In the months since the onset of the COVID-19 pandemic, companies issued a large amount of corporate bonds. As a result of this surge, corporate debt has grown substantially — a burden that institutional credit investors may wish to monitor closely.

Model testing is a key requirement for collateral behavior forecasting models in securitized products. The traditional method focuses on a single dimension, and does not provide a clear measure of model accuracy and effectiveness. Instead, a rank-based error-tracking methodology provides an efficient and comprehensive approach to measure model performance.

With COVID-19 spreading across the globe, we are observing severe spikes in liquidity parameters. In order to keep you informed, we have created the MSCI Liquidity Risk Monitor Special Report, a weekly interim report, demonstrating the behavior of several liquidity risk indicators during the global COVID-19 crisis.

Many real estate markets were showing signs of a slowdown even before COVID-19’s negative impact on property portfolios. Has this correction been similar to previous ones? We looked at dispersion of returns, within and across real estate sectors, for the answer.

After the U.S. COVID-19 lockdown, new monthly remittance reports for asset-backed securities indicated performance deterioration and signaled potential challenges ahead. Meanwhile, in China, ABS showed signs of recovery.

Machine learning using neural networks has been successfully applied to fields in which extremely complex patterns can prove challenging for other algorithms. Are neural networks suited for modeling prepayment risk in agency mortgage-backed securities?