Synthetic Collateralized Debt Obligations (CDOs) were among the driving forces of the rapid growth of the market for credit derivatives in recent years. Possibly the most popular model beside the Gaussian copula for pricing CDO tranches is the Random-Factor-Loading-Model of Andersen and Sidenius...

We extend the well established link between structural change and estimated persistence from GARCH to stochastic volatility (SV) models. Whenever structural changes in some model parameters increase the empirical autocorrelations of the squares of the underlying time series, the persistence in...

Synthetic Collateralized Debt Obligations (CDOs) were among the driving forces of the rapid growth of the market for credit derivatives in recent years. Possibly the most popular model beside the Gaussian copula for pricing CDO tranches is the Random-Factor-Loading-Model of Andersen and Sidenius...

Implementing a multifactor signal in the corporate bond market into an actual portfolio is subject to many challenges. In general, corporate bonds have higher transaction costs compared to equities and a substantial amount is not traded at all for longer periods. This makes the implementation of...

Synthetic Collateralized Debt Obligations (CDOs) were among the driving forces of the rapid growth of the market for credit derivatives in recent years. Possibly the most popular model beside the Gaussian copula for pricing CDO tranches is the Random-Factor-Loading-Model of Andersen and Sidenius...

We extend the well established link between structural change and estimated persistence from GARCH to stochastic volatility (SV) models. Whenever structural changes in some model parameters increase the empirical auto correlations of the squares of the underlying time series, the persistence in...

Machine learning techniques have gained enormously in popularity in recent years, but so far only to a very limited extent in fixed income research. In this paper we therefore like to do some pioneering work and apply Boosted Regression Trees to Equity Momentum in the corporate bond market. We...

This study fills the literature gap by employing the longest yet-analyzed period and introduces multiple short selling proxies to explain the relationship between short selling information and bond performance. We examine short selling signals derived from bond and equity markets and find both...

The emerging literature suggests that machine learning (ML) is beneficial in many asset pricing applications because of its ability to detect and exploit nonlinearities and interaction effects that tend to go unnoticed with simpler modelling approaches. In this paper, we discuss the promises and...