A framework for tracking changes in the intensity of investment funds' systemic risk

Abstract

This study applies to investment funds a novel framework which combines marginal probabilities of distress estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology and the generalized dynamic factor model (GDFM). The framework models investment funds' distress dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures investment funds' systemic credit risk in three forms: (1) credit risk common to all funds within each of the seven categories the Eurosystem reports to the ECB; (2) credit risk in each category of investment fund conditional on distress on another category of investment fund and; (3) the buildup of investment funds' vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the investment funds' distress measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. The ranking of drivers of those common components in terms of importance differs from the ranking of the drivers of the common components of marginal measures of distress. This framework contributes to the formulation of macroprudential policy. • First study about the systemic credit risk in all types of investment funds (IF) • Contains a number of methodological features not applied to research on IF so far • Allows the estimation of measures of systemic credit risk for IF • Provides a structural early-warning measure of systemic vulnerabilities' build-up • Contributes to making macroprudential policy operational