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Pavol Jurca, Ján Klacso, Eugen Tereanu, Marco Forletta, and Mr. Marco Gross
We develop a semi-structural quantitative framework that combines micro and macroeconomic data to assess the effectiveness of combinations of borrower-based macroprudential measures in Slovakia. We expand on the integrated dynamic household balance sheet model of Gross and Población (2017) by introducing an endogenous loan granting feature, in turn to quantify the potential (ex-ante) impact of macroprudential measures on resilience parameters, compared with a counterfactual no-policy scenario, under adverse macroeconomic conditions. We conclude that (1) borrower-based measures can noticeably improve household and bank resilience to macroeconomic downturns, in particular when multiple measures are applied; (2) those measures tend to complement each other, as the impact of individual instruments is transmitted via different channels; and (3) the resilience benefits are more sizeable if the measures effectively limit the accumulation of risks before an economic downturn occurs, suggesting that an early, preemptive implementation of borrower-based measures is indeed warranted.
Pavol Jurca, Ján Klacso, Eugen Tereanu, Marco Forletta, and Mr. Marco Gross

changes in the aggregate unemployment from the macro module to determine the probability of HHMs’ staying employed over the adverse simulation horizon . These probabilities are mainly relevant for the HHMs who are mortgage debtors. Since in the HFCS data mortgages are only reported at the HH level and links to particular HHMs are missing, for HHs with more than two HHMs, we use an algorithm that assigns the mortgage loans to the relatively younger income earning members of the household. The loan is assigned to at most two income earning members, for whom the