Pricing Sovereign Credit Risk of Poland: Evidence from the CDS Market

We analyze the market assessment of sovereign credit risk using a reduced-form model to price the credit default swap (CDS) spreads, thus enabling us to derive values for the probability of default (PD) and loss given default (LGD) from the quotes of sovereign CDS contracts. We compare different specifications of the models allowing for both fixed and time-varying LGD, and we use these values to analyze the sovereign credit risk of Polish debt throughout the period of a global financial crisis. Our results suggest the presence of a low LGD and a relatively high PD during a recent financial crisis.     

Modeling severity risk under PD–LGD correlation

In this article, a generic severity risk framework in which loss given default (LGD) is dependent upon probability of default (PD) in an intuitive manner is developed. By modeling the conditional mean of LGD as a function of PD, which also varies with systemic risk factors, this model allows an arbitrary functional relationship between PD and LGD. Based on this framework, several specifications of stochastic LGD are proposed with detailed calibration methods. By combining these models with an extension of CreditRisk+, a versatile mixed Poisson credit risk model that is capable of handling both risk factor correlation and PD–LGD dependency is developed. An efficient simulation algorithm based on importance sampling is also introduced for risk calculation. Empirical studies suggest that ignoring or incorrectly specifying severity risk can significantly underestimate credit risk and a properly defined severity risk model is critical for credit risk measurement as well as downturn LGD estimation.     

Copula‐Based Formulas to Estimate Unexpected Credit Losses (The Future of Basel Accords?)

The model used to estimate the capital required to cover unexpected credit losses in financial institutions (Basel II) has some drawbacks that reduce its ability to capture potential joint extreme losses in downturns. This paper suggests an alternative approach based on Copula Theory to overcome such flaws. Similarly to Basel II, the suggested model assumes that defaults are driven by a latent variable which varies as a response to an unobserved factor. On the other hand, the use of copulas allows the identification of asymmetric dependence between defaults which has been registered in the literature. As an example, a specific copula family (Clayton) is adopted to represent the association between the latent variables and a formula to estimate potential unexpected losses at a certain level of confidence is derived. Simulations reveal that, in most of the cases, the alternative model outperforms Basel II for portfolios with right‐tail‐dependent probabilities of default (supposedly, a good representation for real loan portfolios).     

The Sensitivity of the Loss Given Default Rate to Systematic Risk: New Empirical Evidence on Bank Loans

We verify the existence of a relation between loss given default rate (LGDR) and macroeconomic conditions by examining 11,649 bank loans concerning the Italian market. Using both the univariate and multivariate analyses, we pinpoint diverse macroeconomic explanatory variables for LGDR on loans to households and SMEs. For households, LGDR is more sensitive to the default-to-loan ratio, the unemployment rate, and household consumption. For SMEs, LGDR is influenced by the total number of employed people and the GDP growth rate. These findings corroborate the Basel Committee’s provision that LGDR quantification process must identify distinct downturn conditions for each supervisory asset class.

A Critique of Revised Basel II

This paper critiques the revised Basel II capital requirements for banks. To provide a framework for analysis, the XYZ theory of regulatory capital is formulated. Independent of the XYZ theory, we argue that the revised Basel II capital rule for credit risk is not a good approximation to the ideal rule. Based on this, and using the XYZ theory, we argue that: (1) the revised Basel II rules should not replace the existing approaches for determining minimal capital standards, but should be used in conjunction with them, and (2) that calibrating the capital rules to maintain aggregate market capital is a prudent procedure.     

When Are Commercial Loans Secured?

We analyze the factors that influence the decision to secure a commercial loan. We find evidence that variables reflecting adverse selection, moral hazard, and the prospects for default all affect the likelihood a loan will be collateralized. We find no evidence in favor of the predictions of certain theoretical models that high‐quality firms signal by providing collateral. Our results also show that lenders with less risk protection in the form of equity capital are more likely to require collateral, but that banks themselves are less likely to secure loans than nonbanks. Certain loan characteristics also influence the collateralization decision.     

股票抵押贷款预期回收率比较分析

股票抵押贷款是一种风险较大的产品.股票价值和收益的高波动性造成抵押品价值的回收率不稳定,进而影响贷款预期回收率,使得银行经营风险加大.为了有效估算股票抵押贷款回收率,本文运用预期回收率模型,借助中国色诺芬股票数据库和美国CRSP股票数据库信息,比较分析不同市场情况下预期回收率的区别.分析发现中国市场由于近几年股票波动率加强,整体预期回收率较低,而美国则相反;预期回收率与抵押率、违约概率都成负相关关系,而且预期回收率对于抵押率的变化非常敏感;60%贷款抵押率的设定在美国市场是可行的,而中国市场合理抵押率是54%左右.     

信用违约风险传染模型的比较研究

本文试图对几种有代表性的模型进行比较,来分析由于建模方式的不同,而导致的对信用期权定价和对冲的结果的不同.如果将违约风险传染考虑进去,类似德隆帝国崩溃的事件,或许就能避免.     

Modelling Credit Risk for SMEs: Evidence from the U.S. Market

Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyse its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analysed and the most significant variables in predicting the entities' credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 U.S. firms (with sales less than $65 million) over the period 1994–2002, we develop a one-year default prediction model. This model has an out-of-sample prediction power which is almost 30 per cent higher than a generic corporate model. An associated objective is to observe our model's ability to lower bank capital requirements considering the new Basel Capital Accord's rules for SMEs.     

Basel-2 capital adequacy: Computing the ‘fair’ capital charge for loan commitment ‘true’ credit risk

This research makes two contributions: (i) to price analytically put option and extension premium embedded in a borrower-extendible commitment, and (ii) to compute the ‘fair’ capital charge that corresponds to the commitment ‘true’ credit risk. In doing so, the procedure replaces the BIS accounting-based concepts of credit-conversion factor, principal-risk factor, and initial term to maturity of irrevocable commitments with the market-based concepts of exercise-cum-takedown proportion and put value implicit in the borrower-extendible commitment, respectively. Finally, the approach is developed one step further to account for the borrowers' risk ratings by public credit agencies; this results in a two-dimensional (time-state of nature) risk-weighting system that applies to all commitment types.     

Marking-to-model credit and operational risks of loan commitments: A Basel-2 advanced internal ratings-based approach

Within a marking-to-model framework, this research computes the bank's capital charge for credit and operational risks of loan commitments at Basel-2 fixed audit date. This is done in three steps. The first one prices commitment credit risk as a Gram-Charlier put value and determines the commitment forward-funding proportion. In the second one, put value and funding proportion are combined to compute Basel-2 ‘fair’ capital charge for credit and operational risks. By producing a moderate total capital charge, marking-to-model offers substantial capital relief with respect to the corresponding charge computed with Basel-2 simplified approach. Both charges are however larger than the corresponding nil charge arrived at in Basel-1. In the third step, marking-to-model reveals its flexibility by showing how banks can determine the cost of their exposure to borrowers' credit-rating downgrades and how they can also hedge any exposure to commitment default risk.     

SECONDARY MORTGAGE MARKET PURCHASE COMMITMENT YIELDS

We provide empirical evidence that quoted secondary market mortgage yields conform to the predictions of option theory. We compare Fannie Mae and Freddie Mac origination yields offered in the secondary market from 1985 to 2003 with the predictions of a two‐state binomial mortgage option valuation model. Our two‐state approach considers a mean‐reverting interest rate process as well as a stochastic housing price. Using predictions from option simulations, we find strong links between market practice and mortgage option prepayment and default factors over time. We also find cross‐sectional differences that are consistent with the institutional structure of the markets.