Observable covariates are useful for predicting default under the natural measure, but several findings question their value for explaining credit spreads under the pricing measure. We introduce a discrete time no-arbitrage model with observable covariates, which allows for a closed form...

Structural credit risk models have faced difficulties in matching observed market credit spreads while simultaneously matching default rates, recoveries, leverage and risk premia - a shortcoming that has become known as the credit spread puzzle. We ask whether stochastic asset volatility, as an...

Structural credit risk models have faced difficulties in matching observed market credit spreads while simultaneously matching default rates, recoveries, leverage and risk premia - a shortcoming that has become known as the credit spread puzzle. We ask whether stochastic asset volatility, as an...

Structural credit risk models have faced difficulties in matching observed market credit spreads while simultaneously matching default rates, recoveries, leverage and risk premia - a shortcoming that has become known as the credit spread puzzle. We ask whether stochastic asset volatility, as an...

Most extant structural credit risk models underestimate credit spreads while matching default rates, recoveries, leverage, and equity risk premia - a shortcoming known as the credit spread puzzle. We calibrate and estimate a model able to explain medium to long-term credit spreads by...

We build and estimate an equilibrium model of the term structure of interest rates based on a recursive utility specification. We contrast it with an arbitrage-free model, where prices of risk are estimated freely without preference constraints. In both models, nominal bond yields are affine...

The extended Kalman filter, which linearizes the relationship between security prices and state variables, is widely used in fixed income applications. We investigate if the unscented Kalman filter should be used to capture nonlinearities, and compare the performance of the Kalman filter to that...