Systemic risk and macroeconomic forecasting: A globally applicable copula-based approach

Financial markets are interconnected and fragile making them vulnerable to systemic contagion, and measuring this risk is crucial for regulatory responsiveness. This study introduces a new set of measures for systemic risk using a copula-based (CB) estimation method with a focus on U.S. Bank Holding Companies. Unlike most of the prevailing systemic risk measures, CB methodology relies on balance sheet data, instead of market price data, which makes it globally applicable. We compared CB measures with three existing measures of systemic risk that rely on market data and find that CB measures provide competitive results, in both the short and medium term, for systemic risk forecasting. The forecasting evaluation shows that CB measures perform consistently better than historical unconditional quantile of macroeconomic indicators. By using out-of-sample predictive quantile regression, we ascertain that CB systemic risk measures can forecast the 10th and 20th percentile movements of different macroeconomic indicators up to 6 quarters in advance. Moreover, systemic risk measures, existing as well as CB, are better predictors of the 20th percentile shocks to sector-specific indicator and 10th percentile shocks to broader macroeconomic indicators.