In the last few years, according to the evolution of financial markets and the enforcement of international supervisory requirements, an increasing interest has been devoted to risk integration. The original focus on individual risk estimation has been replaced by the growing prominence of top-down and bottom-up risk integration perspectives. Following this latter way, we bring together different approaches developed in the recent literature elaborating a general model to assess banking solvency in both the long-run (economic capital) as well as in the short period (liquidity mismatching). We consider banking capability to face credit, interest rate and liquidity risks associated to macro-economic shocks affecting both assets and liabilities. Following the perspective of commercial banks, we concentrate on information available in the risk management practice to propose an easy to implement statistical framework. We put in place this framework estimating its scenario generation parameters on Italian macro-economic time series from 1990 to 2009. Once applied to a stylized commercial bank, we compare the results of our approach to regulatory capital requirements. We emphasize the need for policy makers as well as risk managers, to take into account the entire balance sheet structure to assess banking solvency. assets value at time t assets value change due to shocks asset liquidity inflows for period q ⩽ h asset loss corresponding to −ΔA loss due to defaults for the period h equity value at time t economic capital, expected shortfall economic capital, value at risk liquidity haircut for obligor i income for the period h liabilities value at time t liabilities value change due to shocks liability liquidity outflows for period q ⩽ h liquidity mismatching net interest for period h probability of default for obligor i performing net interest for period h present value interest rate for node d at time t health index for sector s liquidity shrinkage at time t indicator function for obligor i macro-economic variables at time t shocked macro-economic variables 
Integrated bank risk modeling: A bottom-up statistical framework