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...

We introduce a top-down no-arbitrage model for pricing structured products. The losses are described by Cox processes whose intensities depend on economic variables. The model provides economic insight into the impact of structured products on the risk exposure of financial institutions and...

Building on the theoretical asset pricing literature, we examine the role of market risk and the size, book-to-market (BTM), and volatility anomalies in the cross-section of unlevered equity returns. Compared with levered (stock) returns, the unlevered market beta plays a more important role in...

The existing literature finds that information not captured by traditional term structure factors helps predict excess bond returns. When estimating no-arbitrage affine term structure models, aligning in-sample and out-of-sample objective functions results in term structure factors that capture...

We propose a no-arbitrage term structure model with a Taylor rule and two macroeconomic variables, real activity growth and inflation, that each contain long-run and short-run components. Variance decompositions indicate that the impact of macroeconomic variables on the term structure differs...

We specify and estimate no-arbitrage models that value sovereign CDS contracts by assuming that the country's default intensity depends on observable economic and financial indicators. We estimate these models using a sample of twenty-eight countries, three CDS maturities, and over a decade of...

We propose no-arbitrage term structure models in which the volatility factors followGARCH processes. The models’ tractability is similar to that of canonical affine termstructure models, but they capture the conditional variances of yields much more accurately.We estimate a model with one...