Editor’s Letter

Global bond managers are particularly concerned with the dynamics of how yield curves move in major countries and their effect on investment strategies and performance. We begin this issue of The Journal of Fixed Income with an article by Diana Tunaru, Francesco A. Fabozzi, and Frank J. Fabozzi that provides a thorough investigation using seven multi-factor term structure models to forecast LIBOR rates. They carefully identify and explain the varying dynamics among the UK, Eurozone, and Japanese money markets.

In the next article, Andrew Kalotay and Guy Davidson provide a clear analysis of why institutional municipal bond portfolio managers prefer premium over par and discount bonds as a result of tax liabilities. They also provide analytics for the value of future tax expenses and predicting municipal bond price changes.

We need to understand the dynamics of Covid-19 on the changing risk assessments of corporate bonds. In the next article, Hans Byström finds that credit risk among the companies in the Dow Jones Industrials Average increases with the spread of Covid-19. However, the levels are short of what was reached in the 2008-2009 financial crisis.

Given their potential conflicts of interest and appearance of oligopolist power, credit rating agencies (CRAs) have been cited as a major cause of the early 2000s high-profile bankruptcies and the 2008-2009 recession. That led to more regulation from the 2006 Credit Agency Reform Act and the 2010 Dodd-Frank Act intended to increase industry competition and rating quality evaluation. In the next article, Miles Livingston, Gina Nicolosi, and Lei Zhou find a general increase in competition since 2006 and a substantial variation in rating accuracy across asset class, but no significant rating quality differences among CRAs. Importantly, investor-paid and issuer-paid CRAs cannot be distinguished in rating quality and increasing industry competition is observed by the negative correlation between CRA market shares and rating accuracy.

There are a multitude of fixed-income security valuation models that could benefit from new technology. For example, Gerardo Manzo and Xiao Qiao incorporate deep learning to the pricing and calibration of credit risk models. They demonstrate that combining deep neural networks with an unscented Kalman filter to calibrate credit risk on historical data can learn with high degrees of accuracy. Specifically, their methodology achieves an in-sample R-squared of 98.5% for the reduced form model and 95% for the structural model.

Finally, Fan Chen and Duane Stock examine the liquidity effect on outstanding bonds when there is a new issuance of bonds from the same firm. They find an improvement in the liquidity of pre-existing bonds, and more significantly, when the new issue has a longer maturity than preexisting bonds.

We hope you enjoy this issue of The Journal of Fixed Income. Your continued support of the journal is greatly appreciated.

Stanley J. Kon

Editor

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