Stress-Testing Net Trading Income: The Case of European Banks

Abstract

Net trading income is an important but volatile source of income for many euro area banks, highly sensitive to changes in financial market conditions. Using a representative sample of European banks, we study the distribution of net trading income (normalized by total assets) conditional to changes in key macro-financial risk factors. To map the linkages of net trading income with financial risk factors and capture non-linear effects, we implement a dynamic fixed effects quantile model using the method of moments approach. We use the model to empirically estimate and forecast the conditional net trading income distribution from which we quantify tail risk measures and expected losses across banks. We find a heterogeneous and asymmetric impact of the risk factors on the distribution of net trading income. Credit and interest rate spreads affect lower quantiles of the net trading income distribution while stock returns are an important determinant of the upper quantiles. We also find that the onset of the Covid-19 pandemic resulted in a significant increase in the 5th and 10th percentile expected capital shortfall. Moreover, adverse scenario forecasts show a wide dispersion of losses and a long-left tail is evident especially in the most severe scenarios. Our findings highlight strong inter-linkages between financial risk factors and trading income and suggest that this tractable methodology is ideal for use as an additional tool in stress test exercises.