In this paper, we empirically compare two structural models (basic Merton and Vasicek-Kealhofer (VK)) and one reduced-form model (Hull-White (HW)) of credit risk. We propose here that two useful purposes for credit models are default discrimination and relative value analysis. We test the ability of the Merton and VK models to discriminate defaulters from non-defaulters based on default probabilities generated from information in the equity market. We test the ability of the HW model to discriminate defaulters from non-defaulters based on default probabilities generated from information in the bond market. We find the VK and the HW models exhibit comparable accuracy ratios as well as substantially outperform the simple Merton model. We also test the ability of each model to predict spreads in the credit default swap (CDS) market as an indication of each model's strength as a relative value analysis tool. We find the VK model tends to do the best across the full sample and relative sub-samples except for cases where an issuer has many bonds in the market. In this case, the HW model tends to do the best. The empirical evidence will assist market participants in determining which model is most useful based on their purpose in hand. On the structural side, a basic Merton model is not good enough; appropriate modifications to the framework make a difference. On the reduced-form side, the quality and quantity of data make a difference; many traded issuers will not be well modeled in this way unless they issue more traded debt. In addition, bond spreads at shorter tenors (less than two years) tend to be less correlated with CDS spreads. This makes accurate calibration of the term-structure of credit risk difficult from bond data.

Accurate and rapid assessment of the as-built status on any construction site provides the opportunity to understand the current performance of a project easily and quickly. Rapid project assessment further identifies discrepancies between the as-built and as-planned progress, and facilitates decision making on the necessary remedial actions. Currently, manual visual observations and surveying are the most dominant data capturing techniques but they are time-consuming, error-prone, and infrequent, making quick and reliable decision-making difficult. Therefore, research on new approaches that allow automatic recognition of as-built performance and visualization of construction progress is essential. This paper presents and compares two methods for obtaining point cloud models for detection and visualization of as-built status for construction projects: (1) A new method of automated image-based reconstruction and modeling of the as-built project status using unordered daily construction photo collections through analysis of Structure from Motion (SfM); (2) 3D laser scanning and analysis of the as-built dense point cloud models. These approaches provide robust means for recognition of progress, productivity, and quality on a construction site. In this paper, an overview of the newly developed automated image-based reconstruction approach and exclusive features which distinct it from other image-based or conventional photogrammetric techniques is presented. Subsequently the terrestrial laser scanning approach carried out for reconstruction and comparison of as-built scenes is presented. Finally the accuracy and usability of both of these techniques for metric reconstruction, automated production of point cloud models, 3D CAD shape modeling, and as-built visualizations is evaluated and compared on eight different case studies. It is shown that for precise defect detection or alignment tasks, image-based point cloud models may not be as accurate and dense as laser scanners' point cloud models. Nonetheless image-based point cloud models provide an opportunity to extract as-built semantic information (i.e., progress, productivity, quality and safety) through the content of the images, are easy to use, and do not need add burden on the project management teams by requiring expertise for data collection or analysis. Finally image-based reconstruction automatically provides photo alignment with point cloud models and enables image-based renderings which can remarkably impact automated performance monitoring and as-built visualizations.

This chapter reviews the fundamentals of credit and debt valuation, including what credit risk premium should be charged to an obligor with a specific probability of default, the effects of credit spreads and interest rates on bond and debt valuation, simulating a credit risk spread based on industry comparables, generating an internal credit risk tiered structure, valuing the profit-cost analysis of new debt or line of credit, quantifying the market value of risky debt and its volatility, pricing risky debt assuming mean-reverting interest rates, amortizing debt and simulating prepayment risks, quantifying debt sensitivity using durations and convexity, and valuing risky debt using a markets-based options approach assuming stochastic market variables. These models are illustrated using the Modeling Toolkit software. This model is used to determine the credit risk premium that should be charged above the standard interest rate given the default probability of this debt or credit's anticipated cash flows.

A more rapid and widespread use and implementation of technology in construction often fails since its benefits and limitations remain somewhat unclear. Project control is one of the most variable and time consuming task of construction project managers and superintendents and yet continues to be mostly a manual task. Controlling tasks such as tracking and updating project schedules can be assisted through remotely operating technology such as high-resolution cameras that can provide construction management and other users with imaging feeds of job site activities. Although construction cameras have been around for many years, the costs, benefits, and barriers of their use have not been investigated nor quantified in detail. Subsequently, definitions and understanding vary widely, making it difficult for decision makers at the organizational level to decide on the investment in camera technology. This paper reviews the status of high-resolution cameras and their present use in construction. Results of a multiphased survey to industry professionals were collected in order to identify benefits and barriers and develop a cost-benefit model that can be used for implementation technology in construction.

Monitoring construction projects is a constant task for project managers and executives. The shear size of many projects makes complete coverage difficult and time consuming. Emerging technologies are entering the industry to aid workers in maintaining project controls, but additional research is needed to clearly identify benefits and barriers associated with their use, as historically, data has not been collected in this area. Hi-Resolution construction cameras can allow project managers to see a broader perspective of construction activities and operations and allow these people to do so anywhere internet connectivity is available. This paper focuses on creating a standard approach to quantify cost-benefits of new technologies, specifically construction cameras. Results of a multi-phased survey to industry professionals were collected in order to identify benefits and barriers and develop a cost-benefit model for implementation of new technology in construction.

Models of credit valuation generally predict a hump-shaped spread term structure for low quality issuers. This is understood to be driven by the shape of the underlying conditional default probabilities curve. We show that (a) recovery assumptions and (b) deviation of bond's price from its par value can also drive different term structure shapes. Our analysis resolves conflicting empirical evidence on the shape of speculative grade spread curves and explains the related existing theoretical results. On examining a large set of speculative grade bonds and credit default swaps, we find evidence that par-spread term structures are likely to be downward sloping as credit quality deteriorates sufficiently.

Some modified structural and reduced-form models of credit risk implicitly assume that the market has less information than managers who declare default on their outstanding debt. As a result the announcement of default or disclosure of information that indicates a firm is in distress comes as a “surprise” to the market. In this paper, we study the extent to which private information is revealed about a firm when it announces information indicating distress. The presence of this private information can be inferred from the extent to which investors can earn abnormal returns on bonds or equities issued by firms announcing distress or default. We analyze how much of the information revealed through the declaration of a credit event is publicly available before a specific announcement of credit difficulties. Using default probabilities supplied by Moody’s KMV (MKMV), known as the Expected Default Frequency—or the EDF—credit measure, we model market expectations regarding the firm’s likelihood of default. We then measure the impact of information revealed through an adverse credit event conditional on this expectation. We find that conditioning on EDF credit measures, only 11% of the distressed firms’ equities and 18% of the distressed bonds (belonging to 25% of the distressed firms) display a significantly negative “surprise” reaction in the sense that the price of these securities drops substantially following the announcement. The vast majority of prices for bonds and equities issued by these distressed firms reflect the firm’s credit deterioration well before announcement of default or distress. Most of these significant negative price reactions tend to occur when a firm declares bankruptcy. We also find that conditioning on lagged equity market returns, the extent of the “surprise” reflected in the corporate bond market shrinks, indicating that equity prices tend to be a leading indicator of a firm’s impending distress. This result, however, can also be due to differences in the samples of bonds and equities, or the lack of liquidity in the market for distressed corporate bonds. Our findings are robust to the choice of time horizon of analysis, and to the choice of publicly available information other than EDF credit measures. Our results have implications for determining the appropriate framework for modeling credit risk. ∗Address correspondence to Dr. Jeffrey R. Bohn, Managing Director, Research Group, Moody’s KMV, 1620 Montgomery Street, San Francisco, CA 94111. E-mail: Jeff.Bohn@mkmv.com

In this paper, we validate the performance of the MKMV EDF credit measure in its timeliness of default prediction, ability to discriminate good firms from bad firms, and accuracy of levels, with a focus on U.S. markets. We focus on the period 1996-2004 for most of our tests. Wherever possible, we compare the performance to that of other popular alternatives like agency ratings, Z-Scores, and a simpler version of Merton model. We find that the MKMV EDF credit measure has done consistently well across different time horizons, and different sub-samples based on firm size and credit quality. Our tests indicate that the MKMV EDF credit measure is a superior alternative to other popular credit risk measures.

In this paper, we validate the performance of the MKMV EDF credit measure in its timeliness of default prediction, ability to discriminate good firms from bad firms, and accuracy of levels, with a focus on U.S. markets. We focus on the period 1996-2004 for most of our tests. Wherever possible, we compare the performance to that of other popular alternatives like agency ratings, Z-Scores, and a simpler version of Merton model. We find that the MKMV EDF credit measure has done consistently well across different time horizons, and different sub-samples based on firm size and credit quality. Our tests indicate that the MKMV EDF credit measure is a superior alternative to other popular credit risk measures.

In recent years, the Moody's KMV Expected Default Frequency™ (EDF) credit measure has become a standard measure of corporate credit risk among traders and managers of credit risk. Beyond predicting defaults, one other important application of any quantitative credit risk measure is to value credit risky claims such as corporate bonds, loans and credit derivatives. The goal of this paper is to provide evidence on the valuation performance of an EDF-based valuation model on a comprehensive sample of corporate bond data. This paper serves to document some of the valuation related research done at Moody's KMV and builds on previously published work. We apply our valuation method to a large sample of bond spreads and find that, unlike many other versions of structural models, ours performs quite well. Our model consistently explains more than 70% of the cross-sectional variation in bond spreads. AUTHORS

The option-pricing framework first introduced by Fischer Black, Myron Scholes, and Robert Merton in the 1970s facilitates the development of valuation models to use equity market information to price corporate bonds and credit default swaps. This paper reviews a particular implementation of an option-pricing model developed by Moody's KMV to relate equity and debt markets. The out-of-sample testing suggests that this structural model can be used effectively in pricing debt when only equity prices are available. AUTHORS

This article surveys available research on the contingent‐claims approach to risky debt valuation. The author describes both the structural and reduced form versions of contingent claims models and summarizes both the theoretical and empirical research in this area. Relative to the progress made in the theory of risky debt valuation, empirical validation of these models lags far behind. This survey highlights the increasing gap between the theoretical valuation and the empirical understanding of risky debt.

In this second installment, the author addresses some of the problems associated with empirically validating contingent‐claim models for valuing risky debt. The article uses a simple contingent claims risky debt valuation model to fit term structures of credit spreads derived from data for U.S. corporate bonds. An essential component to fitting this model is the use of expected default frequency; the estimate of the firms' expected default probability over a specific time horizon. The author discusses the statistical and econometric procedures used in fitting the term structure of credit spreads and estimating model parameters. These include iteratively reweighted non‐linear least squares are used to dampen the impact of outliers and ensure convergence in each cross‐sectional estimation from 1992 to 1999.