本资产定价模型是在一系列过于严格化、理想化的条件下建立起来的。针对现实资本市场情况,通过对资本资产定价模型的应用条件的部分修改,如增加保险公司存在违约风险、交易费用和税收的条件,并且讨论交易费用分别为固定值和保费的函数时的情形以及税收分为固定值和变量的情形,对保费定价问题进行模型扩展。理论推导结果显示,在存在违约风险情况下,保险公司所收保费应该更低;承保费用越少,所需保费就越少;存在税负条件下的公平保费与税收水平有关。     

Small Enterprise Default Prediction Modeling through Artificial Neural Networks: An Empirical Analysis of Italian Small Enterprises

Having accurate company default prediction models is vital for both banks and enterprises, especially small enterprises (SEs). These firms represent a vital part in the economy of every country but are also typically more informationally opaque than large corporations. Therefore, these models should be precise but also easily adaptable to suit SE characteristics. Our study applies artificial neural networks (ANNs) to a sample of over 7,000 Italian SEs. Results show that (1) when compared with traditional methods, ANNs can make a better contribution to SE credit‐risk evaluation; and (2) when the model is separately calculated according to size, geographical area, and business sector, ANNs prediction accuracy is markedly higher for the smallest sized firms and for firms operating in Central Italy.     

The risk of betting on risk: Conditional variance and correlation of bank credit default swaps

Credit default swaps (CDS) have been used to speculate on the default risk of the reference entity. The risk of CDS can be measured by their second moments. We apply a Glosten, Jagannathan, and Runkle (GJR)-t model for the conditional variance and a Dynamic Conditional Correlation (DCC)-t model for the conditional correlation. Based on the CDS of six large US banks from 2002 to 2018, we find that CDS conditional variance is asymmetric and leptokurtic. A positive innovation actually increases CDS conditional variance more than a negative innovation does. CDS conditional correlations have stayed elevated since the financial crisis, in contrast to the decreasing stock conditional correlations.     

Competing Risk Model for Technology Credit Fund for Small and Medium‐Sized Enterprises

Despite the need to foster a technology‐intensive industry, most Korean SMEs (small and medium‐sized enterprises) are faced with the difficulty of raising funds. To resolve this problem, the government set up the technology credit fund to give loans to enterprises that achieve a certain technology evaluation score. However, many of the recipient SMEs fail to pay back the loans for various reasons. In this paper, we distinguish two causes of default due to owner and company, respectively, using the competing risk model. The proposed prediction models for competing defaults are expected to contribute to the healthy management of technology finance.     

A UNIFIED FRAMEWORK FOR PRICING CREDIT AND EQUITY DERIVATIVES

We propose a model which can be jointly calibrated to the corporate bond term structure and equity option volatility surface of the same company. Our purpose is to obtain explicit bond and equity option pricing formulas that can be calibrated to find a risk neutral model that matches a set of observed market prices. This risk neutral model can then be used to price more exotic, illiquid, or over‐the‐counter derivatives. We observe that our model matches the equity option implied volatility surface well since we properly account for the default risk in the implied volatility surface. We demonstrate the importance of accounting for the default risk and stochastic interest rate in equity option pricing by comparing our results to Fouque et al., which only accounts for stochastic volatility.     

INCORPORATING RISK AND AMBIGUITY AVERSION INTO A HYBRID MODEL OF DEFAULT

It is well known that purely structural models of default cannot explain short‐term credit spreads, while purely intensity‐based models lead to completely unpredictable default events. Here we introduce a hybrid model of default, in which a firm enters a “distressed” state once its nontradable credit worthiness index hits a critical level. The distressed firm then defaults upon the next arrival of a Poisson process. To value defaultable bonds and credit default swaps (CDSs), we introduce the concept of robust indifference pricing. This paradigm incorporates both risk aversion and model uncertainty. In robust indifference pricing, the optimization problem is modified to include optimizing over a set of candidate measures, in addition to optimizing over trading strategies, subject to a measure dependent penalty. Using our model and valuation framework, we derive analytical solutions for bond yields and CDS spreads, and find that while ambiguity aversion plays a similar role to risk aversion, it also has distinct effects. In particular, ambiguity aversion allows for significant short‐term spreads.     

CREDIT SPREADS, OPTIMAL CAPITAL STRUCTURE, AND IMPLIED VOLATILITY WITH ENDOGENOUS DEFAULT AND JUMP RISK

We propose a two-sided jump model for credit risk by extending the Leland–Toft endogenous default model based on the geometric Brownian motion. The model shows that jump risk and endogenous default can have significant impacts on credit spreads, optimal capital structure, and implied volatility of equity options: (1) Jumps and endogenous default can produce a variety of non-zero credit spreads, including upward, humped, and downward shapes; interesting enough, the model can even produce, consistent with empirical findings, upward credit spreads for speculative grade bonds. (2) The jump risk leads to much lower optimal debt/equity ratio; in fact, with jump risk, highly risky firms tend to have very little debt. (3) The two-sided jumps lead to a variety of shapes for the implied volatility of equity options, even for long maturity options; although in general credit spreads and implied volatility tend to move in the same direction under exogenous default models, this may not be true in presence of endogenous default and jumps. Pricing formulae of credit default swaps and equity default swaps are also given. In terms of mathematical contribution, we give a proof of a version of the "smooth fitting" principle under the jump model, justifying a conjecture first suggested by Leland and Toft under the Brownian model.