Measuring systemic risk in the global banking sector: A cross-quantilogram network approach

We propose a new systemic risk index based on the interdependence of extreme downside movements of stock returns using the cross-quantilogram and network analysis approach. While quantile dependence allows for sensitivity in times of market downturn, the topological network properties allow for capturing the interconnectedness of the banking system and identification of the specific contribution of each individual bank. Using this design, the proposed systemic risk index is not only easy to calculate and interpret but identifies the banking system's significant transmitters and receivers of extreme downside risk. For the empirical evaluation of the proposed risk index, we use a sample of 83 large banks during the 2003–2020 period, spanning multiple recent crises affecting the banking market. The proposed index is found to be robust in comparison to major alternative systemic risk measures.

Price overreactions in the commodity futures market: An intraday analysis of the Covid-19 pandemic impact.

The objective of this paper is to examine the overreaction behavior of 20 commodity futures based on intraday data from November 20, 2019 to June 3, 2020 with a focus on the impact of the Covid-19 pandemic. A dynamic and non-parametric approach is applied on intraday data for four different frequencies (from 1 min to 1 h) and two different sub-periods (pre-Covid-19 pandemic and during Covid-19 pandemic) in order to detect overreaction behavior which is defined as a large change of prices followed by proportional price reversals. Our empirical findings show that the overreaction hypothesis is confirmed for the considered commodity futures. Furthermore, both the number and the amplitude of overreactions is higher during the Covid-19 pandemic. Our findings also indicate that soft and metal commodities show much less overreactions than precious metals and especially energy commodities. In particular, crude oil futures exhibit a different overreaction behavior compared to other commodities since it has a higher number of negative than positive overreactions during the Covid-19 pandemic. We also find that the data frequency is independent of the overreacting behavior in both periods as the results continuously improve when having more observations due to higher frequencies. Finally, we find that extreme overreactions during the Covid-19 pandemic provide a great potential for profitable trading returns, which can be exploited by traders.