Abstract:
For the first time, this study evaluates the contributions to systemic risk in the
context of U.S. institutional prime money market funds (MMFs) from different
sources using partial least squares structural equation modeling (PLS-SEM).
The primary motivation behind this study is to trace systemic risk to its underlying
sources and measure which types of relationships provide significant
explanation using PLS-SEM. I illustrate the application of PLS-SEM and interpretation
of results in a step-by-step manner to empower those new to
PLS-SEM, and undertake robustness testing. Findings indicate that through
crisis years, macroprudential indicators contribute to potential systemic risk
more than prudential indicators. This suggests that macroprudential indicators
that can be traced to individual MMFs market positions are more important
in understanding systemic risk during crises, and further underlines the
interconnectedness of markets. PLS-SEM can be used to test the explanatory
power of new indicators as they emerge in an exploratory environment.
