The purpose of this article is to explain the spread between spot rates on corporate and government bonds. We find that the spread can be explained in terms of three elements: (1) compensation for expected default of corporate bonds (2) compensation for state taxes since holders of corporate bonds pay state taxes while holders of government bonds do not, and (3) compensation for the additional systematic risk in corporate bond returns relative to government bond returns. The systematic nature of corporate bond return is shown by relating that part of the spread which is not due to expected default or taxes to a set of variables which have been shown to effect risk premiums in stock markets Empirical estimates of the size of each of these three components are provided in the paper. We stress the tax effects because it has been ignored in all previous studies of corporate bonds. 1

This paper has examined two specific aspects of stage 1 of the (BISs) Bank for International Settlements proposed reforms to the 8% risk-based capital ratio. We argue that relying on traditional agency ratings could produce cyclically lagging rather leading capital requirements, resulting in an enhanced rather than reduced degree of instability in the banking and financial system. Despite this possible shortcoming, we believe that sensible risk based weighting of capital requirements is a step in the right direction. The current risk based bucketing proposal, which is tied to external agency ratings, or possibly to internal bank ratings, however, lacks a sufficient degree of granularity. In particular, lumping A and BBB (investment grade corporate borrowers) together with BB and B (below investment grade borrowers) severely misprices risk within that bucket and calls, at a minimum, for that bucket to be split into two. We examine the default loss experience on corporate bonds for the period 1981-1999 and propose a revised weighting system which more closely resembles the actual loss experience on credit assets. 3 1.

We survey both academic and proprietary models to examine how macroeconomic and systematic risk effects are incorporated into measures of credit risk exposure. Many models consider the correlation between the probability of default (PD) and cyclical factors. Few models adjust loss rates (loss given default) to reflect cyclical effects. We find that the possibility of systematic correlation between PD and LGD is also neglected in currently available models. 2

What explains the wide swings in the default rate on high yield bonds in recent years? Differences in credit quality from year to year account for much of the observed variation in default rates, but economic conditions and the “age ” of bonds have also played a role. The market for high yield or speculative-grade bonds 1 has grown from $30 billion of outstanding bonds in 1980 to nearly $250 billion today. Over this period, the market has evolved from a collection of “fallen angels”—bonds that have lost their investment-grade rating—into an established capital market for raising funds. Although the high yield market is now mature, its behavior during business cycle downturns is not well understood. During the severe recessions of 1980-82, when the market was in its infancy, few issuers of speculative bonds defaulted on their obligations to creditors. By contrast, in the mild recession of 1990-91, the default

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This paper explores the role of liquidity risk in the pricing of corporate bonds. We show that corporate bond returns have significant exposures to fluctuations in treasury bond liquidity and equity market liquidity. Further, this liquidity risk is a priced factor for the expected returns on corporate bonds, and the associated liquidity risk premia help to explain the credit spread puzzle. In terms of expected returns, the total estimated liquidity risk premium is around 0.6 % per annum for US long-maturity investment grade bonds. For speculative grade bonds, which have higher exposures to the liquidity factors, the liquidity risk premium is around 1.5 % per annum. We find very similar evidence for the liquidity risk exposure of corporate bonds for a sample of European corporate bond prices. ∗ We are grateful to Inquire Europe for financial support. We thank Viral Acharya, Michael

This article reports on an in-depth investigation of the expected ratings changes (drift) over time. Our analysis compares rating changes from the two major agencies, Moody's and S&P, over the period 1970-1996. For the first time, results from several studies which have documented and analyzed these data patterns are contrasted. Depending upon which study one uses, the results and implications can be very different. We expect that the findings will have implications for such diverse practitioners as bond investors who concentrate on any or all segments of the corporate bond market, eg., high yield bond and "crossover" investors, mark-to-market analysts, and traders in the new and growing market for credit-risk-derivatives and for the many analysts who properly view that credit quality assessment involves the entire spectrum of possible outcomes, not just default. A follow-up study will analyze, in greater depth, two critical characteristics of the rating drift phenomenon. These are unexpected, as well as expected, rating migration patterns and also the implied impact on the price of the fixed income instrument. 3 The Importance and Subtlety of Credit Rating Migration 1.0 Introduction and Purpose One of the most important indicators of a corporation's credit quality is the bond rating assigned to its outstanding, publicly traded indebtedness by independent rating agencies. After issuance and the assignment of the initial bond rating, these agencies perform reviews of the underlying issues, although it is not clear if these are periodic or based on market events -- probably both. If deemed warranted, these reviews result in a change, or drift, in the rating signifying improved (upgrade) or deteriorated (downgrade) in the issuers credit worthiness. Using both Moody's an...

The risk neutral credit migration process captures quantitative information which is relevant to the pricing theory and risk management of credit derivatives. In this article, we derive implied migration rates by means of a recently introduced credit barrier model which is calibrated on the basis of aggregate information such as credit migration rates and credit spread curves. The model is characterized by an underlying stochastic process that represents credit quality and default events are associated to barrier crossings. The stochastic process has state dependent volatility and jumps which are estimated by using empirical migration and default rates. A risk-neutralizing drift and forward liquidity spreads are estimated to consistently match the average spread curves corresponding to all the various ratings. The implied migration rates obtained with our credit barrier model are then compared with those obtained via the Jarrow-Lando-Turnbull model by the Kijima-Komoribayashi model in a detailed example.

ABSTRACT. The model introduced in this article is designed to provide a consistent representation for both the real-world and pricing measures for the credit process. We find that good agreement with historical and market data can be achieved across all credit ratings simultaneously. The model is characterized by an underlying stochastic process that represents credit quality and default events are associated to barrier crossings. The stochastic process has state dependent volatility and jumps which are estimated by using empirical migration and default rates. A risk-neutralizing drift and implied recovery rates are estimated to consistently match the average spread curves corresponding to all the various ratings. 1.

We use information contained in yield spreads to recover investors ’ ex ante required rates of return on corporate securities, and then use these ex ante returns to study the pricing of risky assets. Differently from the standard approach, our asset pricing tests do not rely on the use of ex post average equity returns as proxies for expected equity returns. We find that: (i) the market beta plays a significant role in the cross-section of expected equity returns, and its role persists even after size and book-to-market factors are accounted for; (ii) the risk premia associated with size and book-to-market are positive, significant, and countercyclical; and (iii) there is little evidence on positive momentum profits. We also find that systematic risk, as captured by common equity factors, is the main driver of the cross-sectional variation in bond yield spreads. JEL Classification: G12, E44