We analyze the dynamic interactions between credit and liquidity risk and their impact on bond prices and risk. We propose a novel way of modeling credit-liquidity interactions through mutually exciting processes and develop a corresponding Bayesian estimation procedure. Using US corporate bond...

This paper studies the behavior of corporate bond spreads during different market regimes between 2004 and 2016. Applying a Markov-switching vector autoregressive (MS-VAR) model, we document that the dynamic impact of spread determinants varies substantially with market conditions. In periods of...

Cyclicality in the losses of bank loans is important for bank risk management. Because loans have a different risk profile than bonds, evidence of cyclicality in bond losses need not apply to loans. Based on unique data we show that the default rate and loss given default of bank loans share a...

This paper proposes a novel system-wide multi-state framework to model state occupations and the transitions among current, delinquency, default, prepayment, repurchase, short sale and foreclosure on mortgage loans. The approach allows for the modelling of the progression of borrowers from one...

This note discusses some aspects of the paper by Hu and Tsay (2014), "Principal Volatility Component Analysis". The key issues are considered, and are also related to existing conditional covariance and correlation models. Some caveats are given about multivariate models of time-varying...

The problems related to the application of multivariate GARCH models to a market with a large number of stocks are solved by restricting the form of the conditional covariance matrix. It contains one component describing the market and a second simple component to account for the remaining...

We propose a new predictive model for large-dimensional realized covariance matrices. Using high-frequency data, we construct daily realized covariance matrices for the constituents of the S\&P 500 Index and a set of observable factors. Using a standard decomposition of the joint covariance...

We propose uniformly valid inference on volatility with noisy high-frequency data. We assume the observed transaction price follows a continuous-time Itô-semimartingale, contaminated by a discrete-time moving-average noise process associated with the arrival of trades. We estimate the quadratic...

We investigate covariance matrix estimation in vast-dimensional spaces of 1,500 up to 2,000 stocks using fundamental factor models (FFMs). FFMs are the typical benchmark in the asset management industry and depart from the usual statistical factor models and the factor models with observed...