Modeling Corporate Default Rates

  •  Xiaoming Tong    


In this paper, I propose a model for predicting annual one-year high yield default risk. My work is based on the earlier work of Hampden-Turner (2009). My model forecasts monthly default rates using four predictors, each with various lags: Libor 3-month/10-year Slope, U.S. Lending Survey, U.S. Funding Gap, and Gross Domestic Product Quarter-over-Quarter Growth. Forecasts of future corporate default rates are useful for evaluating the attractiveness of credit market investments and for estimating value-at-risk on credit portfolios. I present results of out-of-sample predictions of annual default rates. I also address some imperfections of the Hampden-Turner formulation through utilization of more rigorous selection of variable lags and a logistic transformation of predicted default rates. I demonstrate that estimates of future default probabilities are useful for predicting changes in high yield credit spreads.

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