We estimate a reduced-form model of credit risk that incorporates stochastic volatility in default intensity via stochastic time-change. Our Bayesian MCMC estimation method overcomes nonlinearity in the measurement equation and state-dependent volatility in the state equation. We implement on...

We find that firm-level variance risk premium, estimated as the difference between option-implied and expected variances, has a prominent explanatory power for credit spreads in the presence of market- and firm-level risk control variables identified in the existing literature. Such a...

Traditional liquidity measures can provide a false impression of the liquidity and stability of financial market trading. Using data on auctions (bids wanted in competition; BWICs) from the collateralized loan obligation (CLO) market, we show that a standard measure of liquidity, the effective...

The effective functioning of the municipal bond market is crucial for the provision of public services, as it is the largest capital market for state and municipal issuers. Prior research has documented tax, credit, liquidity, and segmentation effects in municipal bonds. Recent regulatory...

Relying on a perspective borrowed from monetary policy announcements and introducing an econometric twist in the traditional event study analysis, we document the existence of an "event risk transfer", namely a significant credit risk transmission from the sovereign to the corporate sector after...

This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...