We investigate rating and default risk dynamics over the covid-19 crisis from a credit risk modeling perspective. We find that growth dynamics remain a stable and sufficient predictor of credit risk incidence over the pandemic period, despite its large, short-lived swings due to government intervention and lockdown. Unobserved component models as used in the recent credit risk literature appear mainly helpful for explaining the high-default wave in the early 2000s, but less so for default prediction above and beyond growth dynamics during the 2008 financial crisis or the early 2020 covid default peak. Government support variables do not reduce the impact of either growth proxies or unobserved components. Correlations between government support and credit risk are different, however, during the financial and the covid crisis. Using the empirical models in this paper as credit risk management tools, we show that growth factors also suffice to predict credit risk quantiles out-of-sample during covid times.

We investigate the information content of stock correlation based network measures for systemic risk rankings, such as SIFIRank (based on Google’s PageRank). Using European banking data, we show that SIFIRank is empirically equivalent to a ranking based on average pairwise stock correlations as developed in this paper. The correlation based network measures complement currently available alternative systemic risk ranking methods based on book or market values. A further analytical investigation shows that the value-added appears to be mainly attributable to pairwise cross-sectional heterogeneity rather than to more subtle network relations and feedback loops.

This paper provides an empirical description of the relationship between the trading system operated by a stock exchange and the trading behaviour of heterogeneous investors who use the exchange. The recent introduction of SETS in the London Stock Exchange provides an excellent opportunity to study the impact of an electronic trading system upon traders who use the exchange. Using the cost-of-carry model of futures prices we estimate (non-linearly) the transaction costs and trade speeds faced by arbitragers who take advantage of mispricing of FTSE100 futures contracts relative to the spot prices of the stocks that make up the FTSE100 stock index. We divide the sample period into pre-SETS and post-SETS sample periods and conduct a comparative study of arbitrager behaviour under different trading systems. The results indicate that there has been a significant reduction in the level of transaction costs faced by arbitragers and in the degree of transaction cost heterogeneity. Finally, generalised impulse response functions show that both spot and futures prices adjust more quickly in the post-SETS period. These results suggest that both spot and futures markets have become more efficient under SETS.