Abstract
Changes in inflation, particularly if they are sharp, can have important consequences for nominal contracts, especially debt instruments such as fixed-rate bonds. This paper examines the intricate dynamics of inflation and defaults. The experience of the United States during the past four decades is subjected to empirical analysis to examine how the nature of the relationship changed as we shifted from a high inflation to a low inflation regime. The paper is organized as a three-part study. We initially examine the U.S. default experience, as summarized in Moody's speculative grade default rate, along with industry differences. The paper then scrutinizes U.S. inflation dynamics as seen in different summary measures of the general price level and delves into pricing power issues. The study proceeds to examine co-movements in the inflation and default series from a theoretical and empirical standpoint and the results confirm the intuitive postulate: higher the inflation rate, the more pricing power companies have; greater pricing power leads to, better earnings and repayment abilities for firms and a lower incidence of defaults.
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Notes
In Minsky's terminology, speculative finance is a situation where a firm has just enough cash flows from operations to cover the required interest cost on its debt, whereas Ponzi finance is where a firm has insufficient cash flows to cover the principal and interest due on outstanding debts.
We use Moody's Global Issuer weighted speculative grade default rate, which is stated as a 12-month trailing average. Moody's default definition is as follows: “There is a missed or delayed disbursement of interest and/or principal, including delayed payments made within a grace period; an issuer files for bankruptcy (Chapter 11, or less frequently Chapter 7, in the U.S.) or legal receivership occurs; or a distressed exchange occurs where: (i) the issuer offers bondholders a new security or package of securities that amount to a diminished financial obligation (such as preferred or common stock, or debt with a lower coupon or par amount), or (ii) the exchange had the apparent purpose of helping the borrower avoid default.”
Rolling correlations are computed from monthly data for 5-years at a time, with the correlation estimates “rolled forward” by one month. These movements help track the changing nature of the relationship.
Essentially, a scatter with Nearest Neighbor Fit implies that for each data point in the sample, a locally weighted polynomial regression is fitted; the EVIEWS package uses the subset of observations which lie in a neighborhood of the point to fit the regression model. Observations further from the given data point are given less weight. The Loess technique is an application of the nearest neighbor fit and a simple relationship was fitted to the scatter with a first degree polynomial and a “span” of 0.3 to define the weighting pattern.
Tables showing the empirical basis for lag length selections are available on request.
The empirical basis for lag length selection is available on request.
This set is representative since six sets of impulse responses were run for each sample period (12 sets in all) to assess the sensitivity of the results to the ordering of the variables in the VARs, a potential problem arising from the use of the Cholesky factorization.
References
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Acknowledgements
The authors thank Oleg Meleyentev at Bank of America Securities for help with the data.
Additional information
The views expressed are those of the authors and do not represent those of their institutions.
*Parul Jain has taught economics and finance at Baruch College since 2009. She is also Founder and Chief Investment Strategist of MacroFin Analytics. In addition, she is a Senior Research Fellow at the Skolkovo Institute of Emerging Market Studies. Prior to this, Dr. Jain held positions at Standard & Poor's, Nomura Securities, TIAA-CREF, Prudential, and several faculty positions. She is a panelist for the Blue Chip Economics Forecasts and Blue Chip Financial Forecasts, the Bloomberg Monthly Forecasts, and the Forecasters Club of New York. She has been president of the New York Chapter of NABE, is a current director of NABE, and serves on the Business Economics Editorial Board. Dr. Jain received an MA in economics from Delhi School of Economics and a Ph.D. from Washington University. Leo Kamp teaches economics and finance at Baruch College and Queens College of the City University of New York. Prior to that, he was Managing Director and Chief Investment Economist for TIAA-CREF and has held positions with the New York State Banking Department; Insurance Services Office, Inc.; and Hunter College. Dr. Kamp received his BA from the State University of New York at Binghampton, an MA from Hunter College, and a Ph.D. from the City University of New York.
Appendices
APPENDIX I
APPENDIX II
Sensitivity to Sample Length
Using Longer Sample (1972:01–2009:12)
Source: BLS, BEA, BofA.
Using Shorter Sample (1985:01–2009:12)