Optimal Put Exercise: An Empirical Examination of Conditions for Mortgage Foreclosure

Implicit in option-pricing models of mortgage valuation are threshold levels of put-option value that must be crossed to induce borrower default. There has been little research into what these threshold values are that come out of pricing models or how they compare to exercised option values seen in empirical data. This study decomposes boundary conditions for optimal default exercise to look at the economic dynamics that should lead to optimal default timing. Empirical data on FHA insured mortgage foreclosures is then examined to discern the predictive influence of optimal-option-valuation-and-exercise variables on observed default timing and values. Interesting results include a new understanding of how to measure and use property equity variables during economic downturns, house-price index ranges over which default is exercised for various classes of borrowers, and implied differences in appreciation rates between market-price indices and foreclosed properties.     

The relationship between liquidity risk and credit risk in banks

This paper investigates the relationship between the two major sources of bank default risk: liquidity risk and credit risk. We use a sample of virtually all US commercial banks during the period 1998–2010 to analyze the relationship between these two risk sources on the bank institutional-level and how this relationship influences banks’ probabilities of default (PD). Our results show that both risk categories do not have an economically meaningful reciprocal contemporaneous or time-lagged relationship. However, they do influence banks’ probability of default. This effect is twofold: whereas both risks separately increase the PD, the influence of their interaction depends on the overall level of bank risk and can either aggravate or mitigate default risk. These results provide new insights into the understanding of bank risk and serve as an underpinning for recent regulatory efforts aimed at strengthening banks (joint) risk management of liquidity and credit risks.     

Bias in an Empirical Approach to Determining Bond and Mortgage Risk Premiums

Empirical studies of bond and commercial mortgage performance often quantify a required risk premium by examining the difference between the promised yield and the realized yield as adjusted for default occurrence. These studies omit the effects of various other sources of risk, however, including collateral asset market risk, interest rate risk, and possibly call risk. These omissions downwardly bias the empirical risk premium estimate on the debt. In this paper, we disentangle and quantify the sources of this bias by modeling secured coupon debt (the commercial mortgage) as used in the calculation of a realized investment return. We consider deterministic and stochastic interest rate economies with mortgage contracts that are either noncallable or subject to a temporary prepayment lockout period. Given realistic parameter values associated with the term structure, underlying asset dynamics, and debt contracting, we show that the magnitude of the bias can be significant.     

Determinants of earnout as acquisition payment currency and bidder’s value gains

We examine the wealth effects of a comprehensive sample of UK bidders offering contingent payment, or earnout, as consideration for their acquisitions. We show that bidders using earnout generate significantly higher announcement and post-acquisition value gains than bidders using non-earnout currencies (such as cash, stock exchange, or mixed payments). We construct a logistic model to predict when it is optimal for a bidder to offer earnout. We show that bidders offering earnout optimally enjoy significantly higher announcement and post-acquisition gains than bidders offering non-earnout currencies, consistent with our model of the choice of the optimal method of payment. Overall, we provide robust evidence that earnout is an effective payment mechanism to mitigate valuation risk to acquirers, and also enhances acquirer value during the announcement and post-acquisition periods. Our paper contributes to the broader literature on how corporate acquirers use payment currency to manage information asymmetry and the attendant valuation risk.     

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.     

Systemic risk in the European sovereign and banking system

We investigate the systemic risk of the European sovereign and banking system during 2008–2013. We utilize a conditional measure of systemic risk that reflects market perceptions and can be intuitively interpreted as an entity’s conditional joint probability of default, given the hypothetical default of other entities. The measure of systemic risk is applicable to high dimensions and not only incorporates individual default risk characteristics but also captures the underlying interdependent relations between sovereigns and banks in a multivariate setting. In empirical applications, our results reveal significant time variation in systemic risk spillover effects for the sovereign and banking system. We find that systemic risk is mainly driven by risk premiums coupled with a steady increase in physical default risk.     

Catastrophic Default and Credit Risk for Lending Institutions

Mortgage insurance does insure lenders against most ordinary default: that default induced by price movements in the overall housing market. However, private mortgage insurance typically excludes coverage of the truly catastrophic default resulting from such acts of God as fire, floods, earthquakes, and hurricanes, increasingly familiar in the United States in recent years. Often, these events affect a substantial portion of the houses within a particular neighborhood or region; and if disaster insurance or government aid is inadequate or nonexistent, the default is likely to occur, putting the credit institution at risk. This paper uses a two-state option model with an added jump process that accounts for the possibility and severity of a catastrophe. The paper then uses that information to determine the credit risk to a lender. In addition, this article goes beyond the standard market valuations typical of an option model in reporting the distribution of events that average up to the market cost of the lenders liability. This involves doing probability calculations not present in the usual valuation determinations of option pricing. The point of this article is that the option-pricing methodology provides the means for calculating such probability distributions, thus improving credit risk evaluations for the lender.     

基于隐马尔可夫模型的物流企业客户违约风险评估

物流企业客户违约及其变化过程具有马尔可夫性。在利用经典算法预测违约损失值的基础上,将违约损失值作为影响因素,借鉴隐马尔可夫理论,构建基于影响因素的物流企业客户违约风险评估模型。通过该模型可以计算得到某一时刻物流企业服务的每个客户的违约风险及其风险变化指数和物流企业面临的平均违约风险及其变化指数。算例计算结果表明,该评估模型能够准确度量客户违约风险,并具有可行性和易操作性。     

Predicting Prepayment and Default Risks of Unsecured Consumer Loans in Online Lending

Online lending provides a means of fast financing for borrowers based on their creditworthiness. However, borrowers may undermine this agreement due to early repayment or default, which are two major concerns for the platform and lenders, since both affect the profitability of a loan. While default risk is frequently focused on credit scoring literature, prepayment has received much less attention, despite a higher prepayment rate being observed in online lending when compared with default. This article uses multivariate logistic regression to predict the probability of both the underlying prepayment and default risks. Real consumer lending data of 140,605 unsecured loans provides evidence that these two events have their own distinct patterns. We consider systemic risk by incorporating macroeconomic factors in modeling and address the influence of economic conditions, which are lessons learnt from the last financial crisis. The out-of-sample validation has shown that both prepayment and default can be accurately predicted. This article highlights the necessity of regulations on prepayment given the fast growing online lending market.     

The Hazard Rates of First and Second Defaults

This article examines hazards of repeated mortgage default, conditional on reinstating out of an initial default episode. Results indicate that subsequent default risk for reinstated borrowers is significantly greater than the risk of first default, especially during the first two years after a default episode. In addition, economic factors helpful in predicting first defaults are not helpful in predicting subsequent default episodes. This has important implications for mortgage investors and servicers as industry foreclosure avoidance efforts intensify.     

Exploring the components of credit risk in credit default swaps

In this paper, we test the influence of various fundamental variables on the pricing of credit default swaps. The theoretical determinants that are important for pricing credit default swaps include the risk-free rate, industry sector, credit rating, and liquidity factors. We suggest a linear regression model containing these different variables, especially focusing on liquidity factors. Unlike bond spreads which have been shown to be inversely related to liquidity (i.e., the greater the liquidity, the lower the spread), there is no a priori reason that the credit default swap spread should exhibit the same relationship. This is due to the economic characteristics of a credit default swap compared to a bond. Our empirical result shows that all the fundamental variables investigated have a significant effect on the credit default swap spread. Moreover, our findings suggest that credit default swaps that trade with greater liquidity have a wider credit default swap spread.     

Default prediction with dynamic sectoral and macroeconomic frailties

This paper extends the macroeconomic frailty model to include sectoral frailty factors that capture default correlations among firms in a similar business. We estimate sectoral and macroeconomic frailty factors and their effects on default intensity using the data for Japanese firms from 1992 to 2010. We find strong evidence for the presence of sectoral frailty factors even after accounting for the effects of observable covariates and macroeconomic frailty on default intensity. The model with sectoral frailties performs better than that without. Results show that accounting for the sources of unobserved sectoral default risk covariations improves the accuracy of default probability estimation.     

Incentive‐compatible contracts in merger negotiations: The role of acquirer idiosyncratic stock return volatility

We show that the acquiring firm's idiosyncratic stock return volatility (sigma) is an important determinant of the selection and perceived valuation effects of earnouts in Mergers and Acquisitions (M&As). Earnout‐based M&As are more often announced by high‐sigma acquirers (nearly 40% of all earnout‐based M&As), yet the documented higher risk‐adjusted returns accrued to acquirers in earnout‐based M&As, relative to M&As settled in cash, stock or mixed payments (the earnout effect), appear in deals announced by low‐sigma acquirers (nearly 20% of all earnout‐based M&As). High‐sigma acquirers employing earnouts appear to break even, or even experience losses, relative to their counterparts employing single up‐front payments. These results are confirmed based on a quasi‐experimental design through which the earnout effect is measured in isolation. We argue that in M&As announced by high‐sigma acquirers, the earnout effect is potentially elusive due to the presence of an acquirer‐specific information revelation effect, resulting from the heightened extent of information asymmetry between (small) acquirers’ managers and outside investors. On the contrary, the use of earnouts in M&As announced by low‐sigma (large) acquirers, whereby the acquirer‐specific information revelation effect is likely negligible, sends a strong signal for value creation that also prevents investors from inducing a size‐related discount.     

Using extreme value theory to estimate the likelihood of banking sector failure

Abstract

The growing interest in management of credit risk and estimation of default probabilities has given rise to a range of more or less elaborate credit risk models. While these models work well for non-financial firms they are usually not very successful in capturing the financial strength of banks. As an answer to this, Hall and Miles suggest a simple approach of estimating bank failure probabilities based solely on their stock prices. This paper suggests an extension to the Hall and Miles model using extreme value theory and applies the extended model to the Swedish banking sector around the banking crisis of the early 1990s. The extended model captures very well the increased likelihood of a systemic banking sector failure around the peak of the crisis and it produces default probabilities that are more stable, more realistic and more consistent with Moody’s and Fitch rating implied default rates than probabilities from the original Hall and Miles model.     

A Value-At-Risk Calculation of Required Reserves for Credit Risk in Corporate Lending Portfolios

This paper demonstrates that the building blocks of the insurance process, under similar assumptions, produce identical results to the option-pricing approach in the case of pricing individual loans. We examine the performance of a collective of such building blocks, using portfolio historical performance to endogenously parameterize the default cost function unique to a particular portfolio. Having estimated the appropriate default cost function, we can then specify the reserve requirement for a bank operating such a portfolio. In this respect, the additivity of the Poisson parameter is a powerful feature, allowing one to decompose portfolio performance over time and homogeneous portfolio subsections. Portfolios with greater and lesser risk and profitability respectively are hypothesized, and a capital adequacy framework which equates risk across such portfolios is examined. Finally, we simulate the operation of the proposed capital adequacy model. Observed insolvencies are fewer than those observable under present regulation, and specific problems may be identified earlier than at present.     

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.     

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.     

Counterparty Risk for Credit Default Swaps: Markov Chain Interacting Intensities Model with Stochastic Intensity

We analyze the counterparty risk for credit default swaps using the Markov chain model of portfolio credit risk of multiple obligors with interacting default intensity processes. The default correlation between the protection seller and underlying entity is modeled by an increment in default intensity upon the occurrence of an external shock event. The arrival of the shock event is a Cox process whose stochastic intensity is assumed to follow an affine diffusion process with jumps. We examine how the correlated default risks between the protection seller and the underlying entity may affect the credit default premium in a credit default swap.     

Economic Determinants and Information Environment Effects of Earnouts: New Insights from SFAS 141(R)

Contingent considerations (earnouts) in acquisition agreements provide sellers with future payments conditional on meeting certain conditions. Prior research provides evidence that acquiring firms use earnouts to minimize agency costs associated with acquisitions. Using earnout fair value information, recently mandated by SFAS 141(R), we provide new insights into the economic determinants to include earnout provisions in acquisition agreements, including motivations to resolve moral hazard and adverse selection problems, bridge valuation gaps, and retain target firm managers. We document variations in initial earnout fair value estimates and earnout fair value adjustments that correspond with these underlying motivations. We also provide evidence that target managers stay longer with the firm after the acquisition when earnouts are included primarily to retain target managers. Finally, we demonstrate that earnout fair value adjustments required by SFAS 141(R) provide valuable information to market participants and are negatively associated with the likelihood of contemporaneous and future goodwill impairments.     

公司信用债违约风险预警与防范研究

我国债券规模位居世界第二,债券市场已成为企业直接融资的主要渠道;同时我国公司信用债违约频发,违约主体几乎涵盖了全部行业,永煤AAA债券违约事件引发各方关注。在此背景下,本文研究了信用债违约风险预警与防范,搭建了债券违约预警模型:一是深入分析了违约原因,提出了"经济下行加剧‘债务-通缩’""流动性分层导致再融资困难""民企互保引发违约风险串联"的观点;二是基于KLR信号分析法,以历史违约主体财报数据为基础构建了上市公司债违约预警模型,抽离出相关指标权重构成预警指标体系,并进行了实证检验;三是基于预警模型,提出加强动态监测、构建债券风险分类管理办法等政策建议。