The resiliency of the high-yield bond market: the LTV default

Article Abstract:

This paper examines the resiliency of the new-issue high-yield bond market by examining the changes in implied default rates of such bonds before and after the largest high-yield bond default, i.e., the LTV bankruptcy. Specifically, the paper compares implied default probabilities of high-yield bonds during the post-LTV period calculated from actual new-issue yields with instrumental default probabilities calculated on the assumption that the default had not occurred. A comparison of these probabilities reveals that the market's perception of default on the high risk segment of the bond market increased significantly after the LTV bankruptcy. However, the effect was transitory, lasting only six months. Thus, the market was resilient to a major default. (Reprinted by permission of the publisher.)

Finance, LTV Corp.

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Measuring corporate bond mortality and performance

Article Abstract:

This study develops an alternative way to measure default risk and suggests an appropriate method to assess the performance of fixed-income investors over the entire spectrum of credit-quality classes. The approach seeks to measure the expected mortality of bonds and the consequent loss rates in a manner similar to the way actuaries assess mortality of human beings. The results show that all bond ratings outperform riskless Treasuries over a ten-year horizon and that, despite relatively high mortality rates, B-rated and CCC-rated securities outperform all other rating categories for the first four years after issuance, with BB-rated securities outperforming all others thereafter. (Reprinted by permission of the publisher.)

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Introducing recursive partitioning for financial classification: the case of financial distress

Article Abstract:

A new classification procedure referred to as the Recursive Partitioning Algorithm (RPA) for financial analysis is presented and compared with discriminant analysis within the context of firm financial distress. The algorithm is a computerized non-parametric method based on pattern recognition with attributes of both the classical univariate classification and multivariate procedures. It is shown that RPA out-performs discriminant analysis in the majority of original sample and holdout comparisons. Additional information can be derived from RPA by assessing both the algorithm and discriminant analysis results.

Methods, Innovations, Algorithms, Multivariate analysis, Simulation methods, Simulation, Statistics, Statistics (Data)

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