Working Paper: NBER ID: w11493
Authors: M. Hashem Pesaran; Til Schuermann; Bjrn Jakob Treutler
Abstract: The potential for portfolio diversification is driven broadly by two characteristics: the degree to which systematic risk factors are correlated with each other and the degree of dependence individual firms have to the different types of risk factors. Using a global vector autoregressive macroeconomic model accounting for about 80% of world output, we propose a model for exploring credit risk diversification across industry sectors and across different countries or regions. We find that full firm-level parameter heterogeneity along with credit rating information matters a great deal for capturing differences in simulated credit loss distributions. These differences become more pronounced in the presence of systematic risk factor shocks: increased parameter heterogeneity reduces shock sensitivity. Allowing for regional parameter heterogeneity seems to better approximate the loss distributions generated by the fully heterogenous model than allowing just for industry heterogeneity. The regional model also exhibits less shock sensitivity.
Keywords: Credit Risk; Portfolio Diversification; Global Macroeconomics
JEL Codes: C32; E17; G20
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| full firm-level parameter heterogeneity, along with credit rating information (G32) | simulated credit loss distributions (C46) |
| neglecting parameter heterogeneity (C20) | underestimations of expected losses (G33) |
| neglecting parameter heterogeneity (C20) | overestimations of unexpected losses (G41) |
| adverse 2.33% shock to U.S. equity prices (G19) | increase in loss volatility for fully heterogeneous model (C58) |
| adverse 2.33% shock to U.S. equity prices (G19) | increase in loss volatility for homogeneous pooled model (C58) |
| increased parameter heterogeneity (C21) | reduced shock sensitivity (Y50) |
| systematic risk factor shocks (G41) | amplify differences in loss distributions (D39) |
| regional model allows for regional parameter heterogeneity (R15) | better approximates loss distributions (C46) |